How To Build A Data Strategy: The Framework To Success

Many companies are sitting on mountains of data and information, but few are extracting the gold that lies within it, which we think is crazy. In this blog, we’re going to show you how you can maximise its benefit to allow your business to thrive.

You’ll learn that every successful data lead organisation is built on an effective data strategy. We’ll explain:

  • What a data strategy really is
  • The benefits of having a data strategy
  • Why you really should have a data strategy
  • Jarmany’s 5 steps to building an effective data strategy
Let’s get to it.

What actually is a data strategy?

A data strategy is basically a plan that, if implemented properly, will allow you, as a business, to leverage the power of the all the data and information at your disposal quickly and effectively. This power will then result in the business being able to make the most informed decisions possible and act quickly to help maximise commercial performance.

Sounds simple, but the difference between a great data strategy and poor data strategy could result in a massive impact on your business. Research shows that businesses with a strong data strategy can perform over 2.5x better than those with a poor data strategy.1

What are the benefits of having a data strategy, and why should you have one?

You might say to yourself: “I already have loads of data so surely I just need to take a quick look at it and it will give me the answers I need to run my business” …if only life was that simple!

When we start working with our clients, we often see that they are facing a variety of challenges, including:

  • Incomplete and untrustworthy data which results in more arguments than insights
  • Inadequate data cleansing compounding already questionable data
  • Inefficient data management processes slowing down their speed of decision making
  • Insufficient use of available 3rd party data that will give colour and relevance to your internal 1st party data
  • An over reliance on human beings, rather than technology and AI, to do relatively simple and mundane tasks. (A machine will never get bored of doing these tasks, will often do them better, and will be quicker, with far less mistakes or human-error).

Once you have your data sorted so it’s clean, accurate, timely and in a format where you can readily understand and interpret it, you need to ask yourself what’s next and how can you use this information?

You’ll be surprised by how many instances there are where good data and insights can help turbo charge your business. Below is a small subset of the main areas where a data-driven business can drive a massive commercial advantage:

  • Increased Sales a cohesive data strategy can help you identify opportunities to optimise marketing efforts. Businesses that strategically use data to inform business decisions can outperform their peers in sales growth by 85%.2
  • Increased Profits – this can be achieved by streamlining operational logistics and through cost analysis. According to a Business Application Research Center (BARC) data-driven sales reduced the overall cost of operations by 10%.3
  • Greater client satisfaction – Businesses that personalise the customer experience using data can increase the customer lifetime value by 2.5x on average.4
  • Decreased Risk – this can be achieved through better management of regulatory requirements and data breaches. According to IBM the average cost of a data breach in 2022 was $4.35 million and 83% of organisations reported more than one breach.5

Jarmany’s 5 steps to building an effective data strategy

A data strategy is essentially a plan that allows you to quickly and effectively leverage the power of all the data and information available to you as a business. In turn, this allows you to make the best business decisions to drive growth and operational efficiencies.

We’ve consolidated the core steps you need to take to help you define your data strategy:

1. Define the questions that need to be answered to allow the company to meet its strategic objectives and respond to tactical challenges. This could be based on goals relating to revenue growth, increased profit, market share growth or cost reduction.

2. Define the gaps between what you have today and where you want to get to. In particular, you need to consider the following 4 areas:

    • Data – Do you even have all the raw data you need? Are you set-up to collect the data from your business operations required to make the right decisions? Are you maximising the benefit of 3rd party data sets that are available to you? Do you have the right quality, breadth and depth in your data?
    • Technology – What data technology do you already have in your tech stack? Does it have the functionality to complete the tasks required by your business? Are you restrained in your options by significant previous investments in certain tech stacks (Azure, GCP, AWS). Finally, are you making the most of the recent advances in technology that are happening, in particular AI? (Whilst this last question is key to consider, you must always remember to have the enablers of AI in place, such as good data and a clear strategic need, to really leverage its true power).
    • Internal Capability – Do you have the right people with the right skills to enable you to leverage your investment in data and technology so you can transform that data into valuable information?
    • Culture – All of the above points are redundant if you don’t have an organisational culture that is programmed to accept that data needs to be an intrinsic part of the decision support structure. Ask yourself if you have buy-in from the right stakeholders and how you can embed a greater level of acceptance and interest towards data and data-driven insights from your organisation.

3. Define the plan – Once you have defined the objectives that need to be met and the current gaps you face it is important to create the plan to address them. Below are the key factors every good plan needs to contain:

    • Incremental wins – Better data and insights can start driving benefits to your business almost instantly. Therefore, no data strategy should wait until the transformation is 100% complete before launching it. This could mean months of missed opportunity and eventually result in a flop. At Jarmany we think a staged delivery focus is the best. We usually advise 3-month milestones to deliver specific commercial advantages that build on themselves over time. This means you start getting a return on your investment sooner, and also allows you to flex the strategy slightly over time if the needs of the business change. This approach significantly reduces the chances of the business ending up with a BI white elephant that isn’t fit for purpose.
    • Leverage previous investments as much as possible – Don’t reinvent the wheel or spend time and money in areas where you don’t need to, unless it results in greater commercial benefit. (New and shiny isn’t always best).
    • Spend money wisely – Technology, especially AI, is rapidly advancing so investing in the right tech could provide significant commercial advantages to your organisation. However, as always make sure the fundamentals are in place first. (Sometimes new and shiny is the right way forward).
    • Don’t neglect your people – Bring them on the journey and remind your people of the benefits to them. It’s a support function not a threat, training can create your citizen data analysts.

4. Review progress – It’s important to constantly monitor the progress of the implementation of a data strategy. We always advise to stick to the 3-month cadence mentioned above to so you can work in shorter term sprints so you can ensure everything is on track and it enables you to tweak the strategy when necessary.

5. Repeat the above – The needs of any business changes over time especially if it is going through a period of transformational change. Therefore, whilst we talk about working in 3-month sprints, we believe that any data strategy should go through a deep review every 2-3 years. This gives you time to implement a strategy but not too long that the plan becomes irrelevant and doesn’t align with the changing needs and focus of the business.

What’s next?

So, there you go—a successful data strategy framework in five steps, as promised.

We don’t mind confessing to you that negotiating each step can be tricky if you don’t have enough experience and expertise at your disposal. Therefore, the wisest move can often be to work with experts who create data strategies for a living.

At Jarmany, we have the talents to support you in building and implementing a successful data strategy. We’ll help deliver your strategy as well as collect and structure your data to be analysed and modelled in such a way to answer your business questions and deliver your business objectives as quickly and as cost effectively as you can.

Talk to us today and have an honest conversation about how to get your data strategy moving.

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What Is Power BI? And How Does It Work?

Identifying trends and patterns from raw data is hard and has nothing to do with a person’s intelligence. But spotting those signs in shapes and colours is much easier and can be achieved surprisingly quickly. 

Therefore, the rise of data visualisation tools as part of the broader business intelligence (BI) world is no surprise. These tools not only speed up decision-making processes but improve the decisions themselves, helping viewers interpret data more accurately. 

All this brings us to Microsoft Power BI – the most complete data visualisation technology in the market, according to the Gartner Magic Quadrant for BI and Analytics Platforms 2023 – and something that millions of people are using every day to extract insights from within their data. Let us walk you through it.

What is Power BI?

Microsoft Power BI aggregates your data and then represents it visually for you to analyse and share. Forrester calls it Microsoft’s augmented business intelligence platform, infused with the power of AI (which we’ll get to later on).

In essence, Power BI is a collection of software services, apps and connectors. What that means is you can connect data from multiple sources across your business, including Excel spreadsheets, visualise it in a dashboard or a report, share with colleagues and uncover what’s important to you in no time.

Some common types of data visualisation:

  • Bar and column charts
  • Doughnut charts
  • Decomposition tree
  • Funnel charts
  • Gauge charts
  • KPIs

What makes Power BI different from other BI solutions with data visualisation tools? Ask a senior consultant who works with Power BI and has experience of other solutions and you’ll hear words such as more intuitive, adaptable, unified and interactive.

The truth is that because it’s Microsoft, Power BI has a look-and-feel that many of you will recognise and like. If you use Excel then making the step up to Power BI will feel like a natural development.

How Much Does Power BI Cost?

The solution comprises 3 basic elements:

  • Power BI Desktop – a Windows desktop application.
  • Power BI Service – a software-as-a-service offering.
  • Power BI Mobile – apps for Windows, iOS and Android devices.

In terms of licensing:

  • Power BI Desktop is free.
  • Power BI Pro is £8.20 per user/ month
  • Power BI Premium Per User (PPU) is £16.40 per user/ month
  • Power BI Premium is £4,105.60 per capacity/ month

You can find out more about the differences between each package here.

Why Is Power BI Popular?

It’s unlikely you’ll find any area of your operations that Power BI won’t support; hence you’ll see Power BI providing insights to teams across:

  • Finance
  • HR
  • Production
  • Planning
  • Warehouse
  • Supply chain
  • Logistics
  • Sales
  • Marketing

It’s also true that new Power BI use cases will occur as the solution gets more tightly woven into your operations. Soon enough you’ll be building reports and dashboards delivering niche views on everything from expenses to specific project plans and progress on individual targets.

Power BI reports tend to feature historic data sets, delivering a snapshot of your organisation over a set period rather than just in real-time. Nevertheless, your Power BI reports can aggregate and visual data on key parts of your operation in just the same way as your Power BI dashboards, from Finance to HR and Customer Profitability to Ecommerce sales.

Power BI dashboards organise and visualise your data in real-time. You can create alerts when figures change and hit a chosen threshold. Here are a couple of dashboard examples:

  • Ecommerce –
    You can see how your online sales channels are performing day-to-day to gain a deeper understanding of how your products are performing. Insights could include: sales by category, most returned product and reasons for returns and sales over specific periods.
  • Marketing –
    You can visualise the effectiveness of your campaigns and the performance of segments and channels. For example, marketing spend by products, channel performance and campaign success rates.

Once you’re creating your reports and dashboards, you can start using some of the value-adding features in Power BI to distribute your insights and isolate the data that’s most important to your company.

Power BI Apps
Power BI Apps allows you to bundle your reports, dashboards, spreadsheets and datasets and distribute them to individuals or large groups across your organisation in one go.

Power BI Metrics
With Metrics, you can publish the performance metrics that are most important to your business in a single pane within Power BI. The main idea here is that Metrics promotes accountability, alignment and visibility for your teams.

How To Become A Power BI Expert

Power BI is promoted as a self-service tool; and that people with little or no technical background can become data heroes in just a short while.

Because it’s based on Microsoft Excel, many people will get a head start on learning the basics and the drag-and-drop functionality simplifies the process of connecting multiple data sources.

As you’d expect, Microsoft also offers plenty of Power BI training, with online workshops, documentation, and sample dashboards and reports.

At some point, you should think about learning DAX (Data Analysis Expressions), developed by Microsoft for platforms such as Power BI. It’s been referred to as Excel formulas on steroids and is crucial if you want to get the full value of Power BI, helping you create new information from data that is already in your model.

“If you’re familiar with Office 365, you’re going to be able to pick up Power BI quite quickly.”

Building Power BI Dashboards And Reports

You can create visualisations (referred to as visuals) in reports using visual types directly from the visualisation pain in Power BI. Furthermore, there are a growing number of pre-packaged custom visuals available through third parties that might be enough for what you need.

You simply download the custom visuals into your Power BI system and off you go.

Common sense will tell you to be wary of downloading anything unless it’s from a trusted source, in which case you’re better off using custom visuals that have been certified by Microsoft. There are many available in the Microsoft AppSource community site.

To cut down on the effort to extract useful data insights, Power BI has added its own AI Insights feature, which covers Text Analytics, Vision and Azure Machine Learning. It gives you access to a collection of pre-trained learning models that enhance your data preparation efforts. Using this capability, which requires Power BI Premium, you can enrich your data and gain a clearer view of data patterns.

Avoid Common Mistakes In Power BI

As you’d expect, there are best practices that you should follow to extract the full potential of Power BI for your organisation. Here are some top ones:

  • Spend a bit of time thinking carefully about what your dashboard or report is for.
  • When starting out, avoid introducing too much data because it can slow down the performance of your dashboard.
  • Remember you want your data visual to be used by colleagues, so think of them and don’t over complicate the report, making the information difficult to digest.

Top 5 Power BI Tips

Now you know some of the common mistakes, we’ll leave you with some top tips as shared by our own Power BI experts:

  1. Have a clear a purpose in mind – there are so many data visualisation possibilities so be certain on what you’re trying to say and who you’re trying to say it to.
  2. Keep your visualisations simple – it’s worth reviewing your data visual multiple times as it evolves, asking yourself: Can I make it clearer or can anything be removed?
  3. Do some proper benchmarking comparisons – your data also needs context so include benchmarking to show performance against a set of standards.
  4. Annotate your reports using Tooltips and buttons – both provide additional information on visuals, such as contextual data or, in the case of buttons, making them more interactive.
  5. Do a training course – Power BI may be aimed at non-technical people, but there is so much to it and it’s such a powerful tool that to get the most out of this technology it’s definitely worth getting some formal guidance.

Speed Up Your Power BI Development

With all this information, we hope it’s clearer what Power BI is and how it can help your organisation speed up and improve the effectiveness of your decision-making. We also hope you’ve got a sense of why Power BI is a leader in the data visualisation market and how with continued development, such as the integration of AI, that position isn’t likely to change any time soon.

What’s also true, however, is that without the internal experience and expertise of Power BI to hand, you’re going to need to invest time and money in developing those skillsets; and that partnering with an organisation that can plug those skills straight into your operation may be more time and cost effective.

At Jarmany, we’ve built a first-class team of Power BI consultants that can help your business harness the power of data effectively. Whether you are looking for a fully outsourced team or support for your in-house team, we can provide you with seamless expertise at a competitive cost.

If you’d like to know more about how Jarmany could help you maximise the value of Power BI to drive smarter decision-making across your company, contact us today.

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Looker vs Tableau vs Power BI: Which is Best for You?

As data continues to become crucial to all sorts of businesses, the need to understand, analyse, visualise, and use data grows more imperative.

However, without a data visualisation tool or analytics solution to view this data, businesses can quickly become overwhelmed. Data analytics solutions, business intelligence (BI) programs, and data visualisation tools are now essentials — rather than optional extras.

That’s why 54% of enterprises consider BI and other data-based solutions to be critical to their work now and in the future. By understanding the insights within their data, businesses can make better informed, data-driven decisions. But with a range of tools out there, which one is best?

In this blog, we’ll look at Looker, Power BI and Tableau — the three leading BI and data visualisation tools — to help decide which is best for you.

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At a glance: Looker vs Tableau vs Power BI


Looker is a browser-based data analytics and visualisation tool. Founded in 2012, Looker was acquired by Google in 2019 and is now part of Google’s cloud platform. It also uses its own modelling language, LookML, a modular language that allows data and calculations to be reused. Alongside this, Looker’s Data Dictionary is a searchable directory for all metrics and descriptions in a Looker data warehouse.


Looker’s unique approach to data offers some interesting advantages:

  • Cloud-based & browser-based: Looker offers the useful combination of being part of Google’s Cloud Platform and being completely accessible via a browser. Google Cloud offers an advanced level of security and a flexible way to manage data. With direct access through a browser, these benefits are offered without the need for software installation and manual updates.
  • Easy Git Integration: Looker can integrate with the popular version control system Git, enabling multiple people to work on multiple visualisations simultaneously. With Looker, users can see changes made to data-modelling layers, and jump back to them anytime. They can also create different version strands for developers to work on. This setup is easy and provides a benefit not offered by other data visualisation tools.
  • Connects with multiple data sources: Looker integrates with more than 50 different data sources due to LookML, Looker’s data modelling language. LookML’s flexible modelling language means it can analyse and visualise data from multiple sources, including Google Cloud, Microsoft Azure, Amazon Web Services and on-premises databases.
  • Self-serve capabilities: LookML also offers the ability to define dimensions, metrics, aggregates and relationships. These can then be used seamlessly in data visualisations, providing self-service analytics whilst also enabling the data to be reused. Looker also offers an Explore feature that enables users to self-serve their data through drag-and-drop functions, individual dashboards, and the ability to add additional fields to aid in further data


  • Limited range of visualisations: Despite Looker’s popularity, the variety of visualisations offered with the basic program is somewhat limited. This comparison is even starker when comparing these capabilities to Looker’s competitors, Tableau and Power BI. It should be noted that Looker does offer the ability to build custom visualisations, which can go some way to mitigating this issue.
  • More expensive than direct competitors: In theory, Looker’s pricing model is ideal with cost being tailored to the company in question. However, Looker is the most expensive of its competitors — Tableau and Power BI.
  • Steep learning curve: Looker’s unique modelling language requires users to have at least a basic understanding of coding – in particular programming languages like SQL. The theory behind LookML is sound; a programming language that is easier to pick up. However, it is more difficult if a business lacks the right in-house expertise or training.

