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TDWI Upside - Where Data Means Business

Great Analytics Requires Difficult Organizational Transformation

When it comes to getting the most out of their data, organizations are still leaving too much money on the table. How much? It depends.

When it comes to getting the most out of their (big) data, organizations are still leaving too much money on the table. How much? Now as ever, it depends.

According to the McKinsey Global Institute, the research arm of management consulting giant McKinsey & Company, organizations are getting less than one-third of the value they could be getting out of big data. That's the takeaway of The Age of Analytics: Competing in a Data-Driven World, a massive new report. There's a catch, however.

Are the Benchmarks Reasonable?

McKinsey's benchmark is a seminal 2011 report -- Big Data: The Next Frontier for Innovation, Competition, and Productivity -- that set the equivalent of value targets for companies in five different verticals. By investing in analytics technologies, organizations in manufacturing, retail, healthcare, location services, and the public sector could save beaucoup bucks, McKinsey argued.

For example, the 2011 report estimated that companies in the U.S. healthcare vertical could save as much as $300 billion annually by using big data analytics "creatively." Similarly, retailers could use big data analytics to improve their operating margins by up to 60 percent.

Five years on, most verticals have fallen far short of McKinsey's targets.

Even assuming companies really can meet these value targets -- a not-uncontroversial assumption, given other McKinsey estimates -- their "failure" to do so isn't at all surprising for reasons that McKinsey itself acknowledges.

For example, the worst offenders are in the manufacturing, public sector, and healthcare verticals; organizations in these spaces are getting approximately 30 percent of what they could be out of their data, according to McKinsey. Is there anything about these verticals that might prevent them from rapidly (i.e., in the space of half a decade) retrofitting their operations to collect and analyze the data to generate new analytical insights and (most important) put them into production? Yes, as a matter of fact there is.

"Incentive problems and regulatory issues pose additional barriers to adoption in the public sector and healthcare," the McKinsey report explains. It adds: "Progress in healthcare is impeded by a number of barriers -- e.g., less-than-compelling incentives, the complexity of process and organizational change, a lack of skills, difficulty sharing data -- that are unique to that vertical."

"Legacy" Companies Are Slow to Respond

It's even more basic than this, however.

One major reason companies have been slow to exploit the promise of big data analytics is that they're encumbered by existing structural, process, and policy baggage.

McKinsey contrasts these "legacy" or established organizations (disproportionately represented in manufacturing, healthcare, and, of course, the public sector) with "digital natives" -- companies in location services and retail whose business models basically depend on analytics.

Legacy companies must push for both technological and structural change. "Some [companies] have responded to competitive pressure by making large technology investments but have failed to make the organizational changes needed to make the most of them," the report says.

Elements of Organizational Transformation

The McKinsey report argues that an effective strategy for analytics transformation has several components.

The first step is asking questions: What do we want to do with data and analytics? How are we going to use it? How will analytics insights produce value? Most important, how do we measure that value? The second step is arguably the hardest: building out an enabling data architecture.

"Many incumbents struggle with switching from legacy data systems to a more nimble and flexible architecture to store and harness big data," the report says. Companies that are encumbered with legacy baggage "may also need to digitize their operations more fully in order to capture more data from their customer interactions, supply chains, equipment, and internal processes."

The third step -- the acquisition of analytics capabilities -- is also difficult. Because of the cost and complexity, organizations may opt to outsource their analytics to specialists.

The fourth and final step entails changing business processes to make use of analytics insights. This includes embedding analytics in the context of business processes so it's available as needed to decision makers and frontline employees.

"[D]igital native companies were built for analytics, [but] legacy companies have to do the hard work of overhauling or changing existing systems. Neglecting any of these elements can limit the potential value of analytics or even leave an organization vulnerable to being disrupted," the report says. "It may be a difficult transition, but some long-established names, including GE and Union Pacific, have managed to pull it off."

The McKinsey report discusses a slew of other interesting issues, from the analytics skills shortage to the ways in which analytics is changing the way companies compete. Read the full report here.

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