Do we Need Data Analytics for our Data Analytics?
An adage attributed to J. Paul Getty goes something like this: “If you owe the bank $100, that’s your problem. If you owe the bank $100 million, that’s the bank’s problem.”
This is the same kind of issue that many companies face with so-called “big data”. When the amount of information was constrained by a relatively small number of data points, the need to analyze it was minimal. Becoming master of your data was easy. But today, everything is measured, causing information overload for managers who just want to know what’s going on. It’s easy to get to the point where you don’t own your data – your data owns you.
Data analytics is a growing field for precisely this reason. It’s no longer enough to merely produce basic performance metrics – we now talk about flows of data, meta-analysis of trends within trends, even the geometry of data. The simplistic picture of how processes function has given way to a baroque system of dashboards, graphs, and targeting systems.
It’s gotten to the point where we often need data analytics to explain our data analytics. The larger and more complicated the “big data” we analyze, the harder it is to explain it to a lay person. This is the ultimate point of any data set – it points to a trend which ordinary people can appreciate and base their decisions on. The value of data analytics comes not just from pointing out that something is happening. It comes from the decisions which can be derived from that data – the perspective which gives companies a unique edge over the competition.
This begs the question: is less really more when it comes to data? Shouldn’t we be talking less about “big data” and more about “manageable data”? Of course, no company can really tell which data sets are valuable in the absence of a comprehensive outlook. But it is possible that a true “big data” approach might only be needed once, or even once in a while. Narrowing down the scope of data analytics seems counterintuitive, but in the end it may pay off in the form of conclusions which are directly relevant and easily understood. It’s like the difference between brainstorming in the classical sense and actually making a decision – at a certain point, some of the original possibilities will become irrelevant and unnecessary, however prominent they may seem at first.
Data analytics companies like to talk about “hidden data” – that piece of information that you may fall through the cracks, or may not be immediately obvious. Yet more often than not, data remains hidden for a reason: it doesn’t actually contribute any insight at all. It’s like that “one weird trick” which promises to get rid of all that belly fat. If the solution was so obvious all along, it probably would have been discovered by now.
There’s plenty of science behind data analytics, but its true value comes from a more artistic sensibility. Presenting conclusions based on data is no longer enough. Those conclusions have to point to an action or strategy. The data can’t just be factual. It has to be convincing. This is the true value of data analytics – that it gives companies the tools to move forward and reach their true potential.
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