Why is it so Hard to Use Data Analytics? (And are Data Scientists Really as Elusive as Unicorns?)

A new report shows that “only 4% of businesses can extract full value from the information they hold.”  Yes, that’s a single-digit percentage of companies who have the capacity to take full advantage of the data and information at their fingertips.  Even for the big guys this is tough – only 15% of Fortune 500 companies use big data to gain a competitive advantage, as Forbes and Gartner report.  That leaves 85% of Fortune 500 companies NOT using big data analytics to drive their decision-making.

Companies and organizations are sitting on a gold-mine of data these days, given the ease of big and medium data collection.  Yet, establishing data analytics to guide decision-making can be daunting, especially for small- to medium-sized companies.

Why is engagement with analytics so low?  Why are data analytics so hard?

Here are my hypotheses:

1. Who can do this and where will we find them?  With all the hype about data scientists as unicorns, it’s hard to know who to hire for data analysis.  Good data analysts come wrapped in various packages – statisticians, IT whizzes, business analysts, etc.  My advice is to get some advice when hiring or retaining a consultant.  If data science knowledge is not within your organization, look externally for help in choosing your data analyst or consultant.
2. We’re stuck. For a lot of companies and organizations, data anything can sound intimidating. It can be hard to know where to start. Organizations can start slowly, dipping a toe in the analysis waters without a full-on dive in.  Start by assessing the data you do collect and already have – what’s in there that is useful and could guide decisions you already make?  Diving in will entail getting to predictive and prescriptive analytics with one’s data, but there’s no rush.  It’s ok to start slow.
3. Too expensive. While data scientists command high pay, you may not need a statistician / programmer / analyst / data story-teller all in one package.  Similar to starting small, you will likely derive meaning from your data with a data analyst, or even part-time consultant, who can advise on what your current data and information may reveal.  A smart analyst or consultant can also help you determine how much your range of analytics options will cost.  Keep in mind the ROI on data analytics is huge – an investment well worth making.

While comprehensive data science skills all in one person seem to be as elusive as unicorns, you don’t need a unicorn to get started down a data analytic path.