The age of analytics

The potential for big data and analytics to create value in specific domains and revisiting them today shows uneven progress and a great deal of that value on the table. The greatest advances have occurred in location-based services and areas with competitors that are digital natives. In contrast, manufacturing, public sector, and healthcare have captured less potential value comparatively and new opportunities have arisen , further widening the gap between the leaders and laggards.

The first challenge is incorporating data and analytics into a core strategic vision. The next step is developing the right business processes and building capabilities, including both data infrastructure and talent. It is not enough simply to layer powerful technology systems on top of existing business operations. All these aspects of transformation need to come together to realize the full potential of data and analytics. The challenges incumbents face in pulling this off are precisely why much of the value highlighted is still unclaimed.

Data and analytics are already shaking up multiple industries, and the effects will only become more pronounced as adoption reaches critical mass—and as machines gain unprecedented capabilities to solve problems and understand language. Organizations that can harness these capabilities effectively will be able to create significant value and differentiate themselves, while others will find themselves increasingly at a disadvantage.

Much more than fancy data analytics : World chess champion Gary Kasparov claimed he was the first person to have lost his job to AI when IBM’s Deep Blue beat him at chess. But Deep Blue was not really an AI. Rather, it was a supercomputer with the capacity to compute more moves ahead (200 million positions/second) than a human. It would thus be more accurate to say that Kasparov lost his job to brute force and Moore’s law rather than AI. Artificial intelligence is much more than brute force; it refers to a system able to imitate human intelligence. To highlight the difference, consider the paradox “We know more than we can tell” from Michael Polanyi, a mathematician philosopher. Indeed, many of the tasks we perform rely on intuition and tacit knowledge that are difficult to explain. A typical example is the capacity to recognize one’s mother. Babies and animals can do it effortlessly, but it is difficult to verbalize how we do it. So, if we cannot explain it, how can we teach a computer to do it?

Throughout history, we have kept our technologies beneficial. These new technologies will be part of our daily lives, so the best strategy is to be proactive and learn how to control and manage them.