10 Insights: A First Look at The New Intelligent Enterprise Survey

Last May, at the MIT Sloan CIO Symposium main-stage discussion on “Emerging Stronger from the Downturn,” one panelist listened with a growing private smile as his fellow speakers described example after example of how technology-driven information and analytics applications were transforming their companies. The stories were of data and analysis being used to understand customers, parse trends, distribute decision making, manage risk; they foretold of organizations being reinvented and management practice being rethought. They told of change, basically. A lot of it. Driven by ever-emerging technology and the new things it could do.

That was the point at which the panelist, a multinational industrial COO, turned to the audience and unofficially summarized, “So, the lesson: If you don’t like change, you’re going to like irrelevance even less.”

He’s right. Change is here. Failure to adapt means irrelevance. Time and progress march on, but at a Moore’s law pace instead of a clock’s.

However, the focus on exactly what’s changing can be misplaced. For all the swiftness with which technology is shifting — getting smarter, more powerful, more cognitively “human” — it’s sometimes true that the attention we pay to the next new technology is a distraction. It distracts us from the changes that organizations could make with no more new technology at all — the changes organizations could achieve just by capitalizing on how current technology can enable them to capture, analyze and act on information. (Though the “just” in that sentence may be ill-advised.)

MIT Sloan School’s Erik Brynjolfsson, director of the MIT Center for Digital Business, talked about that kind of change in an interview with SMR:

“Although most of what I’ve been talking about has focused on changes in the technology, I think the biggest changes are going to be in the way the companies use the technology. If some catastrophe happened and technology just froze for the next couple of decades, I believe the pace of organizational change would continue just as rapidly, because we have so much catching up to do. Specifically, I think this cultural mentality of using data more effectively, running experiments and responding to the environment and replicating it is something that is going to happen regardless of what additional advances we see in the underlying technology. A decade from now, I expect companies to be far more responsive, far more innovative, far more analytics-minded.”

Brynjolfsson gave experimentation special emphasis, but his observation fits other information-enabled practices found under the big tent of analytics. The technology is here. The data are available. How will companies use them to win?

To answer that question, SMR has teamed with the IBM Institute for Business Value to build a new innovation hub and research program called “The New Intelligent Enterprise.”

Through the SMR and IBM IBV collaboration, The New Intelligent Enterprise aims to help managers understand how they can capitalize on the ways that information and analytics are changing the competitive landscape. What threats and opportunities will companies face? What new business models, organizational approaches, competitive strategies, work processes and leadership methods will emerge? How will the best organizations reinvent themselves to use technology and analytics to achieve novel competitive advantage? How will they learn not only to be smarter, but to act smarter?

In the months ahead, this inquiry into the makeup of The New Intelligent Enterprise will consist of survey research, in-depth interviews with thought leaders and top corporate executives worldwide and the most relevant academic research and case study work in the field. This article presents (very) early returns on that research — especially on the first annual New Intelligent Enterprise Survey, a global survey of nearly 3,000 executives who told us about their top management goals, their uses (and misuses) of information and analytics as they attacked those goals and the management practices in play in their organizations. In both this article and “10 Data Points,” we call out some of what we’re learning. The articles have been coauthored by core members of The New Intelligent Enterprise team: Steve LaValle, IBM Global Strategy Leader for Business Analytics and Optimization; Nina Kruschwitz, SMR Special Projects Editor; Rebecca Shockley, IBM IBV Global Lead for Business Analytics and Optimization; and Fred Balboni, IBM Global Leader for Business Analytics and Optimization.

Please note: What’s here is only preliminary — a true “first look” at the themes, benchmarks and questions that are surfacing. Next on the schedule: conclusive analysis of the survey and stage-one interview findings will be published in a New Intelligent Enterprise Special Report on October 25. Selected interviews will be published online through early winter. And in late December, the Winter issue of SMR will include further exploration of the key ideas in October’s Special Report.

For now, though, consider the following notes — and the survey statistics in “10 Data Points” — as a collective reminder to reexamine your own practices and plans. As the gentleman said, If you don’t like change, you’re going to like irrelevance even less.

Here are 10 observations and questions about analytics-driven management that have popped out of research and interviews so far, and which we’ll be exploring more deeply in the major reports ahead.

1. The Big Picture: Make It New (Or, the Innovation Imperative)

What’s the challenge that information and analytics need most to help solve? Innovation, say New Intelligent Enterprise Survey respondents. They named “innovating to achieve competitive differentiation” their top business challenge (see “10 Data Points: Innovation Is the Top Business Challenge.”), significantly ahead of runners-up “growing revenue,” “reducing costs” and “acquiring customers.” Top performing companies put an even higher premium on innovation than lower performers did.

Innovation’s dominance as a strategic need may explain a lot about the current management emphasis on data exploitation in general and analytics in particular. Think of Thomas Malone on aggregating insight via collective intelligence, or Andrew McAfee on informing human intuition with the rigor and insight of machines. Or think again of Brynjolfsson and his observation about the underlying catalytic force of measurement alone, which is just one of technology’s innovation-enabling traits:

“What we’re going to see in the coming decade are companies whose whole culture is based on continuous improvement and experimentation — not just of specific processes, but of the entire way the company runs. I think this revolution can be fairly compared to the scientific revolution that happened centuries ago. Great revolutions in science have almost always been preceded by great revolutions in measurement. Management historically has not had that kind of careful measurement or experimentation.” Now it does. Or it could, at least.

Next question: What specific ways are companies inventing to aid and abet innovation with analytics?

2. Analytics = Performance?

Correlation is not causality, but results from The New Intelligent Enterprise Survey support anecdotal reporting and interviews by revealing a striking connection between organizations with high analytic sophistication and top performance, relative to industry peers. Top performers are three times more likely to be sophisticated exploiters of data and analytics than lower performers.