Looker’s ability to integrate with other systems, thanks to their unique LookML coding language, means that enterprise businesses can make use of data stored in already present third party software. Features like Looker Blocks — pre-built data models designed to fit common analytics patterns — streamline this integration, offering pre-built code that can more easily be embedded.

Looker is also a powerful beginner platform. Its systems are easy to learn, and the code is easily understood. While its visualistions might not be as sophisticated as its competitors, it also offers visualisation with real-time analysis and the ability to customise.


Tableau formerly held the title of the undisputed king of Premium BI tools and has only recently gained rivals in Looker and Power BI. With quick implementation, ungoverned analytics and data can become accessible and easily shared throughout an organisation.

Tableau has recently been acquired by Salesforce, leading to simple integration with Salesforce users, as well as other programs such as MuleSoft and Slack.


  • Interactive data visualisations: Tableau provides interactive data visualisation benefits, helping to turn unstructured statistical information into logical and intuitive visualisations. Filtering and selection provide options for further analysis and ease of understanding.
  • Adaptable to large amounts of data: Unlike other platforms that have a limit on data model size, Tableau has the ability to handle very large amounts of data without there being any impact on performance.
  • Intuitive user interface: Developer and non-dev users alike can easily use Tableau due to its intuitive user interface (UI). Non-dev users can use all the basic facilities of Tableau, however, specialists might be needed to increase the platform’s functionality. Tableau’s simplicity is also coupled with its ability to reliably operate on big data thanks to its columnar data model.
  • Compatibility: Tableau is compatible with multiple data sources, enabling businesses to connect with, access and blend data from multiple sources into one visualisation for easy data analysis. Tableau is also compatible with multiple scripting languages, such as Python or R, to maximise potential output.
  • Mobile support: Tableau has a mobile app for both iOS and Android systems. This app has the same functionality as the desktop and online software, allowing users to analyse data remotely. Moreover, the Tableau dashboard can be customised to each application, meaning functionality can be maximised to the individual’s separate mobile and desktop needs.


  • Inflexible pricing: Tableau’s pricing doesn’t change on a case-by-case basis, despite the fact that most companies have individual needs. Purchasing an extended licence is required by Tableau’s sales model from the start. Many companies might find that they would rather start with a specific set of features and later adjust the pricing for further features if necessary.
  • Poor after-sales support: Due to Tableau’s seniority, there are many online message forums that users can use to discuss Tableau’s features. However, many focus on a lack of support and maintenance. To resolve this, Tableau’s support team sometimes advise purchasing a new feature, which can become costly.
  • Favoured towards Salesforce: Depending on an enterprise business’s requirements, this might not have a big impact. However, the nature of Salesforce’s acquisition means that Tableau’s development will now be skewed more towards Salesforce integration; Tableau is no longer an independent BI tool.

Tableau is designed with businesses in mind, rather than an IT department or developer. Tableau’s user interface is considered to be the easiest to use of its direct competitors. Its ease of usage means that you do not have to be an expert in programming languages or coding, empowering teams across an organisation to become more data-driven and data-literate in their decision making.

Power BI

Microsoft Power BI integrates well with Microsoft products and systems, however a recent uptick in adoption likely comes from the free version of Power BI that is available to anybody. This free version is reliable for individual analysts, but the premium version allows important functionalities such as sharing reports, dashboards or analytical apps.


Microsoft’s tool offers the following advantages:

  • Large range of visualisations: Power BI has a great number of standard visuals to populate your reports, each with a wide variety of format options. Power BI is backed up by integrations with Microsoft Office and can harness the power of Excel to create easy data visualisations. Moreover, if the desired option is unavailable, users can build their own custom visuals also.
  • User-friendly interface: Power BI is extremely intuitive to navigate and user-friendly. Users with little dashboard experience can navigate the platform as easily as those with expertise. This is partly due to their natural language query tool, which allows people to ask simple questions to easily navigate to the data they wish to visualise.
  • Lower cost: Power BI is relatively low in cost compared to other leading platforms. A trial version of Power BI is available to everyone, while Power BI Pro is included in some Office 365 business and enterprise plans. This has caused a shift in the market, causing other BI vendors to become much more competitive in their licensing options.
  • Easy to learn: Power BI might be the easiest to use of the three platforms. Though you will need expert support to truly get the most of your data, those who are familiar with Excel will be able to start using Power BI’s data visualisation tools quickly.


  • Limited customisations: Though Power BI offers a range of visualisations to choose from, it can be difficult to customise any of them. There are basic formatting options available but this can prove limiting for businesses looking to create bespoke visualisations with limited Power BI experience.
  • Potential learning difficulties: As covered, while Power BI is simple to get to terms with in the beginning, it will require added training further down the line. This especially applies when performing analysis over your datasets, as it will likely require tools that are external to Power BI, like DAX Studio.
  • Data security: Power BI offers advanced encryption capabilities using Azure. However, as it’s a cloud-based tool, some stakeholders may feel uneasy about the security and privacy of their data. Businesses will have to ensure that they have the full breadth of knowledge of Power BI’s encryption services to fulfil their business case.

It’s clear that Power BI offers good integration capabilities, especially with other Microsoft products, allowing data analysis and visualisations to be shared across. It offers the reliability of other products, and even offers integrations into other data analytics tools.

Using a data consultancy to make the most out of your tools

Power BI, Tableau and Looker offer high-quality BI and data visualisation solutions for businesses in 2023. What is ‘best’ for your business is relative — but what’s not relative is that in order to maximise your ROI from these platforms and harness the power of your data, you need to get the best out of these tools.

Without in-house expertise or the right training, the steep learning curve and technical know-how required to maximise its potential can hurt your ROI, and squander the potential within your data. This is where Jarmany can help.

With our consultancy services, we’ll help you find the right platform for your business. Once matched to the correct tool, we’ll help you maximise the insights you get from your data and make business intelligent decisions. Jarmany’s team of data scientists are seasoned experts who understand that no two businesses have the same needs.

Whether you need help selecting a platform, getting the most out of data visualisations, creating a data strategy or something else, Jarmany’s data consultancy experts can help.

Start a conversation today.

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  1.  37 Business Intelligence Statistics to Know in 2022 | 99firms

How Data Visualisation Can Improve Your Decision Making

In 1597 Sir Francis Bacon famously said, “knowledge itself is power.”1 Four centuries later, his words are proving to be more accurate than ever, as knowledge in the form of big data delivers an increasing amount of power to businesses.

Tech giants like Google and Facebook have made it abundantly clear that, to them, big data is a goldmine of insights. Therefore, forward-thinking organisations need to invest in and develop a comprehensive data strategy to improve how they obtain, store, manage, share, and use their data.

However, many businesses struggle to make data work for them. A Mckinsey survey found that 47% of business leaders feel that data & analytics have fundamentally transformed their industries, but they still had difficulties putting data to work for their organisations.2

While new technologies allow organisations to collect lots of data, raw data in and of itself has little value. Instead, the value arises when that data is presented in a way that provides actionable insights, informing business leaders on the best course of action.

That’s why in this blog post we’re going to be looking at how data visualization improves decision making. Let’s dive straight in.

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What is data visualisation?

Data visualisation is the final part of a process that includes the collection, cleansing and analysis of information from numerous data sources. This final stage is all about creating a pictorial representation of that data which can then function as a single source of truth for businesses. 

The goal behind creating these visually stimulating visualisations is to tell a compelling story using raw data whilst keeping crucial KPIs in mind during review processes. With the help of data visualisation, key insights and information, such as trends and patterns, can be digested and understood by stakeholders much quicker.

Types of data visualisation

When it comes to visualising their data to help communicate the story behind it to their stakeholders, there are a number of things businesses need to consider. Chief among these is the category of visualisation they want to focus their efforts on, either:

  • Data exploration: Data exploration helps to uncover insights and identify patterns that need further attention.
  • Data explanation: By presenting an easy-to-understand graph or illustration, data explanation helps an audience better understand the results of that data.

Understanding which of those two ends a given visualisation is intended to achieve is essential in order to achieve success in an overarching data strategy.

While there are just two broad categories of data visualisation, there are a number of specific types of visualisations that organisations can deploy to better understand their data. These include:

  • 2D area visualisations: 2D area data visualisations are typically geospatial, as they relate to the relative position of things on the earth’s surface.
  • Temporal visualisations: Temporal visualisations have a start and finish time and elements that may overlap.
  • Multidimensional charts: Multidimensional charts are those with two or more dimensions that help explore correlations and discover casualty, which is why these are amongst the most commonly used visualisations.
  • Hierarchical charts: Hierarchical data sets are the arrangement of groups in which larger groups encompass smaller sets, allowing users to drill down or drill up to conduct in-depth analysis.
  • Network visualisations: Network data visualisations show how data points are related within a wider network.

How does data visualisation improve decision-making?

Data visualisation helps decision-makers see the big picture. From understanding trends and patterns to highlighting issues and areas of concern, data visualisation is crucial to obtaining enhanced oversight over business operations.

Research has shown that organisations that leverage their customer behaviour data to generate insights and make data-driven decisions can outperform their peers by as much as 85% in sales growth. 3

Consequently, any organisation with an eye on the future needs to make sense of its data through data visualisation techniques and tools to enlighten its decision-making processes. Without effective visualisation, organisations are relying more on guesswork and interpretation when it comes to making crucial decisions.

Benefits of data visualisation

Whilst the primary benefit of data visualisation centres around making better business decisions, it’s worth digging into some of the more specific benefits it can help organisations obtain. These include:

  1. Improving speed: Many bad decisions are just good choices with bad timing, as timing is an often overlooked aspect of decision-making. Data visualisation can help businesses draw insights from vast amounts of data in real-time, increasing response times to challenges.
  2. More accurate numbers: Although data provides decision-makers with potentially all the information they need, it’s usually not presented in an easily digestible format. Data visualisation simplifies the information, boosting our comprehension of the data and reducing the need to fill the gaps with our biases, making our decisions more accurate. However, in order to ensure accuracy, it’s pivotal that the data used within visualisations is of the highest quality.
  3. Simplified communication: Once executives and other decision-makers use data to decide on a specific direction, that decision must be communicated to the team responsible for implementation. While the decision may seem obvious, other stakeholders may not fully understand the reasoning behind it, thereby reducing efficiency. With data visualisation, decision-makers could use graphs and charts to communicate the reasons behind the decision clearly.
  4. Identify benchmarks and trends: An effective visualisation makes it easier than ever before for users to recognise relationships and patterns within their data. By exploring these patterns, users are able to focus on specific areas that need attention to help drive their business forward.
  5. Empowering collaboration: Data visualisation helps organisations by presenting data in a universally understood form, empowering people to contribute to decision-making with their perspectives. Approaching any challenge from multiple perspectives enables decision-makers to make better choices.
  6. Understand the story behind your data: Ultimately, all of these benefits of data visualisation lead to one key outcome — a more comprehensive understanding of the story behind a business’s data. Armed with this knowledge, businesses can make better informed decisions that help to drive outcomes and business success in the long term.

Data visualisation tools

Cutting-edge data visualisation tools are essential for converting raw data into actionable insights. As a result, identifying and deploying the right tools is vital for businesses looking to uncover valuable insights that can help drive growth.

Fortunately, there are now a range of data visualisations tools available to businesses looking to harness the power of their data. The most popular among these include:

  • Domo: Domo is a cloud software company specialising in business intelligence tools and data visualisation.
  • Dundas BI: Dundas Data Visualization, Inc. is a software company specialising in data visualisation and dashboard solutions.
  • Infogram: Infogram is a web-based data visualisation and infographics platform.
  • Looker: Part of the Google Cloud Platform following a 2019 acquisition, Looker markets a data exploration and discovery business intelligence platform.
  • Microsoft Power BI: Power BI is an interactive data visualisation software developed by Microsoft with a primary focus on business intelligence.
  • Qlik: Qlik is a business analytics platform that provides software products such as business intelligence and data integration.
  • Sisense: Sisense is a business intelligence software company best known for embedded analytics.
  • Tableau: Tableau Software is an interactive data visualisation software company focused on business intelligence specialising in visualisation techniques.

Even if businesses have access to one or more of these tools, that isn’t enough to ensure effective visualisations. Remember, collecting, sorting, cleansing and analysing data before it gets fed into a cutting-edge tool is essential to ensuring accurate and relevant insights.

And that’s not all. On top of that, businesses also need knowledge, skills and expertise to ensure that tools such as those outlined above are used correctly and therefore produce results that drive positive outcomes.

Enhance your decision-making with data visualisation

Data visualisation has a track record of driving progress. For example, the 1854 Cholera Outbreak Map of London marked the locations of outbreaks, revealing that affected households used the same drinking water wells. Examination of these wells demonstrated a connection between cholera and contaminated water.4 These results helped the city eradicate cholera and contributed to Louis Pasteur’s discovery of modern germ theory.

Over a hundred years later, businesses are looking to leverage data to ensure both growth and prosperity. A comprehensive data strategy that facilitates visualisations that enhance decision-making processes has therefore become essential to long-term success.

However, that requires access to significant knowledge, expertise and cutting-edge tools, all of which can be difficult to obtain and retain in-house. That’s where data analytics providers like Jarmany come in. We’re here to ensure that your business can establish a successful data strategy that delivers insights through stimulating visualisations.

So, if you’re ready to start using your data to predict needs, deliver efficiencies, connect people and achieve growth targets, get in touch with us today.

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1  Knowledge Is Power: How Data Is Feeding Disruption

2  Catch them if you can: How leaders in data and analytics have pulled ahead

3  Delivering personalized experiences in times of change

4  John Snow’s data journalism: the cholera map that changed the world

6 Ways Third-Party Data Can Benefit Your Business

Whilst first-party data can provide rich and meaningful insights on your customers and can feed into machine learning, it often lacks breadth, especially if your business isn’t able to collect, store and manage valuable high quality first-party data efficiently.

This is where third-party data comes in.

Third-party data refers to data that is collected by organisations outside of your company and can be used to gain valuable insights into your target audience, industry, or market.

In this blog post, we’ll explore the reasons why third-party data is so important and how it can benefit businesses of all sizes.

#1 Close the gaps in your data

A lot of organisations are collecting their own first-party data to help derive actionable insights and gain a greater understanding of their customers to then guide decision making.

This could be:

  • Website data
  • Social data
  • Marketing data
  • Operations data
  • Sales data

Whilst this first-party data can be very high value, unless you have a large quantity of it, it often lacks validity and is not enough to base high-level decisions on. This impacts the quality and reliability of your analysis.

In this scenario, third-party data can be used to close the gaps to enhance the value of your insights and findings. Put simply, third-party data cannot match an organisation’s first-party data, however, it can help you build on to the insights you already have. First-party data lays the foundations, third-party data heightens it and allows you to broaden your data ecosystem.

#2 Greater context into customer behaviour

Even if your business is a well-oiled machine when it comes to collecting first-party data, this is often useless if you don’t understand the macro-economic factors driving consumer behaviour.

This could include:

  • Geographical trends
  • Demographic changes
  • Environmental changes
  • Political news
  • Market share/size information

By utilising third-party data, you can obtain insights that will help you to understand current behaviour and predict future behaviour, so you can calculate any impact on business operations, and gain greater insights into supply and demand shifts.

#3 Understanding your target audience

Third-party data can help you better understand your target audience and their behaviours, interests, and preferences. This information can be used to create more targeted marketing campaigns and to develop more effective customer engagement strategies. For example, if you’re selling athletic clothing, you might use third-party data to learn more about your customers’ exercise habits, which can help you create content and promotions that resonate with them.

