Harvard Business Review

Most industries are nowhere close to realizing the potential of analytics

| Article

Back in 2011, the McKinsey Global Institute published a report on the transformational potential of big data—and it would take a supercomputer to process all of the articles that have appeared since then urging companies to get on board before some digital disruptor renders them obsolete. And yet for all the hype, most industries have still not come close to realizing the full potential of data and analytics.

MGI’s latest research with McKinsey Analytics on the state of the big data revolution measures the progress various industries have made toward capturing the revenue and efficiency gains we envisioned five years ago. Spurred on by digital-native competitors, the retail sector has captured about 30% to 40% of the margin improvements and productivity growth we identified in 2011. Manufacturing has captured some 20% to 30% of the potential, while the public sector and health care make the worst showings, realizing only 10% to 20% of the value.

Lurking behind these numbers are glaring disparities in performance between a few firms at the cutting edge and the average company in any given industry. An examination of the telecom industry, for example, shows that the analytics leaders have posted three to five times higher returns on their big data investment than the typical telecom company. Lower returns cannot be simply chalked up to the fact that companies are not investing at scale. On the contrary, many executives have made big technology bets but are now wondering why those investments haven’t yielded the kind of payoff they expected. McKinsey recently surveyed more than 500 executives representing companies across the spectrum of industries, regions, and sizes, and 86% reported that their organizations were only somewhat effective at meeting the goals they set out for their data and analytics initiatives.

In many cases, the culprit is a gap between launching a few analytics experiments and embedding these insights into the operating model of the larger organization. Many companies invested in analytics systems without fully appreciating that turning data into real value requires a profound reshaping of their day-to-day workflow. Others are still lagging behind in terms of fully digitizing transactions and processes to generate and collect all the data that could be useful.

An effective transformation strategy starts with clearly articulating how data and analytics will be used to generate value and how the results will be measured. Once the strategic vision is in place, senior leadership, including the CEO, will need to champion it personally in order to overcome institutional resistance and break down silos between departments.

Another critical piece of the puzzle is acquiring the right capabilities. Data scientists continue to be in high demand, but companies are also scrambling to attract or develop “business translators” who can ask the data science team the right questions and apply the results to practical business problems. This role needs to combine data savvy with industry or functional expertise. While it may be possible to outsource analysis, the business translator role requires proprietary knowledge; some companies are therefore providing training to develop these capabilities from within.

Embracing data and analytics is not a tactic; it’s a transformation. Merely layering powerful technology systems on top of existing operations is not enough. Digital-native companies have an enormous advantage because gathering, analyzing, and acting on data is hard-wired into their DNA, while traditional companies have to do the harder work of overhauling entrenched systems, roles, and mindsets.

It takes time to execute these types of organizational changes—and the pace is wholly dependent on the actions taken by management. For companies that fully embrace this shift, investing in data and analytics can yield a higher rate of return than other recent technologies, surpassing even the computer investment cycle in the 1980s. Early adopters are posting faster growth in operating profits, which enables them to continue innovating and solidifying their advantages. In industries where analytics adoption has been slow, there is still an opportunity for first movers to gain a significant edge over competitors.

To keep up with the pace of change, incumbents need to consider a two-part strategy. In an environment of constant churn, they need to consider high-risk, high-reward moves of their own such as entering new markets or fundamentally changing their business models. At the same time, they have to apply analytics to improve the performance of their core operations. Organizations that pursue this strategy will be ready to gain a step on traditional competitors and thwart potential disruptors.

Those disruptors are right around the corner. Data and analytics are already shaking up multiple industries. Companies at the leading edge are beginning to deploy machine learning and deep learning, which can do everything from providing customer service and managing logistics to analyzing medical records. We are experiencing the initial tremors of what will soon be a tectonic shift. Given the size of the opportunities at stake and the very real risk of creative destruction, organizations will have to push through the growing pains and adapt to a more data-driven way of doing business.

This column originally appeared in Harvard Business Review.

Explore a career with us