Jarmany logo
6 Minute Read

How To Build A Data Strategy: The Framework To Success

We explain why a business without a well-defined data strategy could be missing out on a tonne of insights—and effectively holding back its growth.

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.

New call-to-action

Read more blogs like this:

Choosing The Right Data Analytics Agency: 5 Key Factors To Consider

In today’s digital landscape, data analytics has emerged as a vital tool for empowering businesses with actionable insights, enabling them to foster growth and maintain a competitive edge. However, the presence of a skills gap in the data and analytics industry is widely acknowledged, creating a significant challenge for companies seeking to establish and sustain their internal data capabilities.
Time icon
4 Minute Read

AI and Ecommerce – A Powerful Partnership For Growth

We highlight how AI can boost eCommerce performance in an increasingly competitive marketplace.
Time icon
5 Minute Read

The Frontier Model Forum; What Is It and How Will It Help Regulate AI?

The Frontier Model Forum (FMF) is a newly announced partnership aimed at promoting the responsible and safe development of AI models. In this blog we delve into what it means for the AI industry.
Time icon
3 Minute Read