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2010
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Executive Summary

To understand better how all organizations are attempting to capitalize on information and apply analytics today and in the future, MIT Sloan Management Review in collaboration with the IBM Institute for Business Value conducted a study that included a survey of nearly 3,000 executive managers worldwide, as well as in-depth interviews with leading researchers.

Among the top-line survey findings:

Top performers view analytics as a differentiator: Top-performing companies are three times more likely than lower performers to be sophisticated users of analytics, and are two times more likely to say that their analytics use is a competitive differentiator.

The biggest obstacle is not the data: Despite the enormous challenge felt by most organizations to “get the data right,” that’s not what executives name as the key barrier to achieving the competitive advantage that “big data” can offer — the top two barriers are “lack of understanding of how to use analytics to improve the business” and “lack of management bandwidth.”

Where are the leaders headed? Toward making information “come alive”: Over the next 24 months, executives say they will focus on supplementing standard historical reporting of data with emerging approaches that convert information into scenarios and simulations that make insights easier to understand and to act on.

Based on data from our survey, case studies and interviews with experts, we have identified a new, five-point methodology for successfully implementing analytics-driven management and for rapidly creating value. This report describes that emerging methodology and its five critical recommendations.

  • Focus on the biggest opportunities first. Attack one big important problem that can demonstrate value and catalyze the organization toward action.
  • Start with questions, not data. Understand the problem — and the insights needed to solve it — before working on the data that will yield the insights.
  • Embed insights to drive action. Ensure end-result impact by making information come to life, articulating use cases and expressing data-driven insights in ways that even nonexperts can understand and act upon.
  • Keep existing capabilities while adding new ones. Even as centralized analytics oversight grows, keep distributed, localized capabilities in place.
  • Build the analytics foundation according to an information agenda. Opportunistic application of analytics can create value fast, but it must be part of an enterprise-wide information-and-analytics plan.
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Findings of The New Intelligent Enterprise Study

At organizations in every industry, in every part of the world, senior leaders wonder whether they are getting full value from the massive amounts of information they already have within their organizations. New technologies are collecting more data than ever before, yet many organizations are still looking for better ways to obtain value from their data and compete in the marketplace. Their questions about how best to achieve value persist.

Are competitors obtaining sharper, more timely insights? Are they able to regain market advantage neglected while focusing on expenses during the past two years? Are they correctly interpreting new signals from the global economy — and adequately assessing the impact on their customers and partners? Knowing what happened and why it happened are no longer adequate. Organizations need to know what is happening now, what is likely to happen next and what actions should be taken to get the optimal results.

To help organizations understand the opportunity provided by information and advanced analytics, MIT Sloan Management Review partnered with the IBM Institute for Business Value to conduct a survey of nearly 3,000 executives, managers and analysts working across more than 30 industries and 100 countries.

Among our key findings: Top-performing organizations use analytics five times more than lower performers. Overall, our survey found widespread belief that analytics offers value. Half of our respondents said that improvement of information and analytics was a top priority in their organizations. And more than one in five said they were under intense or significant pressure to adopt advanced information and analytics approaches. (see Figure 1.)

The source of the pressure is not hard to ascertain. Six out of 10 respondents cited innovating to achieve competitive differentiation as a top business challenge. The same percentage also agreed that their organization has more data than it can use effectively. Organizational leaders want analytics to exploit their growing data and computational power to get smart, and get ahead, in ways they never could before.

Senior executives now want businesses run on data-driven decisions. They want scenarios and simulations that provide immediate guidance on the best actions to take when disruptions occur — disruptions ranging from unexpected competitors or an earthquake in a supply zone to a customer signaling it may switch providers. Executives want to understand optimal solutions based on complex business parameters or new information, and they want to take action quickly.

These expectations can be met — but with a caveat. For analytics-driven insights to be consumed — that is, to trigger new actions across the organization — they must be closely linked to business strategy, easy for end users to understand and embedded into organizational processes in order to take action at the right time. That’s no small task. It requires painstaking focus on the way insights are infused into everything from manufacturing and new product development to credit approvals and call center interactions.