#4 Strengthen Indirect Sales Insights

Third-party data is also pivotal if your business operates through indirect sales channels, as it enables you to gain insights into your sales activity through each third-party retailer. Without it, you only have a partial understanding of your sales performance.

For example, if you were a company selling computers direct to the consumer, but also indirectly through a retailer, you would have access to certain information, such as no. of units you were providing to the retailer, product price point and location where the units are sold. However, you’d be missing a range of insights such as how the retailers discount & marketing schemes impact sales, whether the user is purchasing online or in person, or if certain areas of the world sell better than others.

This is where you can really benefit from utilising third-party data to gain more granularity into your indirect sales performance.

#5 Improving marketing and advertising efforts

Third-party data can also be used to improve your marketing and advertising efforts by providing a more complete picture of your target audience. As a result, you’ll be able to offer a deeper level of personalisation to help your ads resonate more with your target audience.

For example, you can use third-party data to create more effective targeting strategies for your digital ads, such as targeting based on demographics, interests, or purchase history.

This information can also be used to improve your email marketing campaigns by personalising your messages and making them more relevant to your subscribers.

#6 Making informed business decisions

Ultimately, third-party data can provide you with the valuable insights into your industry and market that can be used to make informed business decisions. It allows you assess the competitive landscape, identify market trends, determine the best target audience for your product and predict future customer behaviour. Combined with your first-party data, this information can provide you with a complete picture that will then guide your business in terms of pricing, distribution, product positioning and much more.

In conclusion, third-party data is a valuable tool that can help businesses to close the gaps in their data, gain greater context into customer behaviour, build a better understanding of their target audience, strengthen indirect sales insights, improve their marketing and advertising efforts, and ultimately make informed business decisions. Whether you’re a small business just starting out or a large corporation looking to stay ahead of the competition, incorporating third-party data into your data strategy is essential for success.

How Jarmany can help you

Managing your third-party data can be a minefield, especially in a privacy conscious world with increasing regulations around data protection and misuse. It can also be a struggle to integrate this third-party data with your existing data, and using this to build and feed machine learning models to gain enhanced insights. Additionally, this type of data management requires a specialised skillset, which is often very timely and expensive to build internally. As a result, leaning on a specialist agency, who have expertise in storing, managing and transforming data in order to gain actionable insights is often the favoured approach.

Get in contact with us today if you’d like to explore how we can help you manage your data, use techniques such as web scraping to obtain more insights, and then build machine learning models to help you drive business growth.


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Google Analytics 4 Guide: What You Need to Know

You might already be familiar with GA4 — many businesses have been using it alongside UA for the last two years. Alternatively, you might know next to nothing about it. Whatever the case, getting to grips with GA4 is important to your business.

Google Analytics is one of the most popular analytics tools, with over half (55%) of online businesses using it to gain visibility into key website metrics.1 Understanding how the latest version works should be a priority.

But don’t worry, we’ve got you covered. In this GA4 guide, we’ll explain everything you need to know to get you ready for the shift to GA4 — and leverage it to gain a deeper understanding of your customers. But first, let’s answer an important question.

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What is GA4?

GA4 is an analytics service that allows you to measure traffic, engagement, and performance across your websites and apps (known as properties), giving you the insights you need to improve all three.

Launched in 2020, GA4 is the fourth and latest version of Google Analytics. It was designed to phase out and ultimately replace the previous version, Universal Analytics, which was built when the digital world was very different from today.

GA4 provides data insights throughout the customer lifecycle, making it a useful tool for businesses or marketers seeking to understand how customers behave before, during, and after conversion. As you’d expect from a modern data analytics platform, GA4 also offers machine learning insights and data science analysis.

GA4 vs Universal Analytics

Up until October 2020, Universal Analytics was the default version used when a new Google Analytics property was created. After that date, the default version became GA4.

Google now plans to phase UA out completely. From July 2023, UA will stop processing new hits, although users will still be able to access data for their Universal Analytics properties for another six months.

Universal Analytics 360 (also referred to as Google Analytics 360), on the other hand, is used by bigger, enterprise-sized businesses. UA 360 is a scaled-up, paid version of UA with extended capabilities. UA 360 has higher data limits, service level agreements and, support-wise, a dedicated account manager and implementation support.

Like UA, however, UA 360 is being sunsetted, albeit from the later date of July 2024.

What’s the difference between GA4 and UA?

There are several key differences between GA4 and UA. In this section, we’ll highlight the most important ones to understand. 

Events vs sessions

GA4 uses a fundamentally different model for measuring data compared to its predecessor. UA’s measurement model was based on sessions, including any number of user interactions ( known as hits) within a specific time period. These could include page views, clicks, and transactions, for example.

GA4’s data collection model, on the other hand, is based on events, with any user interaction qualifying as a separate event.

The change to events, however, has also led to some ‘missing’ metrics and reports, in particular bounce rate. The bounce rate metric in UA is replaced with ‘engaged sessions’, which shows sessions lasting 10 seconds or longer, has one or more conversion events, or two or more screen or page views.

Other valuable metrics available in UA, such as views per session and average session duration, while harder to access in GA4, have recently been made available through customisable reports.

Multiple devices

UA was designed for a world where desktop reigned supreme. Since its launch in 2005, however, the world has changed drastically. Today, people access digital services across a range of devices, with mobile becoming increasingly popular in recent years. GA4 is designed to track the users of today, seamlessly collecting data across multiple devices.

Machine learning

GA4 has machine learning (ML) capabilities, enabling it to use current and historical data to predict how your users might behave in the future. The resulting insights allow you to see the probability of customers purchasing something or churning, for example. UA, on the other hand, has no ML capabilities.

Data protection and security

GA4 anonymises IP addresses automatically, guarding against the identification and misuse of personal information and protecting personal privacy. Unlike Universal Analytics, this brings GA4 in line with GDPR compliance.


Compared with UA, GA4 focuses on tracking user IDs rather than cookies. Reducing the reliance on cookies helps future-proof GA4 and move away from UA’s focus on tracking page visits and sessions through cookies. This will help improve the quality and access to insights across multiple platforms.

Google Ads

GA4 enjoys a deeper integration with Google Ads, allowing you to measure app and web integrations together. This ultimately provides a deeper level of insight than UA.

More reporting

With GA4, you get reporting options across the customer lifecycle, with reports focusing on acquisition, engagement, monetisation, and retention (more on this later). With UA, on the other hand, you only get reporting for acquisition. 

GA4 is taking over

GA4 is designed to meet the needs of businesses in 2023, enabling them to understand how their customers behave across platforms and journeys. UA was designed for an era of desktop dominance and cookie-related data — ideas that are slowly becoming obsolete. 

With UA being phased out completely by summer, now’s the time to switch to Google Analytics 4 — if you haven’t already. This means you should:

  • ensure you have a centralised archive of historical data you can draw from
  • set up GA4 event tracking  
  • and transition all your existing UA properties to GA4.

Setting up GA4

As set out in the Google Support guide, GA4 is relatively simple to set up — if you know how.2 In this section, we’ll walk you through the process step by step.

  1. Log in to your Google Analytics account.
  2. Check which version you are currently using. If you can see three columns (Account, Property, and View), you are using UA. If you can see just two columns (Account and Property), you are already using GA4.
  3. Assuming you are still using UA, select ‘GA4 Setup Assistant’ under the Property column.
  4. Click ‘Get Started’ to set up a Google Analytics 4 property. Alternatively, if you already have a GA4 property that isn’t connected to your Google Analytics account, select ‘Connect Properties’ and follow the instructions.
  5. If you are already using gtag.js tags, select ‘Enable data collection using your existing tags.’ If you are using Google Tag Manager or the old analytics.js tags, you’ll need to add gtag.js tags yourself.
  6. Click ‘Create property’.

Once you’re up and running, you can set up a range of capabilities designed to help you track and obtain data, including:

  • Configure Custom Events
  • Configure User IDs
  • Configure Enhanced Measurements
  • Activate Google Signals
  • Link to Google Ads
  • Define Audiences
  • Import or set up Conversions

Using GA4

Tracking across multiple platforms

One key benefit of GA4 is the ability to track data across multiple platforms — something that was virtually impossible in UA. In practice, this means that GA4 tracks website and app data for one property. So if a user visits your site using a laptop and a mobile, the data for the various sessions is consolidated under one user rather than two. This helps you keep track of the same user across multiple devices and sessions.

Cross-platform tracking provides a much more complete view of user behaviour, allowing you to understand how customers engage with your website or app, as well as the different devices they are using to access them. You get to see the entire customer journey — from acquisition through engagement and retention — across various platforms. 

To set up cross-platform tracking, you need to use the appropriate gtag.js script to create unique user IDs. These IDs can then be configured to track users across platforms. 

Using Events

As we touched on earlier in this Google Analytics 4 guide, GA4’s data collection model is based on events. Sessions dominated UA — and they’re still used to a degree — but events are how you track almost everything in GA4. 

Put simply, all user actions on your site or app now qualify as events. So to understand and track events is to understand and track user behaviour and engagement. You can choose which events you want visibility over, and how you track them is up to you. 

Broadly speaking, events fall into four different categories in GA4:

  • Automatically captured events: These events, such as when a user clicks on an ad or when a free trial is converted to a paid subscription, are automatically tracked by default, without you having to do anything.
  • Enhanced measurement events: These are events that you can enable in GA4, allowing you to measure interactions with your content. Enhanced measurement events can be toggled on and off by going to the Admin column, selecting Data Streams, then Web, and then Enhanced Measurement.
  • Recommended events: These events require additional context to function effectively, meaning you’ll have to set them up yourself. They include ‘login’ events (when a user logs in), ‘search’ (when a user searches your content) and ‘share’ (when a user shares your content).
  • Custom events: These are events that are specific to your business, website, or app and not already known or measured by GA4. With custom events, you define the name and the set of parameters for each event.

How to create a custom event in GA4

To create a custom event in GA4, simply follow these steps: 

  1. Select the Admin icon in the bottom left of your screen
  2. Go to the Property column and select Events
  3. From here, select Create Event 
  4. Choose the data stream for which you want to deploy the event (assuming you have more than one)
  5. Click Select
  6. Follow the rest of the set-up prompts to complete the process

Getting the most out of your GA4 reports

As you’d expect from a data analytics tool, GA4 provides a range of reports and data visualisations designed to help you understand your data — and act upon it. In this section, we’ll explain everything you need to know to get the most out of your GA4 reports. 

Reports snapshot

As the name suggests, the reports snapshot provides an overview of the most popular metrics in one single, easy-to-read dashboard. This is where you go if you need an at-a-glance view of how your property is performing. Data sets in the reports snapshot include things like: 

  • User behaviour
  • New users by channel
  • Number of sessions by channel
  • Users by country
  • User activity over time
  • Views by page and screen
  • Top events
  • Top conversions
  • Top-selling products

The data sets in the snapshot are pulled from other reports, and the reports snapshot is customisable, allowing you to focus on the insights that matter to you most. To customise your report snapshot, you’ll need to follow these steps:

  1. Select Library from the bottom of the left navigation bar (note: you’ll need admin rights to do this — this option isn’t available in a demo account)
  2. Select Reports 
  3. Select Create a new report
  4. Select Create an Overview Report
  5. Follow the set-up steps to complete

Real-time Overview Reports

With GA4, you also get access to real-time reports, allowing you to see how customers are using your website in real time and track their journey through the sales funnel. Real-time reports offer a range of metrics, including:

  • Geo-maps, showing where current users are based
  • Number of users in the last 30 minutes
  • Users by source, showing how your users arrived at your site
  • Users by audience
  • Views by page title and screen name
  • Event count by event name
  • Conversions by event name

Lifecycle reports

GA4 breaks down the customer lifecycle into four stages — acquisition, engagement, monetization, and retention — with corresponding reports for each. Let’s take a look at what they offer. 

  • Acquisition: See how new users found your website or app, allowing you to understand which channels and campaigns are proving the most successful.
  • Engagement: Explore how users interact with and navigate through your website or app, with metrics covering a range of events.
  • Monetization: Get a full breakdown of how your website or app is generating money, covering e-commerce, subscriptions, and ad revenue.  
  • Retention: Understand the frequency and duration of users’ interactions with your website or app after their first visit — and how valuable they are to you over their lifecycle.

Together, these reports give you a complete picture of how users behave across all stages of the customer journey, as well as the value they bring through engagement. Ultimately, this helps you refine your campaigns, content, and UX to improve customer acquisition and retention — and ultimately drive more revenue.

Other reports

In addition to those highlighted above, GA4 comes with a range of other reports designed to give you a complete picture of your users and how they interact with your website or app. 

For example, the Tech report in Google Analytics 4 analyses the technology that people use when visiting your website or app, including the platform, operating system, screen resolution, and app version. 

Meanwhile, the Demographics report breaks down your users by their age, location, gender, and affinity category, which includes acquisition, behaviour and conversion metrics — giving you greater insight into your customer base. 

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Making the most of GA4

If you rely on the Google Analytics platform, it’s time you started thinking about switching from UA to GA4. At Jarmany, we recommend a test and trial period of at least six months; this helps you identify any nuances in your reporting and reconcile them to help you get up and running with GA4. With July’s sunset date coming fast, this is no longer a choice but a necessity. To benefit from the switch, it’s critical that you start to get to grips with GA4 as quickly as possible. 

That said, getting started with Google Analytics 4 can involve a steep learning curve, while migrating from UA to GA4 can be tricky for those without the technical know-how. Plus, for businesses with multiple brands, websites and properties, successfully merging them together in GA4 for a complete view can be tricky. That’s why it pays to work with an expert technology partner with expertise in migration, implementation and support — like Jarmany. 

As an analytics and data consultancy, we can help you seamlessly migrate to GA4, providing you with the support and expertise you need to get up and running fast and maximise its potential. The change is coming, make sure you’re prepared for it with our expert help.

Sounds interesting? Get in touch today and talk to one of our experts. 

1  How Many Websites Use Google Analytics 2022: Google Analytics Statistics.

2 [GA4] Add a Google Analytics 4 property (to a site that already has Analytics) 

Looker Data Visualisation: A Complete Guide

This is where business intelligence and visualisation tools come in. They allow businesses to turn complex data sets into clear visualisations, and then act on them. The result is smarter decision-making, more streamlined processes, and a competitive advantage over businesses that fail to capitalise on this opportunity.

In this article, we’ll take an in-depth look at one of the most popular data visualisation tools on the market: Looker. Read on to learn about: 

  • Looker’s data visualisation capabilities
  • Its key features and how they are used
  • The pros and cons of choosing Looker over one of its competitors
  • How your business can get the most out of this powerful tool

But before we start, let’s answer an important question…

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What is Looker? 

Looker is a data analytics and visualisation tool. It enables businesses to analyse, and explore their data through unique visualisations, helping them to turn raw data into actionable insights that drive smarter business decisions. It does so through powerful features such as integrated insights and data-driven workflows.

Launched back in 2012, in 2019 Looker was acquired by Google for $2.6 billion and is now part of the Google Cloud Platform. It’s a browser-based solution, so there’s no need to worry about installation or maintenance.

While Looker is well-known in the data visualisation world, direct competitors including Microsoft Power BI, Tableau and Qlik might be more familiar to businesses. Though Google’s acquisition of Looker in 2019 is aiming to change that.

As you’d expect, Looker shares some core features with other popular data visualisation and business intelligence tools, such as the ability to: 

  • Build custom real-time dashboards
  • Connect to any SQL database
  • Create custom applications
  • Leverage embedded analytics
  • Access a range of customer support options 

What modelling language does Looker use?

One of Looker’s key differentiators is LookML, its native modelling language. LookML is an SQL-based language, but it aims to improve on SQL’s shortcomings to help users write simplified and streamlined SQL queries.

LookML is a modular, reusable language. And collaboration tools such as version control means that Looker users don’t have to start a script from scratch or spend ages trying to find what changed and when. 

