Big data is now, more or less, an accepted fact of business, and all companies are collecting it to some degree. Collection is, however, the easy part. Analyzing it to leverage insights that could actually improve company performance is the entire point of collecting data, yet many firms lack staff with the appropriate skill set to do this. It’s like a football team spending millions on training facilities and then signing exclusively farm animals to play for them.
In an EY and Forbes Insights survey of 564 executives in large global enterprises, most respondents said they still lack an effective and aligned business strategy for competing in a digital, analytics enabled world. They also continue to struggle with change management issues in getting business users to adopt analytics insights. Meanwhile, according to a recent study by Forrester Research, most companies estimate they're analyzing a mere 12% of the data they have.
This inability to really embrace data analytics is often a case of not having the right people in place who understand data and where it can add value. Contrarily, many companies are also putting data on a pedestal and forgetting how important the human element is. Firms often now find themselves reliant on analytics software powered by algorithms that they have no understanding of, with machine learning algorithms and data visualization software to provide them with insights which they then blindly accept. Humans have limited capacity when it comes to making sense of large data sets, and computer driven algorithms are far better placed to deal with them, so they are largely left to their own devices and the conclusions they reach taken as gospel.
This has the potential to cause a number of problems further down the line. Buying some analytics software and treating data as the be-all and end-all of decision making is not ‘having a data strategy’, in the same way that buying a fast car doesn’t make you ‘a Formula 1 driver’. Data analytics needs people in place who have a real understanding of how the models work to establish which information is useful. They are also needed in cases that big data throws up that have an ethical and moral dimension data alone can’t address. Big data needs context and interpretation, and when it’s applied to scenarios such as who to give life-saving treatment to, there are often variables at play that only humans can understand. The logical conclusion of this was illustrated in the film ‘I, Robot’, in which a robot is forced to make a decision between saving the life of a young girl and the hero, played by Will Smith. The robot opts to save Smith because the data suggests he will be easiest to save, despite his protests.
A lack of human input around data is not necessarily all a company’s fault. There is a substantial difference between the number of data scientists available and the number needed by companies. McKinsey & Co estimated that there would be a shortfall of between 140,000 and 190,000 people with analytical expertise by 2018 in the United States alone, and between 1.5m managers and analysts with the skills to understand and make decisions based on the analysis of big data will be needed.
While it may be difficult to find people, it is still important that companies recognize the importance of human judgment in having the last word when it comes to decision making. The human element is needed throughout the chain, both in understanding when data analytics is needed, but also when it isn’t.