Businesses are surrounded by data. Every sale, invoice, customer query, late payment, abandoned cart, product return and service complaint leaves a trail. Together, these signals should help a business understand where value is being created, where it is being lost and where opportunities or risks are beginning to build. However, many businesses still struggle to turn all this data into information and further into commercial momentum. They can collect and measure more data than ever before, but still find themselves reacting late, second guessing the numbers or relying on instinct instead of data-based fact.
Part of the problem is that data has been oversold as an asset in itself. Businesses are constantly being told that data is the new oil, the new currency and the new competitive advantage. That language has encouraged many to focus mainly on data accumulation – collecting more, storing more and building more dashboards. But data sitting in a system can’t really do much. On its own, it won’t improve cash flow, strengthen margins or deepen customer relationships. It only becomes valuable when it is dealt with in a way that changes what the business can see and how quickly, and effectively, it can respond.
And that’s the heart of a data strategy. Instead of asking what can be done with the data already available, businesses should first ask where better information will have the biggest commercial impact. So, a strong data strategy starts with a business outcome, not a reporting requirement or a vague ambition to “use AI”. The aim might be to reduce late payments, increase repeat purchases, identify margin leakage, improve fulfilment or understand why customers leave. These are not data problems; they are commercial problems with data running through them. A good data strategy should therefore work backwards from the point of value. It needs to understand what the business is trying to protect, improve or unlock, and then figure out what information would make that possible.
The current excitement around AI makes this discipline more important than ever. AI is powerful, but it’s not a silver bullet. It can’t repair, run or enhance a process the business itself does not understand. It can’t produce useful answers from poor inputs. And it definitely can’t improve the customer experience if the underlying information is incomplete, outdated or disconnected from the way the business actually operates. As such, in many businesses of all shapes and sizes, AI has not replaced the need for a strong data strategy – it has exposed the weaknesses in the foundations needed to build one.
It’s also a strong reminder that the concept of garbage in, garbage out (GIGO) still applies. The difference now is that poor inputs can move faster, look more convincing and be harder to challenge. A sophisticated tool built on weak data will not create intelligence, but it can create the appearance of sophistication while leaving an underlying business problem unresolved. Businesses therefore need to resist the temptation to chase every new tool before they have done the less glamorous work of understanding what information they need, where it comes from and how best to use it.
A useful data strategy is ultimately about creating an information advantage. Not by turning every business into a data science company, but by helping people across the business make better decisions with information they can trust. Information that gives them to see what the strategy needs, act with confidence and reduce the noise around decision-making.
Achieving that requires that businesses stop treating their data strategy as something that must be created and owned by a data team. A data strategy is not a technical document that sits to one side of the business. It is a business strategy expressed through data. Many businesses miss this and define the data strategy outcome too narrowly. A better approach is to work backwards from the decision or outcome that that needs to improve. What is the business trying to change? Who makes the decision that influences that outcome? What information do they currently use? What are they missing? Which data would help them act sooner, with more confidence or with a clearer understanding of the risk?
Once those questions are clear, the next step is to get the data strategy basics right. That means ensuring the data is accurate, complete and relevant. It also means dealing with data in a way that preserves customer trust. And it means making sure the data is fresh and current enough to inform valuable insights, not build false confidence.
The world is overflowing with data, so your competitive advantage is not going to come through a strategy aimed at making your business data-rich. It’s achieved by making sure you are decision-ready.