Top Performers Say Analytics Is a Differentiator

Our study clearly connects performance and the competitive value of analytics. We asked respondents to assess their organization’s competitive position. Those who selected “substantially outperform industry peers” were identified as top performers, while those who selected “somewhat or substantially underperforming industry peers” were grouped as lower performers.1

We found that organizations that strongly agreed that the use of business information and analytics differentiates them within their industry were twice as likely to be top performers as lower performers.

Top performers approach business operations differently from their peers. Specifically, they put analytics to use in the widest possible range of decisions, large and small. They were twice as likely to use analytics to guide future strategies and twice as likely to use insights to guide day-to-day operations. They make decisions based on rigorous analysis at more than double the rate of lower performers. The correlation between performance and analytics-driven management has important implications to organizations whether they are seeking growth, efficiency or competitive differentiation. (see Figure 2.)

Three Levels of Capabilities Emerged, Each with Distinct Opportunities

Organizations that know where they are in terms of analytics adoption are better prepared to turn challenges into opportunities. We segmented respondents based on how they rated their organization’s analytics prowess, specifically how thoroughly their organizations had been transformed by better uses of analytics and information. Three levels of analytics capability emerged — Aspirational, Experienced and Transformed — each with clear distinctions. (See Figure 3.)

Aspirational. These organizations are the farthest from achieving their desired analytical goals. Often they are focusing on efficiency or automation of existing processes, and searching for ways to cut costs. Aspirational organizations currently have few of the necessary building blocks — people, processes or tools — to collect, understand, incorporate or act on analytic insights.

Experienced. Having gained some analytic experience — often through successes with efficiencies at the Aspirational phase — these organizations are looking to go beyond cost management. Experienced organizations are developing better ways to effectively collect, incorporate and act on analytics so they can begin to optimize their organizations.

Transformed. These organizations have substantial experience using analytics across a broad range of functions. They use analytics as a competitive differentiator and are already adept at organizing people, processes and tools to optimize and differentiate. Transformed organizations are less focused on cutting costs than Aspirational and Experienced organizations, possibly having already automated their operations through effective use of insights. They are most focused on driving customer profitability and making targeted investments in niche analytics as they keep pushing the organizational envelope.

Transformed organizations were three times more likely than Aspirational organizations to indicate they substantially outperform their industry peers. This performance advantage illustrates the potential rewards of higher levels of analytics adoption.

While our findings showed that organizations tend to wait until they have gained some experience before they apply analytics to growth objectives, this may be more a common practice than a “best practice.” Our experience indicates that analytics, applied wisely to an organization’s operational capabilities, can be used to accelerate a broad range of business objectives, even at the earliest stages of analytics adoption.

Data Is Not the Biggest Obstacle

Despite popular opinion, getting the data right is not a top challenge organizations face when adopting analytics. Only about one out of five respondents in our survey cited concern with data quality or ineffective data governance as a primary obstacle. (see Figure 4.)

The adoption barriers organizations face most are related to management and culture rather than being related to data and technology. The leading obstacle to widespread analytics adoption is lack of understanding of how to use analytics to improve the business, according to almost four of 10 respondents. More than one in three cite lack of management bandwidth due to competing priorities. Organizations that use analytics to tackle their biggest challenges are able to overcome seemingly intractable cultural challenges and, at the same time, refine their data and governance approaches.

Information Must Become Easier to Understand and Act Upon

Executives want better ways to communicate complex insights so they can quickly absorb the meaning of the data and take action on it. Over the next two years, executives say they will focus on supplementing standard historical reporting with emerging approaches that make information come alive. These include data visualization and process simulation, as well as text and voice analytics, social media analysis, and other predictive and prescriptive techniques.

