A data strategy is a plan that documents where and how data supports critical business objectives. It lists out the problem the organisation is facing with data or the opportunity that the firm can unlock if they get data right. It then lists guiding principles for overcoming the problem, and a sequence of coordinated steps that the firm will take to achieve its objective.
The volume, velocity and variety of data are increasing in every organisation. Data leaders are able to exploit the data their firm has to achieve a competitive advantage. Data laggards must keep pace, otherwise, their customers will start to desert them.
A data strategy diagnoses whether your firm is a leader or a laggard, and directly links together the corporate ambitions with the data you have today. It sets out a plan to improve the data, or to acquire new data that will allow your business peers to achieve their goals.
Cognopia’s 2021 research into data maturity showed that firms with a good data strategy outperform peers with a bad data strategy by 1.46 points which could add up to another $58,400,000 in Market Capitalisation to an organisation worth a billion dollars.
A data-driven strategy reflects the desire of an organisation to leverage data in critical business decisions. Companies that pursue a data-driven strategy are aiming to make better decisions and to put data at the heart of everything they do. This is different from a data strategy, as it simply states the organisation wants to make decisions using data. A data strategy, on the other hand, determines how to leverage data to achieve business goals and supports the business strategy,
A data-driven strategy is a statement of intent. A data strategy is a document that describes how you’re going to achieve your goals.
I like to rely on Richard Rumelt’s definition of good strategy, from his book “Good Strategy, Bad Strategy”. According to Richard, a good strategy requires:
- A diagnosis of the problem the strategy solves, or a definition of the opportunity they wish to achieve
- Guiding Principles – what is the overarching theme behind our data strategy? In the eventuality that there is a decision we have not planned for, what principles should our team use to solve it?
- A sequence of coordinated actions – a step-by-step roadmap that lays out how we will leverage our strengths to overcome our weaknesses and threats in the pursuit of solving the problem or unlocking the opportunity
A good data strategy has all 3 of these things and can be used to solve the problem at hand.
Richard Rumelt also defines “bad strategy”. It’s the flip side of creating a good data strategy. The signs you have a bad data strategy are:
- You have not diagnosed the problem. If your data strategy is a list of things you are going to do, but it does not explain why you’re doing them, then you have started in the wrong place. Make sure you are solving a real business problem with your data strategy, not just documenting a list of things you would like to do.
- You have set unachievable goals. The leaders in data strategy are able to monetise their data. Not only are their teams working efficiently, but they can identify opportunities to sell the information they have. You might set a goal of monetising your own data – perhaps you want to double your revenues by exploiting your data assets. But if the data is currently in a bad state, undocumented, unmanaged, and of low quality, you have set an unachievable goal. Set a proximate goal to improve that data first, then you can create more ambitious goals later.
- You use “fluff words”. If your data strategy is full of high-minded, strategic sounding phrases, you may be guilty of this. Examples include “we’ll deliver an unparalleled customer experience using digitisation, innovation and personalisation” then you’ve written some fluff. State what you will do in plain English that everyone can understand.
- Your strategy lacks an execution plan. Unless someone can read the document and know what they must do, when they must do it, and why they are doing it, the strategy is useless.
Data analytics takes a large amount of data and represents it in a way that makes it easier for a human to understand. This might be a dashboard or report that you provide to senior management. Being able to analyse your data and present it in useful formats is essential to supporting the data strategy.
Many teams start at the analytics stage. They produce lots of reports and dashboards, but this is not a data strategy. Start by defining the business goals. Then work out the questions that must be answered in order to achieve those goals. Lastly, work out the analytics you need to answer those questions.
When drafting a data strategy it’s important to establish your data literacy today. What are your decision-makers able to understand? There’s no point providing analytics unless these teams can access it and make better decisions on the back of it.
The first step is to identify the problem. What is your business trying to achieve? Where does data play its part? Is your data able to support this ambition, or do you need to do something to fix it?
Once you have identified the problem, the next step is to analyse your strengths and weaknesses. How can you leverage your strengths to achieve the goal? What impact might your weaknesses have, and do you need to mitigate that first?