Thought leaders who were interviewed noted that productivity studies also offer support for viewing analytics as a key performance driver, showing that as organizations increase their leveraging of technology and information, they recognize disproportionate productivity gains.

Whether causal or just correlative, the link is strong enough to have bred one of several storylines being explored in ongoing survey analysis: the notion that one sure journey a company can take toward competitive outperformance of its peers is the journey from analytics “starter” to analytics “sophisticate.”

That leads to the next questions: What are the mileposts on that journey? What organizational characteristics need to be either nurtured or pruned in order for a business to advance along those steps? What processes are critical to install, and at what points?

3. You Can’t Graft Analytics on to Your Business Without Modifying Its Culture, Too

Turns out it’s wishful thinking to expect that inserting analytics into a business is like adding a room to a house — a move requiring no structural changes elsewhere in the operation. Culture (and talent, see below) is among the less well understood impediments to comprehensive analytics adoption. See “10 Data Points: Sophisticates Welcome Challenges to the Status Quo” for a glimpse of just one way in which advanced analytics users are different from the norm. Compared to many companies, they’re both paradoxically more distributed (information, decision making and experimentation all need to happen close to the ground) and more centralized (data has to be governed in order to be shared). Traditional 20th-century organizations find none of that easy.

Question: Which culture attributes need to be developed first?

4. Help Wanted (The Analytics Talent Challenge)

Survey results, expert interviewees and field commentators all suggest that there’s a gap between the size of the analytics opportunity and the amount of talent needed to seize it.

The gap is created by the peculiar requirements of the ideal analytics-driven managers. They have to combine expertise in statistics, experiment design and interpretation and analytics with fundamental business knowledge and acumen. These analysts need the ability to ask the right questions and pose the right hypotheses. They need to know how to get data to tell them the things that matter (and not the things that don’t).

Question: Given the shortage of such individuals, can organizations structure analytics management and execution so that individuals with some of the needed talents can work together to complement each other and constitute an effective whole?

5. Where in Organizations Is Analytics Done?

Three shorthand points:

• Both the survey and field research show that IT departments aren’t leading the analytics charge but are integral to its success.
• Back to the story of analytics-sophistication evolution: It appears that analytics typically take root at point of need when companies begin the sophistication journey. Point-of-need applications remain present in about the same quantity, but as companies evolve, they first grow additional analytics capabilities at the unit or line-of-business level and finally at the central enterprise level.
• Several interviewees have suggested that a dedicated executive role may emerge — a “Chief Analytics Officer.”

6. Where Are the Leaders Going?

The leaders — the top performers, the analytics sophisticates — are going further, as fast as they can. The survey reveals that despite already being ahead of the pack as data-driven organizations, they disproportionately feel pressure to capitalize on analytics even more; improving their information usage is a higher priority than for non-leaders; and they are more likely than less sophisticated businesses to look outside their walls for benchmarks and lessons.

A case of: The more you know, the more you know you want to know more. (Also, see #7 — Seeing Is Believing.)

7. Seeing Is Believing (and Understanding, and Using, and Collaborating)

One more place the leaders are going: toward methods of making information come alive for users throughout their organizations — methods of making information real. Data visualization and simulations and scenario development are just some of the ways companies are trying to turn information into an active, rather than a passive, engagement. (See “10 Data Points: What Matters Is Changing.”)

A research question: Which will work best?

8. Strategy: Plan Less, Experiment More

Almost all thought leaders — and many executives — cite the rise of experiments and the necessity for the analytics capabilities that underpin them. Practitioners use terms like “test and learn” and “sense and respond” to describe an approach that at its most rigorous includes a hypothesis and a control group, and at its least rigorous still demands an information sophistication not all companies have. Experiments can be large and organization-wide or tightly targeted and miniscule (which they more often are). Either way, they can powerfully supplant traditional methods of plotting a business’s course. Survey respondents may have had that supplanting in mind when placing “strategy and business development” significantly behind only “financial management and budgeting” as the business activity to which they apply analytics.

9. Analytics Plays No Favorites

There’s a conviction that the analytics-driven company does best in a consumer-facing game — or even better, in a born-digital, consumer-facing game (prototypically, Amazon). The survey results say otherwise, suggesting that organizations in any industry can and do use analytics as a competitive differentiator.

10. Experts vs. Executives — The “Ideal State” Assessment Gap

Among the questions asked of both thought leader interviewees and executive survey respondents was: “Imagine an organization transformed by better ways to collect, analyze and be prescriptively guided by information. How close are you to that ideal? Please rate on a scale of 1 to 10, where 1 = Not at all close, and 10 = Very close.”

This self-assessment question was one of the revealers of analytics “sophistication.” And the answers in aggregate, averaged, suggested how far the corporate world believes it has already come along the evolutionary path to being sophisticated about exploiting technology and data — and presumably to being rewarded with the competitive benefits that seem to attach. The average answer? About 4.5 on that scale of 10.

We asked the same question of interviewees — scholars and professional thought leaders who were asked to grade corporations as a whole — and got average answers 50% lower. Why? It could be testament to unduly high self-regard on the part of executives or unduly low critical assessments on the part of experts, but we think it’s something else. We think it’s about a sense of possibility — a differing notion between experts and executives of where the ceiling is. It’s not that the interviewed experts denigrate the corporate world’s progress to date; it’s that they know enough to envision just how far that progress might extend.

We think the experts are right.

[For more early returns from the first annual New Intelligent Enterprise Survey, including all 10 charts that most captured our attention, see the full story “10 Data Points: Information and Analytics at Work.”]