New tools like these can make insights easier to understand and to act on at every point in the organization, and at every skill level. They transform numbers into information and insights that can be readily put to use instead of relying on further interpretation or leaving them to languish due to uncertainty about how to act.

What Leaders Can Do to Make Analytics Pay Off — A New Methodology

It takes big plans followed by discrete actions to gain the benefits of analytics. But it also takes some very specific management approaches. Based on data from our survey, our engagement experience, case studies and interviews with experts, we have been able to identify a new, five-point methodology for successfully implementing analytics-driven management and for rapidly creating value. The recommendations in the following pages are designed to help organizations understand this “new path to value” and how to travel it. While each recommendation presents different pieces of the information-and-analytics value puzzle, each one meets all of these three critical management needs:

Reduced time to value. Value creation can be achieved early in an organization’s progress to analytics sophistication. Contrary to common assumptions, it doesn’t require the presence of perfect data or a full-scale transformation to be complete.

Increased likelihood of transformation that’s both significant and enduring. The emerging methodology we’ve identified enables and inspires lasting change (strategic and cultural) by tactically overcoming the most significant organizational impediments.

Greater focus on achievable steps. The approach being used by the smartest companies is powerful in part because each step enables leaders to focus their efforts and resources narrowly, rather than implementing universal changes. This makes every step easier to accomplish with an attractive return on investment.

Whether pursuing the best channel strategy, the best customer experience, the best portfolio or the best process innovation, organizations embracing this approach will be first in line to gain business advantage from analytics.

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Recommendation 1: Focus on the Biggest and Highest Value Opportunities

Does attacking the biggest challenge carry the biggest risk of failure? Paradoxically, no — because big problems command attention and incite action. And as survey participants told us, management bandwidth is a top obstacle. When the stakes are high, the best talent will leap at the opportunity to get involved.

It’s extraordinarily hard for people to change from making decisions based on personal experience to making them from data — especially when that data counters the prevailing common wisdom. But upsetting the status quo is much easier when everyone can see how it could contribute to a major goal. With a potential big reward in sight, a significant effort is easier to justify, and people across functions and levels are better able to support it.

A sharp focus on major opportunities can excite an organization with new possibilities. “Where are the best places to advertise to get consumers into our store?” was the looming, time-critical challenge for Best Buy. “How can we reduce the fraud and abuse that are draining scarce money and resources?” is a common refrain among government agencies around the globe.

Conversely, don’t start doing analytics without strategic business direction, as these efforts are likely to stall. Not only does it waste resources, it risks creating widespread skepticism about the real value of analytics.

In our discussions with business executives, we have repeatedly heard that analytics aligned to a significant organizational challenge makes it easier to overcome a wide range of obstacles. Respondents cited many challenges, and none can be discounted or minimized: Executive sponsorship of analytics projects, data quality and access, governance, skills and culture all matter and need to be addressed in time. But when overtaken by the momentum of a single big idea and potentially game-changing insight, obstacles like these get swept into the wake of change rather than drowning the effort.

A process for inspiring change Despite analytics opportunities that are as close as the nearest data warehouse, the inability to understand how analytics can solve business challenges is the most daunting obstacle to adoption. And with management attention focused on other priorities, valuable analytics opportunities can be crowded out by business as usual.

The single greatest opportunity — and challenge — to speed adoption of analytics is to embed them into daily operations. Organizations that use analytics to answer big, make-it-or-break-it challenges have the greatest opportunity to meet their business goals. The answer needs to be simple and unambiguous to work for time-pressed managers. Based on our analysis, we recommend the process-application-data-insight-embed technique. It is a simple means by which an organization can operationalize insights drawn from data. (See Figure 5.)

The PADIE technique helps users across the organization understand from the start the full initiative as it applies to a specific business challenge. This technique enables business and analytic teams to work together to create analytic models based on use cases that show analytics in action.