Looker Blocks — pre-built data models designed to fit common analytics patterns — also prevent users from having to start from square one each time they want to create a data model. Users can select pre-existing models and modify them to their needs. This includes:

  • Analytics blocks
  • Source blocks
  • Data blocks
  • Data tool blocks
  • Embedded blocks
  • Viz blocks

Looker’s data visualisation

As the name suggests, Looker is all about data visualisation. In this section, we’ll run through some of its core data visualisation capabilities — and how you can use them to drive business success. 

Looks and dashboards

  • Looks are visualisations created and saved by users. Looks are created in Looker’s Explore section, which can then be shared and used across multiple dashboards.

  • Dashboards allow users to place and view multiple Looks, graphs or tables in a single place. This allows users to, for example, view a range of different but relevant KPIs in the same way in one place. Dashboards are interactive and customisable. For instance, you can put several Looks into one dashboard and add a filter, acting as a master control that affects each Look within that dashboard in the same way.

Both Looks and dashboards can be shared with anyone, helping everyone get on the same page and view and understand the data easily.

Filtering looks and dashboards

Both Dashboards and Looks have filter functionality. Toggling Looks and Dashboards filters can also provide users with greater flexibility and specificity based on the filters’ hierarchies. For example, by selecting a Dasboard filter for a particular year, that filter would apply to all the Looks in that dashboard by default.

However, you can also choose which Looks within a dashboard are affected by that filter. This enables users to set a dashboard filter for a particular year, and then apply a separate filter specific to certain Looks and disable the default dashboard filter for them. This lets you the ability to apply a filter to all your Looks in one dashboard, or apply different filters to Looks within an overall Dashboard filter.

Types of visualisations

Looker features a rich variety of visualisations that allow you to present, read, and understand data in different ways, including: 

  • Cartesian charts, i.e. any chart plotted on x and y axes, including column, bar, line, and scatterplot charts 
  • Pie and donut charts 
  • Progression charts, including funnel charts and timelines 
  • Text and tables, including single value charts, single record charts, and word clouds
  • Maps, including Google Maps
  • Custom visualisations

There are also 40 visualisations available via Looker Studio, previously known as Google Data Studio, as well as custom visualisations created by Looker’s partners. As mentioned above, Looker’s blocks — and Viz blocks in particular — can be used to quickly and easily create data visualisations. 

Hosted by Looker, you can add them to your Looker instance, allowing for seamless visualisations with powerful functionality, including the ability to drill down, download, embed, and schedule data. 

Suggested reading: For a broader look at how you can leverage your company’s data to drive business success, take a look at our guide: 9 Practical Steps to Building Your Data Strategy.

Pros and cons of Looker visualisations

Now you have a solid understanding of what Looker is and how it works, but how do you know if it’s the right choice for your business? In this section, we’ll look at some of the pros and cons of Looker visualisations. 

Looker Pros:

#1 Cloud-based + browser-based

Looker has all the advantages you’d expect from a cloud-based data analytics platform, including advanced security, high performance, and seamless accessibility. And because you access it directly through your browser, you don’t need to worry about software installation or manual updates and maintenance.

#2 Easy Git integration

Looker allows users to integrate the popular version control system Git, enabling multiple people to work on visualisations simultaneously, record changes, and manage file versions. Looker users can see changes made to data-modelling layers, jump back to them at any time, and create different version strands in repositories that developers can then work on.

While not set up automatically, the integration can be easily set up and provides a benefit other data visualisation tools don’t.

#3 Connects with multiple data sources

Looker can connect with and visualise data from multiple disparate sources, including Google Cloud, Microsoft Azure, Amazon Web Services (AWS), on-premises databases, and a range of database software.

And as a Google browser-based product, Looker easily integrates with Google’s entire suite of browser-based applications. This makes sharing Looker dashboards quick and easy, with no downloading and little set up required.

#4 Self-service analytics

Thanks to Looker’s LookML data-modelling language, users can define dimensions, metrics, aggregates, and relationships. These are then used to populate Looker’s data visualisations, providing users with seamless self-service analytics, while enabling them to reuse data and calculations.

Looker Cons:

#1 Limited range of visualisations

While Looker is a perfectly effective and highly popular data visualisation platform, the variety of out-the-box visualisations is somewhat limited — especially compared to competing data analysis and visualisation tools like Tableau. That said, the ability to build custom visualisations goes some way towards mitigating this issue.

#2 More expensive than direct competitors

When compared with its closest competitors — for instance, Microsoft Power BI and Tableau — Looker is the most expensive of the lot. Businesses looking to cut costs may be tempted to look at one of the cheaper, but no less popular, options on the market.

#3 A steep learning curve

Looker isn’t the type of product you can just pick up and play with from the start. Before you begin visualising data, you need to define a semantic model in LookML, which then translates into SQL. This is to ensure that the underlying data is all drawn from the same place and matches up. 

LookML is designed to make things easier — and it does once you understand how it works — but without the right in-house expertise or outside training, it can be a while before you get the most out of Looker and improve your ROI.

Pros Cons
Cloud-based + browser-based Limited visualisations
Easy Git Integration High cost
Connects with multiple data sources Steep learning curve
Self-service analytics Requires expertise to maximise results

Suggested reading: While Looker is a solid choice for many businesses, there are other business intelligence and data visualisation tools on the market. For a closer look at one of Looker’s direct competitors — Microsoft Power BI — check out the below article: 11 Benefits of Using Power BI for Data Analytics

How to create visualisations in Looker

As a visualisation tool, Looker strives to make creating visualisations as easy as possible. Creating Looker visualisations involves the following simple steps: 

  1. Create and run a query in Looker
  2. Click on the Visualisation tab
  3. Select the visualisation type you want to use 
  4. Select Edit to configure and customise your visualisation

Now, let’s look at some key parts of this process in a bit more detail.

How to choose a visualisation type

Once you’ve created and run a query, click on the visualisation tab. You’ll then be able to choose a visualisation type by selecting one of the chart buttons at the top of the screen. To view more visualisation options, simply click on the three dots to the right of the chart buttons. 

Each option displays your data in a different way, and some options are better suited to certain types of data than others. If you’re measuring the change in a value over time, for example, you’ll be well served by a cartesian chart, with the time-related data making up the x (or horizontal) axis. Meanwhile, if you want to visualise how values are proportioned in relation to each other, a donut chart is your best bet. 

How to customise visualisations

Once you’ve selected one of the visualisation types, you can play around with the configuration options to make the data more readable and customise the look and feel of the visualisation. 

Each visualisation type has its own unique configuration options. In a column chart, for example, you can choose whether you want the data to be grouped or stacked, what kind of spacing you want between columns, the colour of each column, etc. Have a play around and see what works for you. 

Creating multiple visualisation types

Looker also allows you to create multiple visualisations within a Look. For example, you might use a column chart or line chart visualisation in one Look as a way to compare data or provide additional insight and context.

To do this, follow these steps:

  1. Click on the Edit button to display the customisation options for a particular visualisation
  2. Select the Series tab
  3. Go to the Customizations section and click the arrow next to the particular series
  4. Go to the Type box and select the visualisation type you want for that series

Getting the most out of Looker

Looker is a powerful BI and data visualisation tool that helps you start visualising your data and making business intelligent decisions. But you can only do that once you know how to use it and get the best out of it. The companies that are best able to view their data are best positioned to use that data to drive decision-making.

Without in-house expertise or the right training, the steep learning curve and technical know-how required to maximise its potential can hurt your ROI, and squander the potential within your data. This is where Jarmany can help. 

With our Looker consultancy services, we’ll help you to get the best out of the platform, ensuring that your business capitalises on its powerful data visualisation capabilities. Our team of experts has the experience you need to build visualisation solutions tailored to the unique needs and goals of your business, enabling you to:

  • Master Looker’s native language, LookML
  • Create bespoke visualisations that simplify complex data sets
  • Drive data-driven decision-making across your organisation.

To find out more about how Jarmany could help you use Looker to drive business success, get in touch with one of our experts today.

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Our Top 5 Predictions for 2023

Businesses that use their data to drive decision-making are 9x more likely to be profitable, so it’s no surprise that organisations are re-focusing their investments on data and technology. 

With such substantial growth set for the tech industry, we’ve collated our top 5 data and technology predictions for 2023 to guide you on where and how you should be investing your funds. 

our top 5 predictions for 2023

#1 Data Democratisation

First-up, we have data democratisation. We predict that data democratisation will be more widely adopted in 2023, with businesses starting to incorporate ‘data mesh’ as part of their data strategy. 

Data democratisation is the process of enabling employees throughout your organisation to have access to the data relevant to their roles, irrespective of their technical or analytical background. This reduces gatekeepers or bottlenecks, therefore improving efficiency.  

With so much emphasis placed on the value data and actionable insights can drive in your business, it’s important that data is accessible for employees across all verticals. By empowering your entire workforce with data-driven insights, you’re enabling them to do their job more effectively and efficiently.  

A recent study, conducted by McKinsey, found that companies that make data accessible to their entire workforce are 40x more likely to say analytics has a positive impact on revenue. 

As an extension of this, we predict that firms will start adopting a ‘data mesh’ approach, whereby data is de-centralised and each business team has internal data literate capabilities, therefore enabling them to more easily self-serve. If you aren’t already, you should be considering how to adopt a data democratisation and data mesh approach throughout your organisation. 

#2 AI and Machine Learning

No surprise here and of course is an annual trend that you obviously can’t look past. Applications are already live in a number of organisations today, from chatbots and automated responders to process and machinery automation and business forecasting models. However, we’re expecting this to seriously ramp up in 2023.  

According to IDC researchworldwide AI technology spend by governments and businesses is expected to exceed $500 billion in 2023. Gartner also predicts that in 2023, ML will penetrate even more business fields helping to increase efficiency and work security. 

ChatGPT is a prime example of this. Released at the end of 2022 by OpenAI, this new generation chatbot has the ability to understand natural human language and generate detailed human-like responses. This advanced AI technology is already paving the way for next generation customer service chatbots within companies such as Meta, Canva and Shopify. 

With the business world becoming increasingly competitive, factors such as personalisation is also what will set you apart from the competition in 2023 and beyond. Consumers want a personalised experience, and those that get it are 80% more likely to buy from a brand – AI and ML will help you achieve this so you can attain this competitive advantage. 

Machine learning models provide businesses with the means to deliver a more scalable and accurate way of achieving unique experiences for individual users. They enable businesses to track and observe digital habits so they can then pre-empt future consumer behaviour. 

If AI and ML isn’t already part of your digital growth plan for 2023, then it should be. 

#3 Data Clean Rooms

The diminishing of third-party cookies has been a popular topic in 2022, as the deadline fast-approaches Google relinquishing support on their browser, Chrome. This places an even greater emphasis on the importance of first party data to help provide in-depth customer insights. However, first party data will only take you part of the way. Consider data clean rooms as a new solution.

Data clean rooms refers to a piece of software that enables two parties, typically publishers and advertisers, to share anonymised customer data for joint analysis. This private data exchange enables heightened insights on your first party data so you can:

  • Understand how customers are interacting with other brands
  • Establish lookalike audiences
  • Avoid duplicate efforts across channels
  • Build new customer segments for targeting

Walled gardens are a common example of data clean rooms, with the likes of Google, Amazon and Facebook sharing their aggregated customer-level data with advertisers.

With third-party cookies posing huge attribution and insights challenges, we think this is going to be particularly important in 2023 to help brands bridge those gaps in a post-cookie world.

#4 Optimising IT Systems

Computing power and technology has come leaps and bounds in the last few decades, with revolutionary new platforms and tools available and accessible for more people. However, ensuring you have the right systems in place is vital if you want to keep up with the pace of new and developing technologies, and the ever-increasing flow of data into your business. 

When reviewing and updating your IT systems and data stacks you should be considering the 4 v’s of data: 

  • Volume
  • Velocity
  • Variety
  • Veracity 

2023 is going to be a pivotal year for enhancing IT solutions, with factors such as the metaverse, a greater need for automation and systems that can cope with vast amount of data driving this evolution of IT systems. Investing in the right data stacks will therefore be essential. 

Your IT systems should enable you to: 

  • Analyse your data in real-time data
  • Adhere to privacy and security regulations
  • Ensure smoother automation
  • Collect, store and manage your big data
  • And much more.

Further to this, we predict that cloud storage will take strides in 2023 to enable all of the above points and more. If you have the right cloud storage solution in place, you’ll find it easier to scale up as your business grows, and your data will be much more secure. 

According to statistics, about 60% of the world’s corporate data is stored in the cloud, and this number is likely to grow. As a result, you should be factoring in cloud storage as part of your digital transformation strategy in 2023. 

#5 Data as a Service

DaaS can be defined as “a data management strategy that is used to store data and analytics. DaaS companies are organisations that provide customers with a service surrounding data – meaning data management, data storage, and analytics are the main selling points of the software.”  

All of the above points require specialist skills, expertise and experience to deploy, and this can be especially challenging to deliver and maintain internally due to skill shortages in the industry.   

You can offset these challenges by partnering with DaaS providers and agencies. And, we predict that the majority of firms will tap in to the expertise of these types of partners to help deliver their 2023 digital transformation strategy and manage their data and analytics, in turn freeing up their internal resource to focus on higher priority tasks. 

It is estimated that the DaaS market will grow to $10.7 billion in 2023, further demonstrating the value that third-party providers can add to your business. 

Get in touch

The digital world and power of computing systems is enhancing at an unparalleled pace, and having the right technology and data processes in place will be the driving factor for your business achieving growth. 

Ensuring that you’re investing your efforts and finances in the right way will keep you ahead of the game, and we’re confident that if you focus on the 5 points we’ve listed in this blog to define your digital strategy in 2023, you can’t go far wrong. 

If you’d like to discuss how Jarmany can support you on your data and digital journey in 2023 then please contact us today. 

Best Practices for Data Modelling in Qlik


In today’s digital world, data is the lifeblood of business. Whether you’re a small eCommerce retailer or a multinational corporation, data analytics and visualisation give you a competitive advantage by driving smarter decision-making. But for any data to work within an analytics or visualisation platform, you need to get the foundations right. That means effective data modelling.

In this article, we’ll look at some of the best practices for data modelling in Qlik — a popular analytics platform that provides powerful real-time business intelligence and data visualisation. Qlik’s two main solutions, both of which can be used for data modelling, are: 

  • QlikView: A data analytics, visualisation, and reporting tool that helps businesses make sense of their data using charts and dashboards.
  • Qlik Sense: Launched in 2014, Qlik Sense is a modern, self-service data exploration tool that allows users to build custom dashboards via drag-and-drop functionality.

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Why is good data modelling important? 

Businesses today collect a vast amount of data from multiple sources. But the usefulness of raw data is limited; it becomes useful when it’s transferred into an understandable and actionable format.

Data modelling is the visualisation and blueprint for how the data will be used. Without effective data models, platforms like QlikView and Qlik Sense can’t perform at their best, resulting in sluggish performance. To get the most out of your data, you need to design and implement a data model that: 

  • Reduces your system’s memory storage by freeing up access data
  • Creates high-quality visualisations in real-time
  • Run platforms, like Qlik, efficiently.

Qlik data model best practices

Data modelling can be a complex process. In this section, we’ll break down some of the data model best practices for QlikView and Qlik Sense, helping you get the most out of your data. Let’s dive in.

#1 Working with crosstables

A crosstable is a table consisting of columns and rows in a grid-like format. The top row contains one field, and the left-hand column contains another, with data populating the grid accordingly. See the example below. 

Year Jan Feb Mar Apr May Jun Jul
2019 56 34 60 48 84 80 74
2020 19 32 83 54 23 38 20
2021 33 37 43 29 20 09 11

While this may look appealing, it’s not the ideal format for data modelling in Qlik. If you load data this way, it would display a field for the year plus additional fields for every month, whereas you most likely need just three fields: the year, the month, and the respective values.

You can fix this problem by adding the crosstable prefix to the SELECT or LOAD script. Here’s an example: 

Crosstable (Month, Sales) LOAD * from ex1.xlsx

What you get is this: 

Year Month Units
2019 Jan 56
2019 Feb 34
2019 Mar 60
2019 Apr 48
2019 May 84
2019 Jun 80
2019 Jul 74

This process enables efficient data structuring and is the same whether you are using QlikView or Qlik Sense.

