Why Automation Won’t Displace Human Intelligence in Analytics

There’s great value for companies to use automation technologies in analytics, taking advantage of the vast data sets now available. Making machine learning models more precise isn’t just about technology; but a reimagination of business structure and the roles of tech and people.

Nearly every industry today is swimming in data, and the floodgates are not closing any time soon. Expert projections suggest a 4,300% increase in annual data production that will create 35 zettabytes by 2020.

As the acceleration of data analytics continues, more businesses are realizing the necessity for an efficiency of increased automation across their organizations. In fact, nearly three-quarters of business leaders and employees believe at least some part of their job could be automated. Yet, there’s also an ongoing debate around the linear computational ability of machines, which inherently lacks business logic.

A more effective approach is to instead amplify human intelligence through technology, ultimately to figure out in a machine learning world which business processes should be automated versus what can be automated. Either way, striking a proper balance between automation and human intelligence in data analytics is no easy task. While we may soon reach a point of “singularity” where computer processing power will completely match or even exceed brain “power,” we’re not there just yet, and let’s not confuse processing power with reasoning power.

A common mistake around automation is failing to recognize the necessary human element of the technology. While computers are certainly capable of extraordinary feats, human programming is still the core behind these outputs. Human intelligence is required to create any kind of automation right now, and the resulting automated processes are therefore not inherently intelligent themselves. For this reason, we cannot consider big data and analytics as true intelligence, despite appearances. Big data and analytics, as well as data mining, natural language processing, pattern recognition and more are all byproducts of models developed by humans. None of these tools, complex as the may be, can exist without at least some initial human involvement. That’s why all the surrounding business and political talk of automation and job displacement tends to be hyperbole.

Automation allows us to do our jobs better, and in fact, paves the way for new types of jobs too.