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Recommendation 2: Within Each Opportunity, Start With Questions, Not Data

Traditionally, organizations are tempted to start by gathering all available data before beginning their analysis. Too often, this leads to an all-encompassing focus on data management — collecting, cleansing and converting data — that leaves little time, energy or resources to understand its potential uses. Actions they do take, if any, might not be the most valuable ones. Instead, organizations should implement analytics by first defining the insights and questions needed to meet the big business objective and then identify those pieces of data needed for answers. (see Figure 6.)

By defining the desired insights first, organizations can target specific subject areas, and use readily available data in the initial analytic models. The insights delivered through these initial models will illuminate gaps in the data infrastructure and business processes. Time that would have been spent cleaning up all data can be redirected toward targeted data needs and specific process improvements identified by the insights, enabling iterations with increasing levels of value.

Companies that make data their overriding priority often lose momentum long before the first insight is delivered. By narrowing the scope of these tasks to the specific subject areas needed to answer key questions, value can be realized more quickly, while the insights are still relevant.

Organizations that start with the data or process change first often end up with unintended consequences — such as data that is not extensible or processes that are ultimately eliminated — that require rework and additional resources to solve.

Speeding insights into business operations Compared with other respondents, Transformed organizations are good at data capture. Additionally, Transformed organizations are much more adept at data management. In these areas, they outpaced Aspirational organizations up to 10-fold in their ability to execute. (see Figure 7.)

Enterprise processes have many points where analytic insights can boost business value. The operational challenge is to understand where to apply those insights in a particular industry and organization. When a bank customer stops automatic payroll deposits or remittance transfers, for example, who in the organization should be alerted and tasked with finding out whether the customer is changing jobs or planning to switch banks? Where customer satisfaction is low, what insights are needed and how should they be delivered to prevent defections?

To keep the three gears moving together — data, insights and timely actions — the overriding business purpose must always be in view. That way, as models, processes and data are tested, priorities for the next investigation become clear. Data and models get accepted, rejected or improved based on business need. New analytic insights — descriptive, predictive and prescriptive — are embedded into increasing numbers of applications and processes, and a virtuous cycle of feedback and improvement takes hold.

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Recommendation 3: Embed Insights to Drive Actions and Deliver Value

New methods and tools to embed information into business processes — use cases, analytics solutions, optimization, work flows and simulations — are making insights more understandable and actionable. Respondents identified trend analysis, forecasting and standardized reporting as the most important tools they use today. However, they also identified tools that will have greater value in 24 months. The downswings in “as-is” methods accompanied by corresponding upswings in “to-be” methods were dramatic. (see Figure 8.)

Today’s staples are expected to be surpassed in the next 24 months by:

  1. Data visualization, such as dashboards and scorecards
  2. Simulations and scenario development
  3. Analytics applied within business processes
  4. Advanced statistical techniques, such as regression analysis, discrete choice modeling and mathematical optimization

Organizations expect the value from these emerging techniques to soar, making it possible for data-driven insights to be used at all levels of the organization. Innovative uses of this type of information layering will continue to grow as a means to help individuals across the organization consume and act upon insights derived through complex analytics that would otherwise be hard to piece together. For example, GPS-enabled navigation devices can superimpose real-time traffic patterns and alerts onto navigation maps and suggest the best routes to drivers.

Similarly, in oil exploration, three-dimensional renderings combine data from sensors in the field with collaborative and analytical resources accessible across the enterprise. Production engineers can incorporate geological, production and pipeline information into their drilling decisions.

Beyond 3D, animated maps and charts can simulate critical changes in distribution flow, or projected changes in consumption and resource availability. In the emerging area of analytics for unstructured data, patterns can be visualized through verbal maps that pictorially represent word frequency, allowing marketers to see how their brands are perceived.