#2 Star schema vs Snowflake schema

Using a star schema in both QlikView and Qlik sense is the most efficient schema technique. Using a central fact table containing the relevant fields and keys, surrounded by dimensional tables that contain the attributes of the fields located in the central table, is the easiest to understand schema for data modelling.

Snowflake schemas, though useful for more complex fields and data, are less efficient due to the additional, intermediary tables through which information needs to travel.

Pro Tip: Circular references or loops — tables with more than one path of association between two fields — should be eliminated to improve efficiency. Qlik Sense uses loosely coupled tables to break circular references.  

#3 Join and keep

You can combine two data tables in Qlik using the join and keep prefixes in your script. Join is used to fully combine two tables, creating all possible combinations of values from the tables. As a result, joined tables can be huge and slow to process in Qlik.

This is where the keep functionality comes in. Instead of joining tables to create one large table, keep allows you to link the two tables together, reducing repeated or identical data from the two, while continuing to store them as separate tables. This reduces the table size, ensuring faster processing times while freeing up memory.

The process here is the same for both QlikView and Qlik Sense.

#4 Incremental load

Incremental load allows you to load only new or updated data, as opposed to loading the entire data set each time. The best and fastest way to go about an incremental load is by using QVD files. 

Here’s how the basic process works in both QlikView and Qlik Sense: 

  1. New or updated data is loaded from the data source table. While this can be a slow process, only a limited number of records are actually loaded.
  2. Existing/old data is loaded from the QVD file. This involves loading a lot of records but at a much faster speed. 
  3. You then create a new QVD file, containing both the old and new data, which you’ll use the next time you want to do an incremental load.
  4. Repeat this for each table you want to load. 

Pro Tip: Using an ‘As-Of calendar’ prevents users from loading data multiple times to get previous-period calculations. An As-Of calendar prevents multiplication of data volumes.

#5 Generic databases

To display attributes of different objects, you can store data in generic databases. These are essentially tables where field names are stored as values in one column, with field values stored in a second column. See the example below: 

Object Attribute Value
Ball Colour Blue
Ball Diameter 30 cm
Ball Weight 250 g
Box Colour Red
Box Length 25 cm
Box Width 15 cm
Box Weight 400 g

As you can see, this table contains two objects: a ball and a box. While they share some common attributes, e.g. colour and weight, other attributes are specific to one or the other, e.g. diameter or length/width. 

If you load this table as a generic database in Qlik Sense or QlikView, the attributes in the second column become tables of their own, allowing the data to be stored in a more compact way. See the examples below. 

30 cm
250 g
400 g

Pro Tip: Giving tables easy and intuitive names helps users easily filter data and fields using table names. 

#6 Matching intervals to discrete data

By adding the intervalmatch prefix to a LOAD or SELECT statement in Qlik Sense or QlikView, you can link discrete numeric values from one table to different numeric intervals in another table. 

This allows you to show, for example, how certain events actually took place compared to how they were expected to take place. It is particularly powerful in manufacturing, where production lines are scheduled to run at certain times, but due to breakdowns, delays, or other errors, they may run at different times.

There are a few important points to consider when using interval matching: 

  • The discrete data points must already have been read in Qlik before using intervalmatch.
  • The table you want to be matched must always contain two fields, typically start and end.
  • Intervals are always closed, with endpoints included in the interval.

#7 Using and loading hierarchy data

Hierarchy data can be displayed in Qlik Sense and QlikView in several ways, including adjacent nodes tables, expanded nodes tables, and ancestors tables. Let’s take a look at what each one offers. 

Adjacent nodes tables: each node in the hierarchy is stored once and is linked to the node’s parent (see the examples below). Adjacent nodes tables are the simplest way to present hierarchy data. While good for maintaining unbalanced hierarchies, they aren’t suitable for detailed analysis. 

NodeID ParentNodeID Title
2 1 Director 
3 2 Senior manager
4 3 Manager

Expanded nodes tables: In this type of table, each level of the hierarchy is presented in its own separate field, making it easier to use in a tree structure (see example below). 

Expanded nodes tables are more suitable for querying and analysis than adjacent nodes tables, but aren’t best suited for searches or selections as you need prior knowledge of each level you want to search for or select. 

NodeID ParentNodeID Title Title1 Title2 Title3 Title4
2 1 Director CEO Director
3 2 Senior Manager CEO Director Senior Manager
4 3 Manager CEO Director Senior Manager Manager

Ancestors table: This table solves the search/selection issues that come with expanded nodes tables, presenting hierarchy data in even greater detail. Ancestors tables show a unique record for each child-ancestor relation in the data, including keys and names for each child as well as for each ancestor. 

#8 Data cleansing

Sometimes, field values that represent the same thing may be written differently. For example, you could find the following common field values in different tables: UK, U.K., United Kingdom

All three field values clearly mean the same thing, but the lack of consistency in their formatting means they could be interpreted as different values, leading to messy, inaccurate, or redundant data. This is why data cleansing is so important. 

You can cleanse such data in Qlik Sense and QlikView using a mapping table, which maps the column values between different tables. This ensures that values that are written in different ways will consistently be recognised as the same value, not different ones.

#9 Mapping instead of joining

As we discussed in point #2, the join prefix is a powerful way to combine multiple tables in Qlik Sense and QlikView, but it often results in very large tables that can be a drag on performance. You can get around this problem by using mapping instead. 

Let’s look at an example. The first table below presents a business’s order book. Imagine you needed to know which countries your customers are from, which is stored in the second table below.

OrderID OrderDate ShipperID Freight CustomerID
470 2022-11-01 1 62 2
471 2022-11-02 2 58 1
472 2022-11-02 1 32 3
473 2022-11-04 1 11 4
Customer ID Name Country
2 ElectroCorp Italy
3 DataMesh France
4 Coopers UK

To look up the country of a customer, you’d need to create a mapping table, like the one below:

CustomerID Country
2 Italy
3 France
4 UK

By applying the mapping table to the order table, you create a clear table, like this:

OrderID OrderDate ShipperID Freight CustomerID Country
470 2022-11-01 1 62 2 Italy
471 2022-11-02 2 58 1 USA
472 2022-11-02 1 32 3 France
473 2022-11-04 1 11 4 UK

#10 Creating date intervals from single dates

In some cases, time intervals are not stored with a beginning and an end time, but rather a single field representing when something changed. Take this table below, for example, which shows different rates for two different currencies: 

Currency Change Date Rate
EUR 8.59
EUR 28/01/2013 8.69
EUR 15/02/2013 8.45
USD 6.50
USD 10/01/2013 6.56
USD 03/02/2013 6.30

In this instance, the change date field is equivalent to the beginning date of an interval, and the end date is defined by the beginning of the next interval. The two empty rows in the change date column show the initial currency conversion rate, prior to the first change being made.

Additionally, there’s no end date column. To create a new table that has an end date column, you’ll need to follow the steps outlined in this article for Qlik Sense and this article for QlikView. Once that’s done, you will produce a table like this: 

Currency Rate FromDate ToDate
EUR 8.45 15/02/2013 01/03/2013
EUR 8.69 28/01/2013 14/02/2013
EUR 8.59 01/01/2013 28/01/2013
USD 6.30 03/02/2013 01/03/2013
USD 6.56 10/01/2013 02/02/2013
USD 6.50 01/01/2013 09/01/2013

Pro Tip: When using multiple dates, using a master calendar with canonical dates helps reduce multiple calendars, each of which contain date fields. 

Making best practice normal practice

Data modelling is a complicated process. But to make the most of your data and powerful platforms like Qlik, effective data modelling is critical. Without a solid understanding of Qlik data model best practices, however, you could put unnecessary strain on the platform — and never truly unlock the insights in your data.

This can affect the speed and efficiency of your data processing, which in turn can impact the speed of your decision-making, the value of your data, and the ROI of your investment in the tool itself. 

By working with a trusted data partner like Jarmany, you can sidestep these issues altogether, ensuring that you get the most out of Qlik and, as a result, your data. Whether it’s supplementing your in-house team or providing a fully outsourced service, our experts are here to help you implement data modelling best practices with minimum hassle and maximum benefit. 

If you’d like to find out more about how Jarmany could help you unlock the power of Qlik, get in touch today and talk to one of our experts. 


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Why First Party Data Should Be Your First Priority

The sunsetting of third-party cookies has shaken up the marketing and advertising industry, with many now searching for alternative ways to identify and target audiences whilst balancing growing consumer demand for data privacy, security, and greater control and visibility of their data.

Alongside this, factors such as GDPR, Google’s privacy sandbox and Apple’s IOS14 update are further restraints that the advertising ecosystem needs to navigate.

In this blog, we’re going to discuss why first party data should be your first priority, what you should be doing to enhance your first party data strategy, and how it can help you to deliver a personalised customer-first experience whilst remaining fully compliant in a post-cookie world.

Let’s get to it.

The importance of first party data

Google’s current plan is for third-party cookies to be phased out of Chrome by the end of 2024. Most web browsers are already blocking third-party cookies by default, Google’s update will of course have the largest impact.

This means businesses will be unable to conduct cross-domain tracking and as a result will be unable to see:

  • What other websites the user has visited
  • Their end-to-end user journey
  • Other products or services they’ve purchased

Put simply, there will be much less data at our disposal, affecting our understanding of users and our ability to deliver a personalised experience. This places even more emphasis on what we do our first party data. The use of first-party data is the number 1 lever for business growth and gaining competitive differentiation through personalised experiences.

And businesses that utilise their first party data can benefit from:

  • 2 x incremental revenue
  • 1.5x cost efficiency1

First party data powers personalisation

Consumers want a personalised experience, and those that get it are 80% more likely to buy from a brand.

Let’s take a look at Spotify as an example. Since 2016 they’ve been running their viral ‘Spotify Unwrapped’ campaign every December – using their first party data to create the ultimate personalised music experience, focused on telling you what you listen to…and don’t we all love it!

In fact, the 2020 campaign generated over 60 million shares from 90 million users and led to a 21% surge in downloads of the Spotify mobile app2. All just from centring a campaign around their first party data to build a buzz around their brand and generate customer loyalty.

Adopt a privacy-first approach

Whilst we’ve established the importance of first party data, building a database of loyal customers is far from straightforward. There’s been a shift in consumer perspective of data privacy, fuelled by GDPR, and so consumers are increasingly conscious of controlling who has access to their data. It’s therefore vital that brands adopt a privacy-first approach in their digital marketing to establish trust.

It’s also important to identify what your value exchange is. Ask yourself, what is the customer gaining by consenting their information? Does it mean they’re going to get access to a one-time 10% discount code, or perhaps you’re able to give them access to content that matches their interests. Be transparent with it – tell your customers what the value exchange is.

By providing a positive privacy experience, not only are you more likely to gain first party consent, but a recent study also indicated that some companies could increase brand share by 43%3. That same study also found that a poor privacy experience was almost as damaging as a data privacy breach.

Use first party data to fuel machine learning

We’ve entered the ‘predictive era’ of digital marketing, whereby sophisticated predictive modelling and algorithms, such as artificial intelligence and machine learning, are increasingly used to pre-empt consumers behaviour and bridge the gap between observable and unobservable insights. It’s therefore vital that you have strong first party customer data to fuel your machine learning.

“As online advertising becomes more automated, your first party data plays a critical role in optimising towards your KPIs. Machine learning is only as good as what you ask it to optimise.”4

First party data can come from a plethora of sources, including:

  • Web
  • App
  • CRM
  • Social

Data modelling can also help you to connect these disparate data sources so you can see the true picture of your customer data and avoid viewing data sources in silo.

Get in touch

Consolidating your first party data and then using this to fuel machine learning sounds relatively straightforward, but in practice it’s more sophisticated than you may think and requires a specialised skillset. This skillset can be very timely and expensive to build internally – as a result leaning on specialist agency, like us (no apologies for the plug), to support you is often the best approach.

Get in contact with us today if you’d like to explore how we can help you manage your first party data and then build machine learning models to help you drive business growth.


1Responsible Marketing with First-Party Data | BCG

2Spotify Wrapped is free advertising that says nothing about the joy of music | Music | The Guardian

3&4Havas Media Group

9 Advantages of Using Tableau

Having data is one thing, but being able to utilise it to drive positive change is another. After all, data is simply a raw material. Turning it into actionable insights requires the right tools, processes, and expertise.

Get this right, and your business will have a significant competitive advantage over those that don’t. Data-driven businesses are: 

  • 23x more likely to acquire new customers
  • 6x more likely to retain those customers
  • 19x more likely to be profitable1

Data visualisation tools play a central role in the process of becoming more data-driven. By presenting the results of data analysis in a way that is clear, graphical, and actionable, they enable anyone to understand and act on data insights. 

In this article, we’ll look at one of the most popular data visualisation tools on the market — Tableau — and how it could benefit your business. 

What is Tableau?

Tableau is a data visualisation tool that helps organisations maximise the potential of their data and better inform in their decision-making.

Launched in 2003, Tableau is the market leader in the data visualisation space, with a market share of around 18%, putting it just ahead of competitor Microsoft Power BI2 . The company behind the tool, Tableau Software, was acquired by SaaS giant Salesforce in 2019 for over $15 billion.

So, what are the benefits of using Tableau? Let’s dive right in.

#1 Data visualisation

As the name suggests, data visualisation is the process of presenting data insights visually, allowing users to spot patterns, see trends, and understand and unpack insights.

Data visualisation is incredibly powerful because it allows us to process information faster. It’s far easier to understand graphs and charts than raw data and spreadsheets. As a result, anyone can leverage the power of data analytics to make better decisions — whether they’re a data expert or not.

Tableau brings together data from multiple sources and transforms it into easy-to-understand, customisable visualisations that empower teams to make better decisions.

#2 Interactive visualisations

Interactive visualisations allow users to explore and analyse data directly via customisable, responsive dashboards. This empowers users to drill down into the data and uncover new insights. This is particularly helpful for numerous teams using the same dashboard, as it enables them to drill down into the data as much or as little as they need.

With Tableau, visualisations are built through simple drag-and-drop functions to create dashboards and reports. Users can then interact with these visualisations through filtering and selection options.

This makes the process of data analysis more intuitive and inclusive, to those creating the visualisation to those viewing it, making it much easier to understand at a glance, compared to a messy and overwhelming Excel sheet.

#3 Easy implementation

Unlike tools such as SAP BusinessObjects and Domo, or programming languages such as Python or R, you don’t need to be able to code to use Tableau. Nor do you need to be a data expert or data scientist.

Instead, Tableau offers an intuitive, user-friendly interface that makes it relatively simple to use. This empowers teams from across an organisation to become more data-driven and data-literate, instead of having to rely on internal data teams to spoon-feed them insights. But ease-of-use doesn’t mean limited functionality, power of flexibility: users can go beyond this, drawing more out of the data with coding and more sophisticated techniques.

Tableau is also simple to set up, meaning you can start making data-driven decisions faster.

#4 Data source compatibility

Businesses today collect data across multiple sources — everything from files, spreadsheets, and databases to cloud-based applications. Tableau allows you to connect to, access, and blend data from multiple sources into single visualisations.

This means that you don’t have to create different types of visualisation for different data sources. Alternatively, you can choose to use a range of data sources but view them separately.

With Tableau, you get a complete view of all your data — from sources including SQL Server, Google Analytics and Salesforce — allowing you to streamline processes and make smarter decisions.

#5 Use multiple scripting languages

While you don’t need to be able to write code to use Tableau, it is possible to use programming languages such as Python and R to maximise Tableau’s potential and create more complex data flows and calculations.

By using Python scripts within Tableau, it enables users to:

  • Transform data
  • Run complex machine-learning pipelines
  • Query remote sources for information
  • Fix potential performance issues
  • Speed up computation speeds

Python scripts do this in Tableau in two main ways:

  1. Gather and process data, which can then be used in your reports
  2. Produce bespoke visualisations for added power and flexibility.

To ensure compatibility for bespoke visualisations, you can import Python’s visualisation packages into Tableau, as Python is not a native Tableau language. While this process requires sufficient technical expertise and experience, it’s important to remember that Tableau can still be used ‘out of the box’ without needing coding experience (see point #3).

