Advanced analytics provide counterargument to the “robots are taking over” narrative

As advanced analytics, machine learning and cognitive computing make rapid leaps in capability, it’s worth pausing for a moment to consider their ultimate role. Do these new technologies foreshadow the origins of Autofac, a 1955 tale by science fiction writer Philip K. Dick that portrays a future of uncontrollable self-replicating machines? Or does their emergence simply represent the next jump in human efficiency and productivity?

The evidence so far suggests the latter. For the past few years, enterprises dabbling with big data solutions have discovered a couple of important points: Analytics can make undiscovered connections between seemingly unrelated data sets that lead to profitable new insights or take a company rapidly along a path to nowhere.

Realizing advanced analytics ROI

As it turns out, the outcome a company experiences depends on people — specifically, the skill of its data scientists, business analysts and developers to identify the most relevant business issues to address, the best data to analyze and the right questions to ask. Even with the right people, results haven’t always measured up to expectations, leading some companies to question whether the returns are worth the investment.

But as Craig Guinn, a leader of the data and analytics practice for DXC Technology’s IBM Center of Excellence, points out in his paper “The Best of Times for Analytics,” that’s about to change.

Guinn says that companies have been slow to realize real gains from the growing volume of data they’re confronted with because analytical platforms are fragmented, tools are complex and skilled data scientists can be hard to find. Now, new tools are not only addressing the complexity issue, but they’re also making advanced analytics available to people who don’t hold a PhD in data science. Today’s analytics can be performed by well-trained business analysts, dubbed “citizen scientists.” In other words, advances in analytics are bringing them into broader control by humans, not less.

For example, solutions that read X-rays automatically screen out mammograms that are clear of abnormalities. That gives skilled medical professionals more time to analyze radiology images that are problematic. Automatic fraud detection in claims reporting systems helps insurers weed out bad claims, save money and spend more time helping customers with legitimate issues.

Machines can also see patterns in datasets that are simply too large for any one person to analyze, and the ability to do that can lead to important safety advances. Boeing’s next-generation 787 aircraft, for example, generates as much as 500 GB of data per flight. The ability to ingest and pore over that information gives a data-driven platform the ability to spot early signs of engine failure that no human could identify.

The concern that machines will replace human decision makers is legitimate, but there is also the counterargument that machines are simply evolving to elevate the role that humans can serve. Advanced analytics are creating new career paths for business analysts, application developers and others. And, even with advances in automation, job growth for data scientists is expected to remain robust.

That’s why, for the foreseeable future, it appears that these evolving forms of machine-driven decision making will continue to play a supporting role in the enterprise, helping companies —  and the people who run them — work smarter and faster.

But it’s worth keeping an eye on the machines. Just in case.

For more on the new era of corporate decision making based on hard data rather than intuition, see the DXC paper, “The Best of Times for Analytics.”