Lastly, you need to decide on a sequence of actions. The reason we create a strategy is because there are constraints. We do not have limitless time, money, people or other key resources. As such the data strategy must prioritise the things you are going to do, and sequence them in a logical order to make it a reality.
At Cognopia, we find that visual canvases are the best approach to documenting a data strategy. People process visual information more quickly and thoroughly than text. So document the key activities using canvasses.
Our approach begins with the business model. Leveraging Strategyzer’s Business Model Canvas, we document the key components of a business that are consuming resources and generating revenues. Once we have this we can identify the areas where data is likely to have a big impact.
The next step is to define the key business processes where data is put to use. What are the critical steps, and where are the data touchpoints? What are the expectations at each step, and what is the reality today?
We document the outcome of the data strategy in a CDD canvas. This clearly establishes the objective, with measurable goals so we know that we have achieved it. It also documents the levers, or actions we might take to achieve the objective. We list out the External elements that will potentially impact our ability to reach our objectives, and then we build together the links in the middle that show how each action might influence the end result, both positively and negatively.
Yes. Please take a look at this canvas and apply it to your own business.
The core components are a definition of the problem, the principles you’ll follow to solve the problem, and a sequence of coordinated actions that take you from where you are today to where you need to be tomorrow.
When crafting an executive summary, you have to keep it brief.
- Clearly and succinctly describe the problem or objective the strategy is going to solve
- Document the guiding principles you’re going to use to get you there
- Take the overall CDD canvas that your team produced, and refine it into a simple summary of the key steps that you are going to take to achieve your objective.
- Make a clear ask for the resources and support you need to achieve your goal and the timeframe it will take for you to achieve it.
- Check out the Data Strategy One Pager to get some ideas
Guiding principles need to be fit for your organisation and culture. There’s no point in taking a CTRL+C/CTRL+V approach to defining them, as your business is unique.
It is most important to solve a real problem with a data strategy. Do not create one that solves only your need. Ensure you are creating something that adds real value to your organisation today. If you struggle with defining a problem, I recommend the short, flippant book “Are your lights on?” to help.
The main questions you need to ask are:
- How is this data strategy aligned with the business strategy?
- What are the key decisions that our executives need to make to progress their business strategy?
- What is the current level of data maturity in our organisation?
- What is the current level of data quality in our firm?
- How competent are our people in interpreting and using data?
- What is the culture we have today, and how does that impact the data strategy?
- What are the first use-cases we will solve with our data strategy and why?
If you can create tight business alignment, clear use cases and a dollar impact for achieving your goals your strategy will be a winner.
Yes. Our Data Strategy One Pager allows you to capture the critical activities and links between the actions you’re going to take and the measurable outcomes your strategy is going to deliver.
Data strategy defines how you are going to use enterprise data to support the enterprise business strategy. Data Governance is the exercise of executive authority over business data. Think of it as the “rules” for how data is created, stored, updated, used, and deleted or archived.
If you have identified that your data is not high-quality today and that it is not fit to solve the problem, then you’ll need to create a plan to embed data governance into your business before you go for more advanced data use cases.
Big data refers to the high volume, variety and velocity of data today. Typically organisations are sat on a huge mound of data that they could unlock value from. It is too large for a human to understand, so we need to apply different techniques to unlock the insights within it.
You should not aim to create a separate big data strategy. If you discover a problem or opportunity where big data can play a role, add it to the overall data strategy. Do not chase this dream “just because others are doing it”. That’s a road to wasted money and ruin.
Small data represents datasets that a human can understand. Typically this might be a few hundred rows of data at the maximum. Small data is of critical importance. Your staff have a lot of small data in their heads. This represents what they know about a customer, or about a process in your business today. By capturing the small data from these staff, you expose it to others.
Running a CDD workshop is an effective way to extract this insight from your people. We also recommend using Strategyzer’s “Testing Business Ideas” as a mechanism to document assumptions and run tests to validate those assumptions. This might involve a survey for a dozen customers, which adds real insight into their experience with you, but is a small data set.
