10 Jun 2013
Big data paralysis

Big data overload can paralyze decision-making.

In a recent article in Inc. magazine, Margaret Heffernan shares the cautionary tale of a small company that invested for the first time in database marketing, only to find themselves drowning in “big data.”  They were saddled with data paralysis and found themselves on a never-ending quest for that missing piece of information – “If only we knew ‘x’…”

In the elusive search for the right data to produce “the answer,” the company postponed critical decisions and put its performance at risk.

With more and more companies gaining access to greater and greater amounts of data, the risks of data paralysis are increasing. Remembering these three tips can help you avoid the data paralysis trap.

1. Data does not make decisions, people do.  

While new “big data” streams offer many potential benefits, including greater knowledge about customers and their behavior, it’s easy to fall into pursuing data for data’s sake. Data needs context to have meaning, and knowledgeable humans must give it that context.

2. There’s no magical endpoint where “big data” will finally yield “the answer.”  

The myth that data can create a world where all things are knowable is just that – a myth.  So don’t postpone decisions as you search for that point of perfect knowledge. Instead, strive for continuous improvement, adding the data and analysis that are logical next steps.  This iterative process builds new knowledge into decision-making over time.

3. Start with the end in mind.

While it’s increasingly possible to vacuum up volumes of data, and that analyzing this data can reveal interesting findings, having a plan at the outset can make it more likely that you’ll find insights to support your business decisions. This means having a framework for data that helps you to inventory and prioritize types of data you’re gathering and how you’ll use the data you have.

Such a framework includes:

  • Hypotheses about the decisions you’re making and the analysis you’re looking to perform in order to make them.
  • Descriptions of the data needed to feed the desired analysis, and their sources.
  • An understanding of the gaps – areas where you don’t currently have data – and how you plan to fill them and make decisions in the meantime.

The framework should show those areas where data is abundant and where it’s lacking, and enables you to set up strategies for acting even when you lack perfect information – which, in reality, is always.


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