#6 Handle large amounts of data

Tableau is able to handle large data sets, processing millions of rows of data with ease. At the same time, there is no impact on performance when it comes to large data sets. Your Tableau dashboard will continue to offer interactive data visualisation, real-time insights, and more — without you having to worry about lag.

That said, Tableau isn’t used only by large enterprises that collect vast amounts of data; it’s a data visualisation tool for businesses of all sizes. Even if you have relatively little data to work with, Tableau will help you understand what’s happening in your business and make better decisions.

#7 Mobile support

In today’s world of remote work and flexibility, being able to access critical information via our mobile phones is critical. With mobile apps for iOS and Android, Tableau allows you to access data insights on the go.

What’s more, Tableau allows you to customise your dashboard for the device you’re working on — whether that’s your phone, tablet, or laptop. Tableau automatically recognises the type of device you are using to view a report and makes adjustments to its scale, optimising the viewing experience. As a result, you can view beautiful reports and data visualisations wherever you are.

#8 Mapping geodata

One of Tableau’s most interesting features is its geodata mapping, which enables it to produce geodata visualisations. Via instant geocoding, Tableau is able to use location data to create interactive maps, complete with built-in demographic data sets such as population, region name, income, etc.

Geodata mapping adds another dimension to traditional data visualisations, allowing you to see the ‘where’ as well as the ‘what’ and the ‘why.’

#9 Low cost

A look at the advantages and disadvantages of Tableau wouldn’t be complete without discussing the cost. Despite its rich functionality and powerful dashboards, Tableau costs less than some high-profile competitors, like Qlik. So not only does Tableau save you time when it comes to the initial set-up, it could also save you money.

Unlock the benefits of Tableau

Tableau makes data visualisation and analysis easier for the average user than most competitor tools. That said, we’re still talking about data science and analytics, which can be complicated areas to grasp.

If you’ve decided to go with a tool like Tableau, you want to ensure that you get a good return on your investment. If you don’t have the right knowledge or expertise to get the most out of Tableau, you might not even know what you’re missing — impacting both your ROI and your long-term profits.

Furthermore, when it comes to data, mistakes can be extremely costly. After all, being data-driven only works if you understand how to unlock the power of that data in the first place.


Working with Jarmany’s experts

To get the most out of Tableau — and by extension your data — it helps to work with a trusted data partner. That’s where Jarmany’s consultants can help. We can help you unlock the potential of Tableau, empowering your business to become more efficient and data-driven.

Our team of data scientists and analysts are seasoned Tableau experts who work closely with businesses to customise and configure the platform to their specific needs. After all, no two businesses are the same — how you use Tableau will differ from how other companies use it.

If you’d like to learn more about how Jarmany could help your business maximise the potential of Tableau, get in touch today and talk to one of our experts.

1 Becoming a Data Driven Organisation

2 Market Share of Tableau Software

4 Ways You Can Use Data to Enhance Your ABM Strategy

Account based marketing is a strategic approach to B2B marketing, whereby a business pools it’s resources to target a specific set of customers. Campaigns are then personalised to these target customers and designed to establish communication, build relationships and ultimately win new business. 

Whilst ABM has been used by companies in various forms for a couple of decades, many businesses are still missing out on a fundamental element which can help enhance their strategy and drive success… data. 

Did you know that: 

  • 1/3 of businesses aren’t using data for better decision-making 
  • 57% of businesses do not have a single view of data on their top clients across marketing, sales and customer success (I.e a consolidated view of account performance, activity and engagement stats)
  • 32% are not using data to better make decisions across marketing, sales and customer success. 


Here are 4 ways you can (and should) be using data to help bolster your ABM approach. 

#1 Connect your First-Party Data

Before you can launch your ABM strategy, you need to have a good understanding of your current customer base so you can establish important information such as: 

  • Who your contacts are in each account 
  • How they currently interact with you 
  • How often they interact with you 
  • The type of content they are interacting with 

To get a holistic view of your current customers you need to be consolidating your first party data from numerous data sets like CRM, website visits, social engagement, advertising impressions, product usage, emails and meetings. 

Not only will this help you to understand your current relationship with existing customers, but it will also help to unlock insights into the type of content they are engaging with, so you can also deliver relevant content. This is hugely important as consumers are 80% more likely to buy from a company that offers them a personalised experience. 

Whilst it sounds easy, it involves consolidating this data from numerous sources, cleaning it so it’s accurate, in-date and relevant, and then analysing and visualising this data so you can derive valuable insights.  

If you’re in the early stages of mapping out your ABM strategy, getting a true picture of your current customers will help you to map out characteristics of your ideal customer profile (ICP) so you can pinpoint target customers that match your current business model.


#2 Augment Intelligence with Third-Party Data

Once you’ve collated your first-party data, it’s then important to augment this intelligence with third-party data. This includes researching and collating information on: 

  • Industry size 
  • Job titles of influencers and key decision makers in that organisation/business sector  
  • Technographics 
  • Intent data
  • Industry news that may be impacting their business and creating pain points you can help to solve 

It’s important to deliver content that is relevant and personalised to customers their business and their industry. 76% expect more personalised digital experience from companies, so using third party data will provide you with the macro insights to help you achieve this. 


#3 Use AI to Derive Insights from your First and Third-Party Data

Once you’ve collated your first and third-party data, you can then make sense of this through AI. Data and analytics techniques, such as predictive analytics and propensity modelling, can help you derive information that will guide your ABM strategy. Specifically, these models can help you: 

  • Identify and score buying patterns 
  • Establish other customers that are showing similar patterns 
  • Predict pipelines 

This information will then help you to pinpoint opportunities within your existing accounts and new target accounts.


#4 Use Data Modelling to Understand the True Impact of your ABM Campaign

The previous points have focused on how data can help you with the planning stages of ABM, however it’s important to note that data can also derive useful insights post-ABM campaign too. 

Cross-channel activation is a big part of any ABM campaign, but how do you know which marketing tactics are having the most impact and delivering the best ROI?  

Data and analytics techniques, such as marketing mix modelling (MMM), can help with this. Not only will MMM tell you which elements of your campaign are working best, it will guide you towards the channels that are providing the best ROI vs the ones that aren’t, so you can better invest your marketing budget in the right channels. 


Account Based Marketing is all about delivering a personalised customer first experience to those key customers that you’re targeting – data can help you achieve this, and more. Speak to a member of the team today to find out how Jarmany can support you on your ABM journey. 

How to Measure the Success of Your Marketing Strategy

In an ultra-competitive business environment, marketing remains a critical factor that can contribute significantly to the overall success of a business. That’s because an effective marketing strategy:

  • Builds brand awareness
  • Boosts conversion rates
  • Drives growth and revenue

Given the benefits of a successful strategy, it’s no surprise that demand for digital marketing solutions is growing. Last year, 63% of businesses increased their digital marketing budgets, while the industry is growing at an average rate of 9% per year.1 However, without a clear understanding of the impact of your marketing strategy and the metrics to look at, businesses have no way to measure success or improve processes. 

In this article, we’ll explain how the impact of marketing can be measured, helping businesses maximise return on investment (ROI) and boost their bottom line.

Measuring marketing success

So, how do businesses measure the impact of their marketing? First, it’s important to note that marketing is a catch-all term for a range of activities designed to generate leads, and boost sales. These activities include, but are not restricted to: 

  • Email marketing
  • Social media
  • Paid advertising

As such, there’s no single metric to define marketing success. Instead, businesses can use a range of marketing metrics to examine the impact of their strategy, including:

  1. Return on investment (ROI): ROI shows the direct correlation between marketing activities and sales. Businesses can measure ROI either on a per-campaign basis or holistically before using the results to assess what’s working and what isn’t. This makes it easier to set marketing budgets and justify marketing spend moving forward.
  2. Social metrics: Social media is a great way to engage existing customers and attract new ones, and also provides access to key performance metrics that allow you to understand the impact of marketing campaigns and content. Some platforms also allow you to build online communities where customers can discuss products or services, and get answers to product-related questions.
  3. Analytics reports: An analytics report should provide a detailed overview of how a campaign’s results measure up to its goals. One drawback, however, is that they are generated weeks or months after the campaign has finished, meaning they can’t improve outcomes in real-time. Despite this, they can still serve as valuable indicators of what and how to improve in future marketing campaigns.
  4. Cost per lead (CPL) and cost per win (CPW): CPL measures how much a business spends to generate a lead — calculated by dividing the cost of lead-generating activities by the number of leads acquired. CPW measures how much the business spends to generate a single sale. For example, if a marketing campaign costs £5000 to run and resulted in 100 sales, the CPW would be £50.

Broaden your understanding of your marketing performance

Data is playing an increasingly important role in measuring marketing impact. And the good news is that businesses have access to more data than ever before, with 2.5 quintillion bytes of data created each day.2 

But raw data is like a raw material — it needs to be transformed into something usable. For example, businesses need to carry out a series of processes to turn their raw data into actionable insights, including:

  • Data collection: Data is collected from multiple channels, platforms, and systems before it is consolidated in a single location.
  • Data cleansing: The data is cleaned, removing information that is inaccurate, duplicated, or irrelevant, ensuring that the data is high-quality.
  • Data visualisation: Using cutting-edge data visualisation tools, the resulting insights are presented in a clear, graphical, actionable way.
  • Data analysis: The data is then analysed to extract useful information that can inform decision-making.

So, let’s take a look at some of the data-driven techniques that can enhance your understanding of your business’s overall marketing performance.

Attribution modelling

In simple terms, an attribution model is a method used by digital marketing teams to identify how much each of their individual marketing channels has contributed to sales efforts.

There are a variety of attribution models that can be used to distribute the value of a conversation across each touchpoint differently. The six most commonly used are:

  • First interaction
  • Last interaction
  • Last non-direct click
  • Linear
  • Time-decay
  • Position-based

By implementing attribution modelling, marketers can take a holistic view of their efforts. That means measuring the marketing effectiveness of keywords, ads and landing pages, helping to identify which are driving the most value in the long term.

However, attribution models will be impacted by Google’s decisions to phase out third-party cookies in Chrome by 2024, making it difficult to identify, target and measure users and generate consistent user IDs across the funnel.3 Fortunately, businesses can prepare for this by turning their attention to first party cookies.

First party cookies offer another way for websites to collect relevant data. As a result, businesses can benefit from implementing tools that narrow down on first party data and better utilising first party data that they already have for the development of relevant consumer profiles that include age, location, and browsing history.

Product performance analysis

The practice of utilising data to analyse the performance of a specific product is known as product performance analysis. This approach looks at every aspect of the customer journey, including data related to marketing, sales and other metrics.

By undertaking product performance analysis, businesses are able to gain a better understanding of:

  • Which customer segments products appeal to most
  • The number of returning customers
  • Which product details or features are being used and which aren’t

By using customer data in this way, businesses can gain insights into how their marketing approach has impacted the type of people using their products — identifying common characteristics that allow them to build an understanding of their target audience.

Armed with these insights, marketing teams can build optimised campaigns that target the right people, making it easier to boost conversion rates and increase customer lifetime value.

In-depth user journey analysis

User journey analytics is the process of undertaking an in-depth analysis of customer behaviour across numerous touchpoints over a specific period of time. 

This is an approach that is gaining momentum as businesses recognise the value of customer journeys as a means of monitoring customer experience performance and identifying opportunities for improvement.

By implementing this analysis, marketers are able to gain a better understanding of how their current engagement with customers and prospects via their marketing channels is working. 

Furthermore, marketers can also use the insights they can obtain with journey analytics to optimise the way in which they engage with their users throughout the customer journey.

Start measuring success and improving outcomes

Data can unlock massive improvements in your marketing strategy by highlighting what’s working and what isn’t. However, many businesses don’t have the in-house expertise required. What’s more, attempting to become a data-driven business without the right skills and tools is likely to result in poor-quality data and inaccurate insights.

As a result, working with outside providers is becoming increasingly popular for businesses that want to enjoy the benefits of data-driven processes without the hassle, costs and risks of an in-house approach. That’s where Jarmany can help.

By partnering with us, you can access the expertise, skills, and tools required for an effective data strategy. You’ll be able to undertake marketing impact measurements, identify areas for improvement and optimise decision-making. We help our clients become more data-driven with: 

  • Data strategy: We can help you build comprehensive and successful data strategies
  • Data platforms: We take a tech-agnostic approach, tailoring our tools and technology to suit your needs
  • Data science: We build complex AI solutions that help you plan for today and predict tomorrow
  • Data people: Our team of data experts is on hand to help you realise your goals

To find out how Jarmany could help you unlock the power of data and measure the effectiveness of your marketing activities, get in touch with us today and talk to a member of the team.

1  165 Strategy-Changing Digital Marketing Statistics for 2022

2  30 Big Data Statistics Everybody’s Talking About

3 Google Delays Phasing Out Ad Cookies on Chrome Until 2024 

11 Benefits of Using Power BI for Data Analytics

Digital transformation has made it possible to capture data on virtually every process, strategy and touchpoint imaginable. As a result, businesses now have access to an unprecedented amount of data — by 2025 the amount of data generated each day is expected to reach 463 exabytes globally.1

But collecting this data is only part of the story. To drive successful outcomes, businesses need to turn raw data into actionable insights. Those that manage to do this successfully will have a huge competitive advantage over those that don’t.

That’s because data-driven businesses are more likely to:

  • Acquire new customers
  • Retain their existing customers
  • Remain profitable in the long term

There are numerous data analysis techniques businesses can use to unlock cutting-edge insights, but they all require access to the right tools. Cutting-edge data analytics platforms simplify and automate the complex processes involved in data collection, management, analysis, and visualisation.  

While there are various options on the market, including Tableau, Qlik Sense, and Looker, in this article, we’ll be focusing on one of the most popular and powerful platforms — Microsoft Power BI.

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Introducing Power BI

Built by Microsoft and released as a stand-alone product in July 2015, Power BI is a data analytics and visualisation platform that helps organisations manage their data with the help of a modern, easy-to-use dashboard. 

The ‘BI’ in its name refers to Business intelligence, a catch-all term used to describe the strategies, processes and technologies that organisations use to enable data-driven decision-making

Power BI allows businesses to bridge the gap between data and decision-making by transforming raw data from a range of different sources into powerful insights. These insights are then displayed in a clear, graphical, actionable way via the dashboard. 

Since it first hit the market, Power BI has been regarded as a leader in the business analytics and intelligence field, making it the go-to tool for the top organisations around the globe.

Now that we know what Power BI is, let’s break down the key benefits it can bring to your business.

#1 Ease of use

One of Power BI’s greatest benefits is its simplicity. All the complexity of data collection and analysis happens under the hood and users are presented with the final product — insights that are effectively visualised, easy to understand and relevant.

Through its simple, user-friendly interface, users can generate BI reports and dashboards from multiple data sources with minimal effort. This ease of use allows anyone within the organisation to benefit from Power BI and data analytics, not just data experts.

#2 Time-saving templates

Power BI allows users to easily create and use various reporting templates. These can then be saved and subsequently used by different teams and departments across an organisation. 

This helps to:

  • Streamline processes
  • Drastically reduce the time and effort associated with reporting
  • Create internal efficiencies

#3 Security-focused

When it comes to handling and processing data, security is a critical issue for any responsible business. Power BI provides robust and dependable security features that keep your data safe: 

  • Data is secured end-to-end with double encryption, allowing businesses to share analytics insights with confidence
  • The ability to identify patterns of suspicious behaviour with oversight capabilities
  • Strong permissions that reduce the risk of network intrusion
  • Sensitivity labels that ensure your data continues to be protected upon export
  • Built-in compliance measures at a country and industry level
  • Azure services eliminate data exposure to the public internet
  • Row level security (RLS) allows certain people to see certain data points, for example, a sales manager can only see sales related to their team
  • Object levels security (OLS) can show or hide items, such as sales targets, depending on a user’s role permissions

#4 Regular updates

Microsoft releases regular software updates for the platform, usually on a monthly basis.2 These updates help to ensure that Power BI desktop’s features and usability are always ahead of the curve. 

What’s more, Microsoft actively encourages and acts upon the feedback it receives from its users, with feature recommendations often the driving force behind new functionality. 

