From Data to Decisions - Moving past the Hype II 04-09

From Data to Decisions - Moving past the Hype II

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Figure 3: More Data, but Insights Have Less Effect on Strategy

While access to useful data has increased, the ability to use insights to drive strategy continues to decline.


Respondents also noted several difficulties applying analytical insights — not using analytics to drive strategic decisions, uncertainty about how to apply analytics, and failure to act on insights. Over the years, access to useful data has continued to increase, but the ability to apply analytical insights to strategy has declined. As the volume and complexity of data grows at exponential rates, companies wrestle with how to turn the data into useful insights that can guide the business. (See “More Data, but Insights Have Less Effect on Strategy.”)

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Figure 4: Broken Links in the Information Value Chain

Organizations have made no progress in managing how they capture and integrate data or disseminate relevant insights to strategic decision makers.

This decrease in the percentage of organizations reporting a competitive advantage from analytics might suggest that analytics is losing its luster. After all, the original hype around analytics was that it helped organizations compete more effectively. In addition, now there is more data, better technology to capitalize on it, and increased focus on analytical skills. So what’s behind this dramatic drop-off?
One factor in this downward trend is the increase in adoption of analytics across the corporate landscape. As more companies develop analytic capabilities, it is becoming harder for some companies to gain an edge with analytics. “Analytics used to be a competitive advantage, but now it’s becoming table stakes,” says Steve Allan, head of analytics for Silicon Valley Bank. Surprisingly, many managers report that they are innovating with analytics at about the same rate as managers in previous surveys — suggesting that companies are using analytics to stay competitive, but are having difficulty pulling away from competitors.

Beyond the increased use of analytics among companies, there is no single source of the decline that might suggest a simple fix. Among companies not obtaining a competitive advantage from analytics, the reasons varied. (See “Difficulties Gaining an Edge With Analytics.”)

Optimism About the Potential of Analytics Remains Strong

Despite the cross-industry decline in competitive advantage from analytics and the many reasons for this trend, most managers remain optimistic about the possibilities with analytics and do not report a commensurate level of disillusionment or pessimism. Although a substantial group of managers describe senior management expectations around analytics as unrealistic and express ambivalence about the results of analytical efforts in their organizations,2 a majority of managers are optimistic about the business potential of analytics and anticipate that the appetite in their organizations for using analytics will increase significantly over the next few years. (See “Optimism Versus Pessimism About Analytics.”)

Aligning Expectations and Reality

More than one-third (38%) of respondents agree analytics hasn’t lived up to its hype, and 32% think management’s expectations are too high. “The hype has been around not only the amount of data but also around the idea that data can solve all your problems,” said Adam Leary, lead data scientist and senior director of the data team at CBS Interactive. “Vendors have come to us and said, ‘You can build all these dashboards, and now you have insights.’ Well, you don’t really have insights if the team doesn’t know what their own goals are, right? You can provide them with amazing outputs, but if you really don’t know how to make it part of your own goals, if you don’t have the level of sophistication to use it, you’re going to miss out. That’s a big organizational culture question. Analytics can help you a lot, even give you a competitive advantage, but only if you know how to use it.”

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Figure 5: Optimism Versus Pessimism About Analytics

Optimism about analytics is a stronger sentiment than pessimism about analytics.



This survey finding — optimism about the potential of analytics, combined with pessimism about executive expectations and current results from analytics — becomes more interesting when considering analytics maturity levels of companies represented in our survey. (See “Three Levels of Analytical Maturity” for details about the levels.) For the first time since we began using these maturity categories in 2012, the least mature


This is an interesting and surprising result: Many managers agree that analytical results have not lived up to the hype, yet a large proportion of managers remain optimistic about the potential of analytics and believe the use of analytics in their organization will increase significantly in the next few years. “I expected to be running into this Gartner curve,” said an executive at a large U.S.-based pharmaceutical company, referring to the visualization Gartner uses to show how and when technologies move from promise to practicality. “There was definitely hype around big data. But I can’t say we’ve had any disillusionment. I don’t think we ever made any promises.” (See “Aligning Expectations and Reality.”)


group, what we call the Analytically Challenged group, shows marked growth at the expense of the Analytical Practitioners group. (See “Increase in Analytically Challenged Organizations.”)


Since the Analytically Challenged group has the most difficulty deriving a competitive advantage from analytics, it would be reasonable to assume that managers in these organizations would be overwhelmingly pessimistic about the potential of analytics. However, this is not the case. True, optimism does increase with success. But more than half of this challenged group remains optimistic and expects the demand for analytics to increase in the next year.


“There’s huge appetite, not just for the analytics but for our consultative help in resolving their problems,” said Angela Galeziowski, vice president of sales, strategic insights, and planning for IHG, referring to business unit heads. “They’re saying, ‘Sit at the table with me and help me really dig into where and how I can do that.’ We haven’t gotten any pushback. In fact, there’s much more demand out there than I ever anticipated.”  

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Three Levels of Analytical Maturity

We classify organizational maturity based on a company’s ability to gain competitive advantage from analytics and its ability to use analytics to innovate. (See our report from 2014, “The Analytics Mandate,” for additional details.)

