Looking in the wrong place

In recent years, the United States House of Representatives has been characterised more by polemical debate than by sober deliberation. Yet as the curtain comes down on the Obama administration and Donald Trump prepares to rearrange the deck, these words from former Republican congressman, George Nethercutt, give us pause to reflect. 

“If I can't effect some change in six years, maybe I'm in the wrong place.”

Mr Nethercutt's words evoke a sense of resignation, of futility even, the feeling that it’s been “one damn thing after another.” The former congressman could easily have been speaking about data analytics programs in organisations, programs which too often fail to live up to their promise. At first glance, this is rather odd since nowadays, data analytics receives more airtime in executive boardrooms than ever before. One would think that everybody, from the CEO to the fresh faced intern, “gets” it. Wrong.

So why do data analytics programs sometimes fall flat? Many of the companies we talk to are just starting out on their analytics journey. Their main focus is on using basic reporting to describe what happened. It’s rather like driving a car by only looking in the rear view mirror. That approach kind of works if the road ahead of you is clear. However and with customers increasingly taking up cheaper and more relevant digital offerings, the road ahead is not only cluttered, but fraught with danger. That’s when we get the call. On arrival, we often find three deep seated misconceptions that we painstakingly try to get the organisation to unlearn. 

Analytics is only about data

 What many organisations fail to grasp is that the context around an analytics solution (e.g. culture, skill levels, remuneration policy, operating model and so on) can be just as important as the analytics solution itself. A red hot lead that emerges from the depths of a deep learning algorithm won’t get acted on if a sales agent is rewarded for something entirely different. 

Analytics = IT

Only a heretic would believe that IT and systems aren’t important. They are. But the impact of data and analytics doesn’t lie in expending inordinate amounts of money, time and effort on IT. The impact lies in clearly articulating the business decision to be taken and the corresponding actions and outcomes that get you there. You generally get results much more quickly that way.

Dirty data = Useless data

Well, how clean do you need your data to be? Most real world data sets are “dirty”, with missing fields and other assorted rubbish. However, that’s not an excuse to do nothing. There’s sometimes enough information hidden away in a data set, dirty as it may be, that allows the business to take a decision. That has to be better than doing nothing.

Have some sympathy for George Nethercutt.  It’s very easy to look for solutions in the wrong place. However with the stakes so high these days, organisations really need to take a more thoughtful and far-sighted approach to data and analytics.