Data Strategy — What It Is & Why You Need One to Succeed

Understand the fundamentals of what a data strategy is, why it’s important, and how to create one that’s effective and aligned with your business goals.

Key sections

What is a data strategy?

What does a data strategy do?

Your data strategy needs to be actionable

Why you need a data strategy

The six components of a data strategy

How to create a data strategy

Data asset management and your data strategy roadmap

What is a data strategy?

A data strategy is a coherent and actionable set of analyses, concepts, policies, goals, reasoning, and steps designed to unlock the value of your data so you can thrive in the high-stakes digital world.

What does a data strategy do?

Your data strategy should be geared towards helping your organisation use data to make smarter, faster, and all-around better decisions. To be effective, your strategy must clearly link data to the strategic objectives of the broader business and outline the steps needed to ensure your data can support those goals.

Your strategy needs to actionable

Have you ever reviewed a business strategy and thought “Yes, that sounds very nice, but what are we actually going to do?”

You’re probably nodding your head — because corporate strategies too often end up as vague lists of broad goals and lofty ideas instead of well-defined action plans. This is especially common as businesses try to navigate the world of digital transformation and figure out how to keep their competitive edge.

For example, almost everyone these days has a data strategy — or at least a vision they call a data strategy — but most companies struggle to treat data as an asset, exploit its value, and adopt a data-driven decision-making process. Too many data strategies look good on paper but don’t lead to meaningful change.

The problem isn’t having a data strategy — it’s that most organisations don’t understand what makes a good data strategy or how to create one that results in less talk and more action.

We help clients across industries build strong data strategies that lead to meaningful change and measurable value creation — and we’ve learned a lot from our experience. In this guide, we’ll share what we’ve learned about effective data strategies including why you definitely need one and how to build one the Anmut way.

Why you need a data strategy: unlocking business value from data

Our experts could talk for days about why you need a data strategy — it’s that important — but we’ll use the space we have here to focus on three key reasons.

1. Focus on what matters most

The volume of data is increasing exponentially at most companies — and so is the number of technology solutions promising to revolutionise the way you manage or analyse your data.

Without a data strategy, you can easily get distracted building dashboards for every data set or chasing shiny new software you don’t need or aren’t ready for. Even worse, you’re likely to ignore root causes and fundamental issues in favor of point solutions and quick fixes.

A good enterprise data strategy, however, gives you clear goals and concrete actions and helps you ruthlessly prioritise initiatives with the most value to your business. Armed with your strategy, you avoid distractions, stay on track, and focus on correcting the root causes of your problems.

2. Break out of a bad data cycle and reset for success

It’s easy to get trapped in a bad data cycle where you’re trying to accomplish new things (i.e. data-driven decisions) using old methods and getting nowhere except frustrated.

Common signs include spending loads of time and money on technology without observing any improvement and being overwhelmed with demands from the business. You may also spend more time discussing the accuracy of the data than the insight it provides and find it difficult to provide employees the access they need or the speed they demand. Low trust in data from the business, friction with the business over data strategy and value, and an inability to prove how valuable your information is are also telltale signs you’re stuck in a bad data cycle.

Sound familiar? To break the cycle, you need something radical to break the inertia and reset your data journey. A robust data strategy with business alignment, radically new ways of thinking about data, and a clear value proposition and action plan.

3. Better decisions lead to a competitive advantage

Done well, a data strategy is more than a nice presentation or a list of lofty values — it’s a real competitive advantage. How?

Your enterprise data strategy should be full of actions designed to help you use data to effectively analyse industry trends and internal performance, recognise what’s most important, and act decisively to take advantage of pivotal opportunities. Each action in your strategy should build on the next and steadily build your capacity to make better decisions faster and to use data-driven insights to launch smarter products and services.

As you execute your strategy, your data-enhanced insights and decisions will result in a growth in opportunities and ROI.

The six components of a data strategy

We’ve covered what a data strategy is, what it should accomplish, and why you need one, and now it’s time to dive a little deeper into the details. In this section, we’ll discuss the most important components of an enterprise data strategy so you understand what separates strategies that get results from those that lead you in circles.

1. A clear purpose & understanding of value

Your data strategy starts with a clear understanding of how valuable your data is as a whole and how it flows through your organisation to create value in different areas. You must understand and communicate the value of your data and offer a clear justification for why and where you want to invest time and resources to become a data-driven organisation.

