Advanced analytics: Nine insights from the C-suite

Conversations with hundreds of business leaders reveal nine ways that they are—and are not—adapting to the analytics revolution.

Data and advanced analytics have arrived. The volume of available data is growing exponentially, with more added every day from billions of phones, sensors, payment systems, and cameras. Machine learning is becoming ubiquitous, but organizations are struggling to turn data into value.

The stakes are high. Those who advance furthest, fastest will have a significant competitive advantage; those who fall behind risk becoming irrelevant. Analytics cannot be the sole province of the chief information officer (CIO), as is sometimes the case. The CIO may not understand the business as a whole well enough to spot opportunities and threats, or be influential enough to ensure that the company addresses them appropriately. While the expertise the CIO brings is of course essential, business-unit leaders and CEOs must be in charge of analytics to accelerate the pace of change and to ensure intelligent investment. This is beginning to happen: McKinsey has found that more than 50 percent of CEOs consider themselves the primary leader of the analytics agenda, and that figure has been growing steadily.

With this in mind, we spoke to more than 300 top executives of major companies. Here we offer nine insights based on these conversations, and suggest actions for business leaders to take.

“Where do we want to be in five years as a result of advanced analytics? What are the implications to our business model, culture, portfolio mix, and value proposition?” CEOs all over the world are asking these questions—for good reason. Analytics has the potential to upend the prevailing business models in many industries, and CEOs are struggling to understand how. The need is urgent.

Beyond reorienting the existing business models, analytics leaders are also learning how to create and capitalize on new opportunities. Organizations are moving from hoarding data to sharing it. Some are pooling data as part of industry consortia, increasing their comprehensiveness and therefore their value. Product-based organizations are adding data and analytics to their offerings as value-added services. Some have gone further, charging for the analytics-enabled service rather than directly selling the product. For example, some jet-engine manufacturers now sell flight hours instead of the engines; this is only possible because sensors provide the data that help them understand usage and required maintenance.

There are two areas to explore. First, to understand how analytics can disrupt existing business models, set aside the time to focus on the long term. What can be learned from other industries that are farther along? What customer needs can be better met through new business models?

Second, to capture new opportunities, start with the data, analyzing what they are worth, how distinct they are, who would find them valuable, and how they can be combined with other sources to increase their value. Then, think through the business model. A simple way to get started is to conduct a market scan of the data and analytics players, as well as a competitor scan to understand what others may be doing. Identify where and how to play within this ecosystem.

Analytics create value when big data and advanced algorithms are applied to business problems to yield a solution that is measurably better than before. By identifying, sizing, prioritizing, and phasing all applicable use cases, businesses can create an analytics strategy that generates value. For example, a CEO of a global consumer-packaged-goods company told us that the application of advanced analytics and machine learning to business functions such as revenue-growth management and supply-chain optimization uncovered as much as $4 billion in benefits.

Few executives, however, have such a detailed view of value across their business units and functions.