How to Make Data-Driven Decisions

Data-driven decision-making is a mantra you hear thrown around by startups, consultants and big business. But the approach most companies take is backward.

An Accenture survey found that only 32% of companies reported they gained tangible and measurable value from data. So what is happening? A major promise of advanced analytics is the ability to use metrics to make flawless decisions. So why do so many companies seem disenfranchised by big data?

There are a number of factors. Chief among them is a lack of buy-in at the C-level to support a data-driven culture. A lack of investment in technologies and the use of outdated data governance practices ultimately yields poor quality data that is perceived as unreliable.

This is not surprising. A half-hearted investment in data infrastructure should get poor results. But what about companies that consider themselves “data-driven” and still don’t find value in their analytics? It is most likely a flaw in their process that affects the outcome.

Focus on what needs to be solved.

Too many companies start with assessing their analytics capabilities and then shoehorning in a strategy around what they have. They try to extract value from readily available data or find a purpose for data they have on hand. 

This approach is backward and will inevitably serve up answers to the wrong questions or deliver misleading insights. 

A better approach is to focus on what decision needs to be made. Starting with the fundamental business problem will anchor your strategy around the problem rather than the means to find the solution.

If you know the question you are trying to answer it will help frame what data and analytics tools are needed to support the decision-making process — these capabilities may exist or may need to be developed.

So don’t aimlessly follow data.  Analytics and big data are wonderful tools, but ultimately the correct questions need to be asked at the beginning of the process for companies to find value in the results.

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