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.

Want to become more data-driven? Download our ebook today to find out how.

#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 Ipsos Jarmany can support you on your data and digital journey in 2023 then please contact us today. 

Data-driven decision-making, made easy with Ipsos Jarmany.


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.

Find out how to optimise your website to maximise sales potential

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 Ipsos 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 Ipsos Jarmany could help you unlock the power of Qlik, get in touch today and talk to one of our experts. 

Data-driven decision-making, made easy with Ipsos Jarmany