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Figure 6: Increase in Analytically Challenged Organizations

The Analytically Challenged group shows a marked increase at the expense of the Analytical Practitioners group.ii

Analytics Strategy Is a Key Component of Success

Having a strategy for analytics is critical to meeting (or even creating) demand for analytical insights. Among Analytically Challenged organizations, only 1 of 8 respondents says his or her company has a formal long-term strategy for analytics. A significant number — 1 in 4 — has no analytics plan at all. Companies that have pulled away from the pack, the Analytical Innovators, are five times more likely to have a formal strategy for analytics than the least mature group. (See “Planning to Succeed With Analytics.”)

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Figure 7: Planning to Succeed With Analytics

Analytical Innovators are much more likely to have an analytics strategy.


Formal analytics strategies tend to focus on at least three basic areas of activity: skills development, data management, and cultural norms for using data in decision making. Some of the most mature analytics organizations, along with those that are striving to use analytics more widely, forge a strong connection between their organizational strategy and a formal strategy for analytics. The following examples illustrate three different approaches to making this connection.

Bank of England

The Bank of England, Great Britain’s central bank, influences the multitrillion-dollar British economy through regulation and policy. With an expanded charter to regulate the financial industry, analytics excellence is included in the core of the Bank’s mission. Because of the Bank’s role as a regulator, it has gained access to new datasets, and begun integrating and analyzing macroeconomic and, for the first time, some microeconomic datasets. In the past three years, the Bank has hired a chief data officer, created a data lab, established an advanced analytics group and formed a bank-wide data community. Bank leaders have also enlisted the public in a crowd-sourcing effort to use new data sets to find solutions to intractable policy issues. These changes are a direct result of the Bank’s strategic emphasis on analytics.

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Figure 8: The Bank of England's Strategic Plan — One Bank, One Mission

Analytics plays a key role in the Bank of England's strategy and mission.

General Electric

In 2012, GE began creating a new digital business with a multibillion-dollar bet on the Industrial Internet, a platform to aggregate and analyze sensor data from industrial machines.

To achieve this digital orientation, the company has had to refashion several elements of its business model, including changing service delivery, transforming its sales force, and changing the way it prices equipment. It has created a huge software division to support a new cloud-based platform to host and analyze asset productivity data, develop new machine data applications, and bring together a community of customers and developers.

GE’s strategy for data and analytics has become tightly linked to its corporate strategy, a tremendous corporate shift for what was once a traditional manufacturing conglomerate.4 GE anticipates that its Industrial Internet offerings will be a significant source of profitability and growth for years to come. In September 2015, GE formally announced the creation of a new business division, GE Digital, which will combine its Silicon Valley software center, information technology, and industrial security operations.

Xiaomi

“Culture trumps data, I don’t care how good your model is. If you don’t understand the culture … you’re not going succeed with analytics and deliver success for the business.” Jim Sprigg, director of database marketing and analytics for InterContinental Hotels Group

Beijing-based Xiaomi is among the top phone makers in the world. With a market capitalization of $45 billion, the five-year-old company is known for its flash sales of high quality, limited-edition phones, viral marketing campaigns, and razor-thin margins. The importance of Xiaomi’s use of analytics was revealed in an interview we conducted with co-founder Bin Lin. “We are a data-mining company,” Lin said. “Our business model is based on the data we collect.” Analytics has proven crucial to managing the company’s supply chain, delivering data as a valuable service to apps makers, developing new products, launching in new markets, and protecting a loyal customer base from third parties trying to create a black market for Xiaomi products.

Northwestern Mutual

For technologists pushing for change, that change might not come soon enough. David Pahl, director of analytics for The Northwestern Mutual Life Insurance Company (NM) and a 21-year veteran of the organization, had been trying for a decade to get the company to pay more attention to the value of applying advanced analytics to various business problems. Despite creating several proofs of concept identifying new ways to generate value, he could not get any traction within the company to leverage more robust analytics. In February 2013, Pahl was presented with an opportunity to start an advanced analytic team at NM from a leader who felt the company was finally ready and would finally embrace it. Initially skeptical, Pahl ultimately agreed to give it a renewed push.

Today, Pahl runs a full-fledged enterprise solutions analytics division, which employs about 50 analytics professionals and offers data and analytics services to over 30 different business units in the organization (not including a separate analytics unit dedicated to corporate strategy). “People are really becoming advocates for our services and understanding the value that we can deliver,” says Pahl. “It's real now.”

Companies that are innovating with analytics — organizations such as the Bank of England, GE, and Xiaomi — recognize that they need to put in place a robust analytics culture, significant data management capabilities, and a strong talent base for developing analytics results. Our research in past years offers additional evidence that being effective in these areas is highly correlated with analytics success.

As with any effort to develop an organizational capability that touches culture, talent, and decision making, it can be easy to underestimate just how difficult this process can be. The level of resolve necessary to create a business advantage with analytics can be as much about financing a big data initiative over time as it is about leaders demonstrating that the role of data and analytics should have a more prominent role in decision making. This is especially true for companies that are less open to new ideas and have not demonstrated a willingness to adapt their business models to a changing market environment.

“One of the biggest challenges for established companies is the fact that how they think about these capabilities is at odds with how they manage and control their organizations,” says a senior vice president and director of decision analytics and research at a major financial institution. “We’re having a lot of conversations around how to manage these new teams and new capabilities.” He is now formulating a standard framework to address issues where most organizations lack clarity, including how work flows into the analytical group.

Creating an analytics strategy is necessary, but not sufficient. The next challenge is to execute that strategy.


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