#5 Cost efficiency

If you’re an existing Microsoft Enterprise Agreement customer, Power BI is available at no additional cost to your subscription. If you’re not, the cost of Power BI varies depending on the plan you choose, either:

  • Pro
  • Premium Per User 
  • Premium Per Capacity.

With Power BI, you can say goodbye to upfront licensing fees, support services and technical experts. Easy to use and powered by citizen development, Power BI allows you to make your own data sets, reports and connectors for your specific needs — all of which can help highlight ways to reduce risk and long-term costs.

#6 Excel integrations

Being a Microsoft product, Power BI offers seamless integration with other Microsoft tools. One of the most useful integrations is with Excel, as this allows users to connect Excel reports, data models, and queries to Power BI dashboards. This enables the rapid gathering and sharing of Excel data in new ways. 

Furthermore, the data that users have already visualised in Power BI can be exported to an Excel spreadsheet seamlessly. This helps to facilitate easy and simple analysis by the end user. 

#7 Real-time analytics

As data collection is a continuous process, the best data analytics platforms are dynamic. Power BI offers real-time analytics, with insights updated at regular intervals depending on the license level you have. 

For example, with Power BI Pro, data can be refreshed up to eight times a day, while with Power BI Premium, it can be updated 48 times a day.

#8 Build personalised dashboards

Power BI is also highly customisable, allowing users to build dashboards — complete with intuitive visualisations — that fit their own unique requirements. What’s more, Power BI’s drag-and-drop functionality makes this process incredibly quick and easy. 

Users can also drill down into data visualisations, providing insights at a more granular level.

#9 Enhanced connectivity

One key aspect of any business intelligence platform is how it interacts with external data sources. Power BI allows you to connect to a wide range of sources and import important business data, such as:

  • Sales data recorded in your CRM
  • Financial data from your ERP

Power BI also connects to a host of third-party solutions and software, including: 

  • Google Analytics
  • Salesforce
  • Spark
  • Zendesk
  • Marketo

Power BI users can also connect data files — such as XML and JSON — and SQL server databases, allowing them to create new and compelling data sets from multiple sources. These data sets can then be used for analysis and reporting. 

#10 Q&A feature

Another key feature of Power BI is its natural language Q&A functionality, which allows users to type a data-related query into a search box and receive answers in real-time. For example, a user might enter the following: 

“Which of our products sold the most units in Q1 of this year?”

Power BI then queries the relevant data sets and returns intelligent, up-to-date answers. This feature also supports autocomplete, allowing it to guess the data sets and categories that users are interested in. 

#11 Cutting-edge visualisations

Last but by no means least, Power BI is renowned for presenting data insights in a clear, coherent, and actionable way. 

Data visualisation is more than just pretty charts. It’s the final piece of the business intelligence puzzle, allowing users to understand and act on data insights. Power BI does it in style with the following options:

  • Pie charts
  • Column charts
  • Stacked column charts
  • Tables
  • Lists
  • Area charts
  • Scatter charts
  • Geo maps
  • Line charts

Making Power BI work for you

Becoming more data-driven should be the goal of any forward-thinking business, and Power BI is one of the best solutions on the market to unlock the power of data. But without the necessary experience and expertise, businesses may find that they only scratch the surface of what Power BI solutions have to offer. 

Often, partnering with an external expert is the best, most cost-effective way to leverage tools like Power BI, ensuring that you get maximum ROI. 

At Jarmany, we’ve built a first-class team of Power BI consultants that can help your business harness the power of data effectively. Whether you are looking for a fully outsourced team or support for your in-house IT team, we can provide you with seamless expertise at a competitive cost.

If you’d like to know more about how Jarmany could help you maximise the value of Power BI to drive smarter decision-making across your company, contact us today.

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7 Best Practices for Managing Your Data

In today’s digital world, companies have access to a staggering amount of data. Those that leverage it to drive efficiency and growth have a significant advantage over those that don’t, as author and management consultant Geoffrey Moore pointed out:  

“Without big data analytics, companies are blind and deaf, wandering out onto the web like deer on a freeway.”1 

But to enjoy the benefits that data analytics brings, companies need to know how to collect, store, and utilise data in a secure, efficient, and cost-effective way. This makes data management of critical importance. 

In this article, we’ll explain how to manage data effectively, outlining seven data management best practices you should follow to build a strategy that gives you security, compliance, and results.

1. Outline your goals 

Collecting data is one thing, but knowing what to do with it is another thing entirely. Many businesses sit on a goldmine of data but fail to properly utilise it. Others retain data that they’ll never need, leaving their data management systems crowded and disorganised in the process. 

One of the most important steps you can take is to outline the goals you want to achieve through your data. This helps you to filter the important information from the surplus or irrelevant information. 

Here are some examples of wider business goals that can underpin your data strategy:

  • Make smarter, better-informed decisions
  • Boost efficiency by streamlining and/or automating processes
  • Understand customer behaviour and trends better
  • Build smarter, more effective marketing campaigns
  • Improve employee engagement and retention

When it comes to setting your goals, you need to consider where you want your business to go and what you want it to look like moving forward. This will make the process of outlining what you want to achieve much easier. 

Once you know what you want to achieve, that’s when you can start looking at and collecting the data that can help you realise those goals and drive long-term success. 

2. Focus on security

Alongside its huge potential benefits, data collection also comes with risks. Ineffective security practices and inadequate data governance increases the likelihood of data breaches, which can be disastrous for companies, resulting in:

  • Financial losses
  • The loss of sensitive information or intellectual property
  • Damage to reputation and trust that is hard to recover from
  • Legal action from affected customers

On top of this, cyberattacks are increasing in scope and scale all the time. Just last year, ransomware attacks surged by 105%, putting businesses under increased pressure and scrutiny to protect their data.2 

At the same time, phishing remains the most common attack vector, which emphasises the need for effective security processes, training, and education at all levels of the organisation. 

To keep up with the advanced nature of cyberattacks, businesses must adopt increasingly sophisticated systems to protect their data. One such example is a multilayered security system.

By deploying multilayered systems with distinct components to protect different assets, you can ensure that there’s no single point of failure when it comes to data security. Ultimately, the aim is to ensure that any given component is protected by more than one layer of security. When combined, these layers reduce the risk of a successful cyberattack or data breach.

3. Stay compliant

Compliance is another area where businesses can’t afford to slip up, with severe financial and reputational costs for those that fail to stay in line with data protection regulations.

Like security, the compliance landscape rarely stands still for long. Since GDPR came into force in 2018, businesses that operate in the EU and UK have had much more stringent rules to adhere to around data collection, retention, and privacy. 

Staying on top of the latest compliance requirements isn’t easy, especially as regulations continue to evolve. Businesses therefore need to ensure that they have access to the right tools and have proper processes in place. 

Likewise, putting the right people in charge of compliance and ensuring that everyone understands how to handle critical and sensitive data is key. That might mean hiring in-house experts, providing additional training to existing employees or working with outside consultants.

4. Ensure quality 

With an increasing amount of data being collected across an array of platforms and systems, businesses must focus on maintaining data quality. If left unchecked, good data can become contaminated with bad, outdated, or irrelevant data. 

This can have huge repercussions for businesses that rely on data-driven processes such as analytics and automation. Poor-quality data leads to inaccurate or irrelevant insights, which can lead to poor decision-making.

There are a number of steps that businesses can take in order to ensure the quality of their data and thereby access accurate and relevant insights. This includes:

  • Regularly checking data for accuracy and relevance across different data sources
  • Purging outdated, irrelevant, or duplicate data from data management software
  • Ensuring that all staff who have access to data are trained in data collection best practices
  • Implementing tracking and tagging for web analytics to better understand statistics, get a granular view of data and group product lines and families together

5. Make data accessible 

Striking the right balance between data accessibility and security is essential for any well-run business. It’s crucial that people who need access to data can use it without having to jump through hoops.

However, this shouldn’t come at the cost of security. It’s also vital to ensure that it’s impossible for someone to access company data without the right permission.

Remember, some roles require access to only specific data sets, while others require a global view of all data. Compare the needs of a chief data officer to a customer success agent, for example. 

The best way to strike this balance is to set up access permissions based on specific roles and their data needs, rather than applying generic permissions. Industry leading tools and platforms can help with this, providing the functionality you need to manage permissions and ensure access for people who need it.

6. Utilise the right tools 

The right tools and data management software is key to establishing effective data management practices. Yet with an abundance of options available, many businesses make the wrong choices for their needs, resulting in systems that are: 

  • Far too complex to understand or use effectively
  • Unsuitable for the size or type of business — an enterprise solution being used in a startup, for example
  • Weak on security, representing a single point of failure against cyberattacks and data breaches

Comprehensive platforms hide complex data management processes under intuitive, easy-to-understand interfaces. They automatically clean and enrich data to ensure accuracy and quality. And they turn raw data into clear, actionable insights, allowing you to make smarter business decisions that drive efficiency and growth.

In short, using the best platform to store and visualise your data is crucial to success, so it’s worth utilising leading solutions like:

7. Work with experts 

Data management is a high-stakes process. The cost and implications of getting it wrong are considerable, with large-scale data breaches and cyberattacks representing an existential threat to businesses. 

On top of this, the requirements for effective and safe data management processes are constantly shifting in line with technology, regulations, and the threat landscape. Likewise, the skills and expertise needed to implement effective data strategies are constantly changing too. 

As a result, many businesses have skills gaps that are expensive and time-consuming to close, making it particularly tricky to handle data management in-house. But thankfully, there’s another option. By partnering with a trusted data expert, you can:

  • Enjoy the benefits of effective data management with none of the hassle or risk
  • Access cutting-edge data tools and platforms
  • Build data strategies that are tailor-made to your business needs
  • Implement data processes that are bulletproof and effective
  • Free up your resources to focus on adding value to your organisation 

Get the data management support you need with Jarmany

Whilst there are a number of best practices businesses can deploy in order to effectively manage their data, these require significant skills and expertise to implement correctly. Given the costs of hiring, training and retaining in-house talent, partnering with data analytics agencies is an increasingly attractive proposition.

As a leading data management and analytics agency, at Jarmany we have the knowledge and tools required to build data strategies that drive long-term growth and success. We work with some of the biggest organisations in the world, setting up processes that keep their data safe, clean, and accessible, thereby helping them make efficient, data-driven decisions

On top of that, we are a certified ISO 27001:2013 company, which means we have to demonstrate the ability to efficiently handle confidential customer data through robustly managed processes and adhere to stringent compliance checks.

If you’d like to find out how Jarmany can help you build a data management strategy and utilise your data to thrive in an ever-changing business landscape, get in touch with us today.

How to Use Predictive Analytics in Marketing

In today’s world of digital transformation, data is an essential commodity that directly fuels growth and success. And with an abundance of data now available, businesses that harness the power of data analytics to make better decisions will have a competitive advantage over those that don’t. For example:

  • Data-driven businesses are 58% more likely to beat revenue goals1
  • Data-driven businesses are three times more likely to report a significant improvement in decision-making2 

There is a wide range of data analytics techniques businesses can deploy in order to improve efficiency, ensure growth and obtain a competitive advantage. Amongst them, predictive analytics is one of the most advanced and potentially advantageous. 

In this article, we’ll explore predictive analytics and how it works, focusing on one of its most powerful use cases – marketing.

What is predictive analytics?

While it may sound complex, predictive analytics is relatively simple. It’s about using current and historical data to accurately predict future events, outcomes, trends, and behaviour.

These predictions are generated using a combination of statistics, predictive modelling, artificial intelligence (AI), and machine learning (ML), which analyse patterns and trends in data to predict possible future outcomes.

As a result, predictive analytics is often deployed to help organisations navigate during terms of uncertainty. This includes difficult economic times where consumer confidence drops, as predictive analytics can help forecast demand and identify where investments need to be made.

How does predictive analytics work?

Predictive analytics requires a good deal of planning, input, and expertise to yield results. Let’s take a look at some of the steps businesses can take to turn raw data into actionable insights about the future.

#1 Understand your goals

Before we even talk about data, it’s important to set the foundation for what you want to achieve. Understanding the business goals or issues you want to work towards is a critical first step upon which to build. In marketing terms, for example, underlying goals are likely to include: 

  • Understanding how customer behaviour may change in the future
  • Boosting revenue through cross-selling, upselling, and optimised pricing
  • Launching smarter, more effective marketing campaigns

#2 Develop a plan to collect the right data

Once you know what you want to achieve, it’s time to start looking at the data you’ll need to realise those goals. In many cases, customer data can be spread across a wide range of systems and platforms, so understanding what you have and where is key. 

Remember, predictive analytics often requires extensive work with large data sets. That’s why it’s crucial to ensure that you have the ability to collect and analyse sufficient marketing data to accurately predict outcomes.

Businesses should also consider broadening their insights with third-party data. For example, performance data from third-party retailer sites and additional industry data can help you obtain a better understanding of what benchmarks should be, how competitors are performing and the current state of the industry. 

#3 Analyse the data you have collected

Once you have collected the data you need, it’s time to analyse it. In order to make accurate and relevant predictions that ensure well-informed decision-making processes moving forward, predictive analytics needs data that is:

  • Clean
  • Complete
  • In a suitable format

#4 Create a predictive model

Predictive modelling is a core function in the predictive analytics process. Data scientists build them using algorithms and statistical models and then ‘train’ them using subsets of data. Once they are proven to work effectively, they can be applied to full data sets to generate insights. 

Examples of predictive models include:

  • Decision trees
  • Neural networks
  • Linear regression
  • Time-series analysis
  • Cluster models

Building a predictive model is a complex process requiring a great deal of expertise. A defective or poorly trained model will generate inaccurate predictions, which could lead to disastrous outcomes. 

#5 Use data for actionable insights

This is where the magic happens. Once you’ve outlined your goals, ensured that your data is relevant and clean, and built a predictive model that works, it’s time to put it all into action. Now you can use your predictive insights to guide your decision-making and give you a competitive edge.

Predictive analytics in marketing

So what is predictive analytics in marketing? Put simply, it’s the process of using customer data to make predictions about the future that help marketing teams become more effective and intentional in their decision-making.

In an ultra-competitive world, predictive analytics helps businesses to decode past buying habits, enabling them to project future buying habits. Armed with this information, marketers can make smarter, better-informed decisions, allowing them to:

  • Preempt future demand, behaviour, and trends
  • Create targeted, well-informed marketing campaigns 
  • Maximise their resources 
  • Engage existing customers and acquire new ones
  • Outmanoeuvre the competition
  • Identify when and where to target customers

Predicting future trends and behaviour with a high level of certainty brings huge advantages for marketing teams. Without the power of data, decisions around what to market, how, and to whom are essentially best guesses. 

Uses of predictive analytics in marketing

Now we know what predictive analytics means within a marketing context, let’s take a look at some of the ways predictive analytics can be used in order to transform marketing operations and improve outcomes:

  • Analysing and forecasting seasonal behaviour: Customers have different needs and preferences at different times of the year. Having a clear, data-driven understanding of seasonal behaviour allows you to plan for spikes in demand, focus marketing efforts in the right areas, and maximise revenue. 
  • Targeting the most profitable products to customers: Not all sales are equal. Some products have a higher profit margin than others. With predictive analytics, you can boost revenue by targeting customers with the most profitable products and services, helping you to squeeze more out of each sale. 
  • Conduct ‘what if’ scenarios: What if demand for your product dries up? What if customer behaviour shifts significantly in the coming years? What if a new competitor takes a large chunk of your market share? What if you decide to target a completely new customer segment next quarter? Predictive analytics allows you to answer questions like this by exploring different future scenarios and their likelihood, helping you to create long-term plans that are flexible and effective. 
  • Developing more effective marketing strategies: By leveraging the power of predictive analytics, you’ll have a better understanding of what customers want and how your offering lines up. You’ll be able to understand different customer segments and the potential value they bring, while pinpointing opportunities for upselling and cross-selling. All of this will help you design and deliver marketing campaigns that are smarter and more effective. 
  • Prioritising key customers: Predictive analytics can give you a clear picture of your customer’s potential lifetime value, helping you focus your marketing efforts on the ones most likely to bring repeat sales over time.