2. Alignment with business strategy

This is an element most companies get wrong. If your data strategy is an IT project, it will fail. Conversely, if your data strategy is a collaborative effort between the business and IT or Chief Data Office with both parties fully invested and aligned, you’ll set out on the right path.

To ensure this happens, link the goals of your strategy directly to the strategic objectives of the broader business, and make sure everything you plan to do contributes directly to better business results and is communicated in the language of business.

To be effective, your strategy must clearly link data to the strategic objectives of the broader business and outline the steps needed to ensure your data can support those goals.

3. An assessment of gaps & opportunities

Every robust enterprise data strategy includes an honest assessment of the gaps in your company’s approach to data. It should also make clear the opportunities for improvement and the value associated with investing time and resources in pursuing those opportunities.

Honesty is critical here — don’t gloss over deficiencies or exaggerate opportunities. Take an objective look at how you’re doing things now vs. how you should be doing them to maximise the business value of your data. Prioritise the highest value opportunities, and pursue them first.

4. An action plan for everything

If something isn’t clearly specified in your strategy, it won’t get done. The scope of your enterprise data strategy should be broad, but not vague. Include a concrete plan of action for everything you need to do to progress towards data maturity.

This includes activities needed to build a strong data foundation such as data storage, data architecture, data governance, data quality, data condition, data collection, and data infrastructure as well as ongoing data management activities and how you expect employees to use data to make decisions. You should also outline how you will measure and report the success of your data initiatives.

5. Long-term commitment

Big change doesn’t happen overnight. Becoming data-driven is an audacious goal requiring fundamental changes in the way you treat data and make decisions.

Your data strategy should recognise and acknowledge this fact and emphasise that achieving data maturity is a journey. Your organisation must commit to sustained investment in data and to completing the actions outlined in your strategy.

6. Short-term wins

While becoming data-driven can be a long-term process, developing your strategy and demonstrating results shouldn’t be. If you take a year to build your strategy followed by another year to demonstrate results, your initiatives will lose momentum and your organisation will lose interest.

Instead, you should enlist expert help to build an effective strategy quickly and make sure it includes plans to achieve some high-value quick wins.

How to create a data strategy: A look at the Anmut way

Now that we’ve covered the components of a robust enterprise data strategy, we’ll show you how we incorporate all these elements and create strategies for our clients.

The steps below outline the three phases of our data strategy process and explain the specific steps we take at each stage.

Stage 1: Assessment: gathering information and evidence

In this stage, we diagnose the data problems you’re facing and work to understand your specific challenges and why it’s worth building a strategy to overcome them. We assess why you’re struggling with data inertia and uncover the fundamental problems holding your organisation back.

We rigorously measure the current state of data at your organisation through workshops, data capability assessments, and non-invasive surveys. This helps us gather the information and evidence we need to understand what’s required to break out of the data inertia you’re experiencing and make progress on the road to data maturity.

Within 5 weeks, you’ll have a clear understanding of your data pain points and opportunities to unlock value — all presented in a way that aligns with your business language.

Stage 2: Identifying opportunities

Stage 2 is where we refine the results from the previous stage to identify your most valuable opportunities. We start by performing a data valuation to understand how your data assets support your business activities and the value they create.

The data valuation helps us determine which data assets are most valuable and prioritise your data improvement opportunities. We also measure the fitness for purpose of your most valuable data and how well they’re supporting your business objectives. The results of this data condition assessment provide clarity on exactly what needs to be done to fix your data issues.

Stage 3: Data strategy roadmap

This, as they say, is where the rubber meets the road as we develop a feasible plan of action to address your data deficiencies and pursue opportunities to use your data to add business value.

We work with you to prioritise actions based on what will bring you the greatest return on investment and develop a coherent roadmap of both short-term actions and long-term systemic changes. The roadmap includes steps to manage people, policies, and procedures and to measure the value added by your data investments.

Data asset management: An essential part of your data strategy roadmap

Data is a significant asset. For many companies, it’s the most valuable asset, but most don’t manage it or expect the same value from it as they do from other financial or physical assets. As a result, they miss out on significant value, strategic advantages, and higher returns.

Today’s most successful companies, including the likes of Amazon and Tesla, recognise the value of data and treat it as a prized asset. We help our clients transition from treating data as a cost to embracing data asset management and unlocking the tremendous hidden value of their data.

Our experience has shown us that data asset management is an essential part of the success of every data strategy roadmap and the best way to guarantee high returns from your data investments.

Ready to get started building your strategy?

Contact us to learn more about how we can help you create alignment between your data strategy and your business strategy, so you can start unlocking more value from your data.