Big Data

Blog: Why small steps are the key to big data

Perhaps it’s inherent in the name, but it’s easy to feel daunted by ‘big data’. It can feel unwieldy and complex. You know that somewhere within its labyrinthine vaults hide the insights that can make a real difference, but how do you know where to find them? The answer lies in taking an agile approach to the way you treat what your data is telling you. Because when you break down your big data, it doesn’t feel quite so big after all.

Quality not quantity

Too often we seem to be intent on creating a kind of informational landfill – mountains of material gathered for no great purpose, left to grow exponentially so the likelihood of it ever proving useful becomes less and less.

Data shouldn’t be like this. It should be purpose driven – precise information gathered to solve an identified problem; not sweepingly general information gathered ‘just in case’. The quantity of the data is never as important as the insight it delivers and the impact it creates.

Focus on the questions your business needs answering and let them inform the data you gather. If you do this you will find your approach to big data starts to change from a technical problem to a business solution.

Finding real insight

The answers that lie within your data can’t be found using traditional analysis techniques. The volume of data is too great and the analysis tools too crude. Instead the information needs to be refined and segmented, creating manageable blocks of information from the unwieldy mass. Staged analyses then create layers of information, where the insight from each exercise is fed back into the one that follows it. This is agility, taking the feedback from every measurement to hone and refine your understanding.

It’s a continuous, iterative process, and done properly it takes time and resource. But the benefits can be far reaching:

  • Your questions answered: The insights you receive directly address the issues your business faces. This is data that makes a difference, not data for data’s sake.
  • Adding depth to your data: Agility ensures that the insights you receive have been tested and tested again, giving them a rigour and credibility you can rely on.
  • Detail: Big data doesn’t have to mean big overviews. With an agile approach you can uncover highly specific, nuanced insights relating to the smallest segments.
  • Control: An agile approach to big data keeps you in control of it, making it easier to manage the information you need when you need it.
  • Predictive power: Because when you have trust in and control over your data you can reliably use it to predict customer trends.

How has breaking down your big data built your understanding of your customers or helped solve your business issues? Tell us below.