The benefits of predictive analytics within your marketing team

The ability to understand and plan for future events and trends before they happen is a real game-changer. Predictive analytics offers numerous benefits across your business, and particularly in marketing, such as:

  • Enhanced performance due to data-driven decision-making 
  • Increased lead generation and conversion
  • Improved funnel efficiency
  • The ability to build long-term plans that are flexible and futureproof
  • Greater ROI on marketing campaigns
  • An improved customer experience 
  • A better understanding of your customers and what drives their behaviour

Get the support you need to implement predictive analytics

The benefits we’ve outlined in this article aren’t guaranteed. Predictive analytics is a serious endeavour that requires the right skills, expertise, and systems to get right. If you don’t already have the skills and resources to implement predictive analytics in-house, you have two options: 

  • Close the skills gap by hiring fresh talent or upskilling current staff — both of which are expensive and time-consuming processes
  • Work with outside professionals who can guide you towards your goals with maximum efficiency and minimum hassle

In our experience, it pays to go with the latter option. As one of the UK’s leading data analytics agencies, here at Jarmany we can provide the tools and expertise you need to implement a predictive analytics marketing strategy that yields outstanding results. 

We use Azure Machine Learning and Python to deliver rapid and accurate marketing analytics results that help some of the biggest companies in the world make smarter, better-informed decisions. If you’d like to join them in harnessing the power of predictive analytics, get in touch with us today.

A Guide to Product Performance Analysis

The business world has become increasingly competitive in recent years, and as a result, it has never been more difficult for businesses and their products to stand out. To survive in an unforgiving market, it’s crucial to have a competitive edge. 

One way to make smarter decisions and outperform competitors is to harness the power of your business’s data. Companies that leverage data analytics often make better decisions, helping them become more efficient, cost-effective, and strategic.

That’s why, according to one survey, 99% of firms have invested in data initiatives.1 However, that alone isn’t enough to ensure success. Businesses also need to understand and implement best practices when it comes to data and utilise effective tools. 

In this article, we’re going to focus on a data analytics technique that can help you drive long-term success through smarter product decisions — product performance analysis.

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What is product performance analysis?

As the name suggests, product performance analysis is the process of using data to analyse product performance and subsequently glean actionable insights. For example, you might learn:

  • Which customer segments your product appeals to most
  • The number of customers who return time and again
  • Which product details or features are being used and which aren’t
  • How you can streamline in-product processes

Product managers can then use these insights to guide their decision-making process, allowing them to optimise product performance, improve the user experience, and drive towards product-related KPIs.

Why is product performance analysis important?

In ultra-competitive markets, gaining insights into your product’s performance and the way users interact with it can give you a competitive advantage and help to ensure success in the long term.

On top of that, data can also provide insights into how products are performing on affiliate sites. Data analytics can help bridge this gap with web scraping technology that provides insights with regards to share of voice (SoV), the customer journey and how customers are interacting with these sites.

Without these and other data-driven insights, you are left with basic performance metrics like product sales or the number of new customers acquired. These metrics are useful as a fundamental indicator of the success of a product or service, but they don’t provide any context as to why a product is successful or otherwise.

Product performance analysis allows you to drill down into the factors behind a product’s success or failure, providing the following benefits:

  • Deep knowledge of how customers interact with your product: Understanding how your customers use digital products is critical to enhancing the user experience. With product performance analysis, product teams can see the processes customers follow to perform certain tasks – the buttons they click on and the pages they visit, for example. This allows them to pinpoint inefficiencies and make small adjustments that have a big impact on UX. 
  • Reduced user churn: Customers are the lifeblood of any business. And studies have shown that acquiring new clients is significantly more expensive that keeping existing customers, which is why businesses must do everything they can to reduce user churn.2 Improving their products is a great place to start. Product performance analysis gives you the insights you need to make products that are intuitive, beautiful, and relevant — products that understand user needs. If your customers are happy with your products, chances are they’ll stick around. 
  • A better understanding of who your ideal customer is: Using customer data, you can gain powerful insights into the type of people who are using your product. You can spot common characteristics that allow you to build an understanding of what your target customer looks like. With these insights, you can build products, features, and marketing campaigns that target the right people, allowing you to boost conversion rates and keep key customers happy.
  • Understanding of what drives user engagement: Seeing which product features your customers are using is one thing. Understanding the factors behind that usage is another altogether. With product analytics, you can gain valuable insights into the way users discover and use certain features. This allows you to fine-tune processes within your product, and design your customer onboarding in a way that maximises product usage. 

How does product performance analysis work?

Now we’ve discussed some of the benefits, let’s focus on how to conduct product performance analysis. Here are some key steps to understand before you implement them in your business.

Align data needs with business goals

This is crucial. Before you start thinking about results, it is critical that you understand and outline your business goals. This will help you define the specific metrics you need to track and the data sources you will use. 

Build a data tracking plan

One way to ensure that your analytics efforts are coherent and in line with your wider business goals is to build a data tracking plan. Essentially, a tracking plan helps you to:

  • Outline critical events and user actions you need to track in order to capture relevant data – examples include clicking buttons, making a purchase, loading a page, or performing a search
  • Determine the methods you’ll use to track this data
  • Ensure that your data tracking efforts are aligned with your wider business goals
  • Prevent key details from being missed
  • Keep everyone on the same page during data collection and analysis

Collect user actions

Once your plan is set up and you have determined the user actions you want to track, it’s time to implement the plan. This involves instrumenting your product to collect the relevant data. Make sure you understand which user actions relate to which key metrics. 

You can then analyse this data to gain actionable insights that help you make smarter decisions about the direction of your product and marketing operations. 

Ensure that you have the right skills and expertise 

Like any branch of data analytics, product performance analytics is an esoteric discipline that requires the right skills and expertise to be successful. Data analytics is also a fast-evolving discipline, making it difficult to keep up with the latest trends and techniques.

A poorly executed analysis will likely lead to inaccurate or irrelevant data points. This can lead to false assumptions, which in turn leads to poor decision-making. 

To reap the benefits of product performance analysis, you will need to make sure you have the right skills in-house. In many cases, this means hiring new staff or upskilling existing ones. 

Alternatively, you can outsource your analytics processes to an external partner, allowing you access to all the benefits of product performance analytics without having to make major internal changes. 

Use the right metrics and analysis techniques

There is a whole range of metrics and analysis techniques you can use when performing a product performance analysis. The approaches you choose will depend on your specific product and the business goals that are underpinning your efforts. Here are some examples:

  • Funnel analysis: This type of analysis maps out the processes users take on your website to achieve a certain goal, such as making a purchase or signing up for something. Tracking the user journey through different phases of the funnel allows you to spot pain points and optimise the UX. 
  • Trend analysis: Using current and historic data, trend analysis provides you with insights into customer behaviour and changes in demand. It also allows you to see the customer segments that your product resonates with most. Ultimately, these insights provide you with a stronger understanding of your customers, allowing you to take a more strategic approach to product development and customer retention.
  • Customer journey analysis: This process allows you to analyse the complete customer journey – from awareness through activation all the way to advocacy. At each stage of this journey, there are various touchpoints and actions that move customers on to the next. Customer journey analysis allows you to see which stages of this process are working and which aren’t, helping you to optimise the journey at all stages.
  • Cohort analysis: Cohort analysis takes your customer data and divides it into subsets – or cohorts – based on shared characteristics. For example, users could be segmented into cohorts based on their behaviour within a product, their sign-up date, or whether they make repeat purchases or not. These cohorts can then be further analysed, allowing you to understand the motivations behind different groups’ decisions and behaviour. This helps you to target key cohorts and take a more flexible, strategic approach to meeting customer needs.
  • Retention analysis: This process helps you to understand the percentage of customers that stick around, which is a vital metric for understanding product success. You can apply various timeframes to measure customer retention rates, depending on your needs.

Get the most out of product performance analysis

As we have already touched on, product performance analysis — and data analytics in general — is a complex process that requires a specific set of skills and expertise. Given the dynamic nature of this discipline, many companies have a skills gap that must be addressed if they are to successfully handle analytics in-house. 

Fortunately, there are ways businesses can overcome this and benefit from the power of data analytics. For example, partnering with a trusted third party that specialises in data analytics allows you to maximise the potential of techniques such as product performance analysis.

At Jarmany, that’s exactly what we offer our clients. As one of the UK’s leading analytics specialists, we have the expertise, skills, and tools you need to turn raw data into actionable insights. We also work with outside partners to provide additional insights around performance on third-party retailer sites, giving you a full picture of product performance.

By working with us you can gain all the benefits of a thorough product performance analysis with none of the risk or hassle. If that sounds interesting, contact us today to find out more.


What is Predictive Analytics? An Introductory Guide

The amount of data available to companies has exploded in recent years. Driven by the wide-scale digitisation of everyday processes, it’s now possible to gather information on an unprecedented scale — and this is a trend that looks set to continue.  

According to one survey:

  • The volume of data created, captured, and consumed worldwide in 2021 was approximately 79 zettabytes
  • By 2025, this number is predicted to grow to 181 zettabytes – a 129% increase1

Given the volumes of data they now have access to, businesses are searching for ways to leverage it in order to obtain a competitive advantage with well-informed, rapid decision-making that can ensure consistent and high-quality outcomes. That’s exactly what predictive analytics can deliver when implemented correctly. 

This article will offer an introduction to predictive analytics, examine how it works, and outline some of the benefits it can bring to your business.

Defining predictive analytics

Predictive analytics is the process driven by propensity modelling that looks to transform historical and current data into future insights. It uses a combination of artificial intelligence (AI) and machine learning (ML) techniques, which are able to identify patterns and trends in data.

When done right, predictive analytics offers a reliable, transparent, and accurate way to predict future events, trends, and outcomes — offering a competitive advantage to businesses that leverage it effectively. 

How does predictive analytics work?

Predictive analytics is a complex process that requires a good deal of planning and attention. Before you can think about results, it’s important to take a step back and understand what business goals you are looking to achieve. For example, your goal might be to:

  • Avoid future skills gaps
  • Understand how customer behaviour might change over time
  • Ensure the correct staffing levels to meet customer demand
  • Maximise revenue through optimal pricing
  • Drive operational efficiencies to ensure resources are distributed to the right place at the right time 

Whatever your goals may be, you’ll need to make sure you have the right data to model accurate predictions. Predictive analytics requires large data sets to generate clear, actionable insights. The cleaner, more accurate, and more complete the data, the more reliable those predictions will be. 

Once you have the right data, it is then fed into neural networks or statistical models designed to spot trends and predict or identify outcomes. For example, a predictive maintenance model assesses the chances of essential equipment breaking down, allowing you to foresee when replacements will be needed. 

Some other real-life examples of this process in action include: 

  • Airlines use predictive analytics to decide how many tickets to sell at a specific price for a flight
  • Hotels predict the future number of guests they can expect on any given night in order to adjust prices, maximise occupancy, and increase revenue
  • Marketers are able to determine cross-selling opportunities based on customer responses and purchases
  • Banks use it to determine a person’s credit score, affecting their eligibility for credit cards and loans
  • Insurance companies use it to understand the chances of a claim being made, allowing them to adjust premiums when pricing a policy

Predictive analytics best practices

Knowing how predictive analytics works and the outcomes you want to achieve is one thing, but it’s not enough to get the desired results. That requires skills, knowledge, and the implementation of processes that help drive success.

So, let’s take a look at some best practices that businesses can apply to maximise the effectiveness of predictive analytics, resulting in accurate and reliable outcomes that contribute toward a competitive advantage.

Ensure that your data sets are large and valid  

Predictive analytics relies on historical data to predict future outcomes. Over time, the machine-learning algorithms and models used to generate predictions fine-tune themselves, becoming increasingly more accurate. But the original data that you start with is all-important.

If you use data sets that are small, incomplete, or even invalid, the quality and accuracy of those predictions will be severely compromised. Furthermore, trusting predictions based on poor or incomplete data can have severe consequences for businesses, as decisions based on inaccurate data are likely to have adverse outcomes.

Before you embark on your predictive analytics journey, make sure that your data sets are large enough to work with. You can’t make accurate predictions based on a week’s worth of data. You should also ensure that the data sets you use are complete, clean, reliable, and valid. 

Identify and draw from the best data sources

Not all data is equal. In many cases, you’ll have data spread across multiple different sources, including a business’s various platforms and systems. As a result, some datasets will inevitably be better quality or more appropriate than others.

In many cases, predictive analytics models have to be dynamic, meaning they take account of new data in real-time, adjusting their models and predictions accordingly. By identifying the most suitable data streams, you can work to guarantee the best predictive outcomes. 

Present predictions clearly

High-quality data visualisation is an essential component of effective predictive analytics. That’s because the predictions and insights generated need to be communicated in a way that is:

  • Clear
  • Easy to understand
  • Actionable

This is essential for all stakeholders — whether it’s the decision-maker whose job is to turn insights into action or the executive who wants a clear understanding of ROI. Presenting the analytics results in a straightforward and visually pleasing way helps ensure stakeholder buy-in and facilitates seamless decision-making processes.

The benefits of predictive analytics for business

In the words of Angela Ahrendts, the former senior vice president of retail at Apple, “consumer data will be the biggest differentiator in the next two or three years. Whoever unlocks the reams of data and uses it strategically will win.”2

So, the message is clear — businesses looking to stand out from the competition and drive long-term success need to find ways of utilising their data effectively. As we have outlined above, one of the best ways of doing just that is with predictive analytics.

But what are the actual benefits of predictive analytics? And how do these translate into successful outcomes for businesses? Let’s take a closer look. 

Improve decision-making

In a world of constant and rapid change, human experience and intuition are less reliable than ever as the basis for decision-making. While they have a role to play, decision-making needs to be informed by other factors, including data. 

Predictive analytics leverages massive data sets to predict trends and events before they happen, taking into account the multitude of competing factors that influence outcomes. The resulting insights allow you to make smarter, better-informed decisions about the future. 

Predict demand

Without a reliable way to predict demand for a particular product or service, it can be difficult to know how to organise and manage supply chains. Too little of a product and you’ll lose customers and revenue. Too much and you’ll waste time and resources. 

Predictive analytics takes into account current and historical trends in customer behaviour to allow you to meet supply with demand in the most cost-effective and efficient way.

Optimise pricing

Pricing goods and services aren’t as simple as it seems. You need to maximise revenue while remaining competitive. Set prices too high and customers will look elsewhere. Set them too low and you’ll cut into your profit margin. 

Predictive analytics allows you to set prices at the optimum level at any given moment, helping you to squeeze the maximum amount of revenue from your offering.

Improve customer retention

Customers are the lifeblood of any business. But in an ever-changing world, how do you keep them happy and loyal to your brand? Predictive analytics ensures that your customers stick around for longer by predicting:

  • Churn before it happens, allowing you to re-engage at-risk customers
  • Customer lifetime value, allowing you to focus your efforts where it matters most
  • What customers are likely to buy next, allowing you to target them with marketing campaigns and cross-selling opportunities

Reduce risk

Predictive analytics provides you with powerful insights into the likelihood of future events and how trends and behaviour may change. This allows you to prepare for the future, mitigate risk, and adapt to change seamlessly. 

Gain a competitive advantage

Gaining an accurate picture of the future is a game-changer. Predictive analytics helps you to see around corners, allowing you to increase efficiency and productivity. Put simply, businesses that leverage the power of predictive analysis stand to gain a competitive advantage over those that don’t.

Getting started with predictive analytics

Predictive analytics is a serious business. To be able to trust your data-driven and data-informed decisions, you need to be able to trust the insights that guide them. But such insights are only as accurate or relevant as the data used to generate them. 

Remember, the consequences of getting predictive analytics wrong are significant. Using poor data or carrying out poor analytics processes can result in poor decision-making and negative outcomes that set your business back considerably.

While it is possible to tackle predictive analytics in-house, it requires time, money, and data science expertise to get it right. In many cases, you’ll need to hire data analysts or upskill your current staff in the field of data analysis, diverting resources away from critical business objectives.

This is why it pays to partner with third-party providers like Jarmany. As one of the UK’s leading data analytics companies, we help our clients get the most out of their data, allowing them to enjoy all the benefits of predictive analytics without hassle or risk. We use cutting-edge analytics tools to deliver forecasting solutions that provide operational insights for some of the biggest companies in the world. If you’d like to find out how we could do the same for your organisation, get in touch with our team today.