This is the sequence of coordinated actions that we state are a must-have for a good data strategy. To create a roadmap you should run a CDD workshop, which will bring together all stakeholders that will be involved in implementing the strategy. Each can add their own insight into which lever you need to pull first, and what the expected outcome of that level might be. You may also uncover a need to run small experiments before you begin – to prove that you’re heading in the right direction – or experiments “mid-flight” that confirm you’re heading towards your objective as planned.
A data strategy framework defines the key elements a data strategy must-have, namely:
- A diagnosis of the problem or a statement of the opportunity
- Guiding principles that govern how you’ll get there
- A sequence of coordinated actions to achieve your ambitions
Take a look at the Data Strategy One Pager to apply this.
Digital transformation is just the adoption of technology to solve a business problem. Data strategy governs the data that sits inside this technology, but it should also oversee the choice of technology too.
If you have a data strategy that does not include an assessment of the technology your data is housed upon, the interoperability of those systems, and the roadmap to change that technology then you run the risk of the business buying a new toy that adversely impacts the strategy you’ve laid out.
Digital strategy is about deploying technology to solve a problem. Data strategy looks at the data – the records housed within the technology – and how you can use that to achieve a goal.
We call this “ambidextrous data strategy”.
- Offensive data strategy seeks to use our data for competitive gain. We are going to monetise that data, or leverage it to create better relationships with our customers.
- Defensive data strategy exists to solve problems. Perhaps our data quality today is bad, and that’s causing us to send incorrect invoices to our customers. Defensive data strategy will prioritise the fixing of problems or the avoidance of risk.
Ambidextrous data strategy recognises that you can and should do both. Solving a problem won’t lead to customer delight – no one is going to jump for joy if you send them an accurate invoice, it’s just what they expect. However, as you approach fixing a process or problem that’s broken you should also seek out areas to delight – offensive strategy – that will allow you to create joy and increase the delight in your customers.
No. The data strategy is just a representation of the way you will leverage your data assets to achieve and unlock a business ambition. A business strategy that does not factor in your data maturity or the depth of data assets is missing out on a massive opportunity. Make sure you are at the table when the next strategic review comes around.
Before you run a data strategy workshop, you should focus on defining the problem or opportunity. Map out the requirements that key stakeholders have for data, and measure whether it’s able to support those requirements today. Gather the constraints – what rules must you follow when solving this problem – and document those clearly.
With this in mind, run a data strategy CDD workshop with the following agenda:
- What is the decision we are trying to reach?
- What are the goals we have? What does a good outcome look like?
- How can we measure these goals and objectives? When will we know we have succeeded?
- What are the possible actions we could take to achieve these goals?
- What are the “Externals”? What should we be aware of that’s outside our control?
- How do the actions link to the objectives? What are the feedback loops we need to be aware of?
Once you have documented this, you can then tidy the diagram up and share the meeting notes for everyone to confirm this is the right approach to your data strategy.
I would recommend that you read general strategy books. Richard Rumelt’s “Good Strategy, Bad Strategy” is the best book I have read on the subject.
The Cognopia Academy has a course “How to Create Ironclad Data Strategy your Execs Can’t Ignore”. Check it out here.
The usual suspects are at play – McKinsey, Bain, and the Big 4. However, a lot of good work is delivered by smaller, independent consulting houses like Cognopia. Smaller firms have a more bespoke, personal engagement and they are able to customise their delivery more effectively than the big shops, where templates and cookie-cutters are more common.
Yes! One of the best case studies I’ve seen on the use of data came from a stay at the Hyatt. I was on a hotel quarantine (thanks, Covid), and experienced first-hand how a company that cares for its customers, has a clear understanding of the problem they’re solving and has identified some key steps to executive it can achieve great things.
Check it out here: The smallest details can have the biggest impact
Still have questions? Want to learn more about Data Strategy, Data Quality, Data Maturity, or the bigger picture of Data Governance? Not sure if your company is even ready for Data Governance in the first place? Cognopia is here to help.
With free Data Maturity assessments and a range of bespoke consulting services, Cognopia’s team of Data Governance experts will help you craft a bulletproof Data Strategy unique to your organisation’s needs. Drop us a note below and find out what Cognopia can do for you.