KPMG’s 2018 CEO Outlook suggests that CEOs have an ambivalent relationship with data. Technology itself isn’t really the issue. In fact, 95 percent of the leaders we surveyed see technological disruption more as an opportunity than as a threat. They also reported that they have trust in predictive analytics and historical data. However, these same leaders said that they don’t plan to increase their use of predictive analytics models or unstructured data in the near future. More to the point, 78 percent of US CEOs said that they have overlooked data-driven insights in favor of their intuition at some point over the last three years.
So why do business leaders trust the numbers ─ but only so far ─ when it comes to making complex decisions?
I think the answer has to do with the fact that we can only prove data analytics applications over time. With simulation models of revenue growth, we have to keep adjusting variables over months and even years to see if our assumptions are correct. In the meantime, we have to rely to a large degree on our instincts and what we have found to work in the past.
However, many problems today are so complex and present so many variables that technology is the only way to find the answers we need. One example involves scheduling optimization for large organizations such as sports leagues, hospitals, universities and transit authorities. I’ve had the opportunity to collaborated with colleagues across the globe and witnessed the value these algorithms can provide to the decision making process. The algorithms have been designed to assist in reducing costs, improving efficiencies, and optimizing service delivery ─ all while ensuring that a broader range of objectives and challenges are integrated and evaluated as part of the process.
I believe that it’s not really a matter of humans versus machines but rather humans and machines working together. This means that we’ll have to swallow our pride a bit and rely more on this technology in the years ahead.
With GBS, CEOs can use analytic technology the way it was intended ─ not as a substitute for decision making but as a tool for decision makers. Leaders will still go by instincts but only along with technology ─ and maybe technology more than instincts as applications mature.
In fact, analytic modeling and multi-disciplinary decision making with GBS will be even more important as business partnering, alliances and collaboration becomes increasingly complex. For example, we see incubators today where startups use crowd sourcing to attract workers on an ad hoc basis to solve multiple problems. In this sort of complex, rapidly shifting ecosystem, GBS supports the ability to fully utilize resources in a collaborative, flexible way while still maintaining governance over many stakeholders and multiple activities concurrently.
As another benefit, GBS allows CEOs to monitor and improve their modeling applications to better identify the business value of their data. In our business realization group, my colleagues track data usage and measure the return on assets through GBS governance structures. This in turn helps drive more efficient modeling and data usage.
In the future, business leaders will most likely find themselves using technology more often as applications mature and business environments increase in complexity. The key is to adopt new technologies in line with proper governance in line with business strategies.