New techniques and approaches transform insights into actions New techniques to embed insights will gain in value by generating results that can be readily understood and acted upon:

  • Dashboards that now reflect actual last quarter sales will also show what sales could be next quarter under a variety of different conditions — a new media mix, a price change, a larger sales team, even a major weather or sporting event.
  • Simulations evaluating alternative scenarios will automatically recommend optimal approaches — such as which is the best media mix to introduce a specific product to a particular segment, or what is the ideal number of sales professionals to assign to a particular new territory.
  • Use cases will illustrate how to embed insights into business applications and processes. For the direct-channel to agent-channel migration illustrated in Figure 5, automated work flows include initial communication with prospective insurance policyholders, timed to take place before leads are sent to the agent. (See Figure 5, Introducing the PADIE Technique for Operationalizing Analytics.) In that way, permission is secured before the agent makes a call, helping to ensure a smooth channel transition and a superior customer experience.

New methods will also make it possible for decision makers more fully to see their customers’ purchases, payments and interactions. Businesses will be able to listen to customers’ unique wants and needs about channel and product preferences. In fact, making customers, as well as information, come to life within complex organizational systems may well become the biggest benefit of making data-driven insights real to those who need to use them.

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Recommendation 4: Keep Existing Capabilities While Adding New Ones

When executives first realize their need for analytics, they tend to turn to those closest to them for answers. Over time, these point-of-need resources come together in local line of business units to enable sharing of insights. Ultimately, centralized units emerge to bring a shared enterprise perspective — governance, tools, methods — and specialized expertise. As executives use analytics more frequently to inform day-to day decisions and actions, this increasing demand for insights keeps resources at each level engaged, expanding analytic capabilities even as activities are shifted for efficiencies. (see Figure 9.)

Sophisticated modeling and visualization tools, as we have noted, will soon provide greater business value than ever before. But that does not mean that spreadsheets and charts should go away. On the contrary: New tools should supplement earlier ones, or continue to be used side by side, as needed.

There are other ways that capabilities grow and deepen within an organization. Disciplines like finance and supply chain are inherently data intensive, and are often where analytics first take root. Encouraged by early successes, organizations begin expanding analytic decision making to more disciplines. In Transformed organizations, reusability creates a snowball effect as models from one function are repurposed into another with minimal modifications.

Over time, data-driven decision making branches out across the organization. As experience and usage grow, the value of analytics increases, which enables business benefits to accrue more quickly.

Add value with an enterprise analytics unit Organizations that first experience the value of analytics in discrete business units or functions are likely soon to seek a wider range of capabilities — and more advanced use of existing ones. A centralized analytics unit, often called either a “center of excellence” or “center of competency,” makes it possible to share analytic resources efficiently and effectively. It does not, however, replace distributed and localized capabilities; rather, the central unit is additive, built upon existing capabilities that may have already developed in functions, departments and lines of business.

We found that 63 percent more Transformed organizations than Aspirational organizations use a centralized enterprise unit as the primary source of analytics. A centralized analytics unit can provide a home for more advanced skills to come together within the organization, providing both advanced models and enterprise governance by establishing priorities and standards by:

  • Advancing standard methods for identifying business problems to be solved with analytics
  • Facilitating identification of analytic business needs while driving rigor into methods for embedding insights into end-to-end processes
  • Promoting enterprise-level governance on prioritization, master data sources and reuse to capture enterprise efficiencies
  • Standardizing tools and analytic platforms to enable resource sharing, streamline maintenance and reduce licensing expenses.

In three distinct areas — application of analytic tools, functional use of analytics and location of skills — we found that adding capabilities without detracting from existing ones offers a fast path to full benefits from analytics-driven management.

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Recommendation 5: Use an Information Agenda to Plan for the Future

Big data is getting bigger. Information is coming from instrumented, interconnected supply chains transmitting real-time data about fluctuations in everything from market demand to the weather. Additionally, strategic information has started arriving through unstructured digital channels: social media, smart phone applications and an ever-increasing stream of emerging Internet-based gadgets. It’s no wonder six out of 10 respondents said the organization has more data than it knows how to use effectively.

All this data must be molded into an information foundation that is integrated, consistent and trustworthy, which were the leading data priorities cited by our respondents. Every phase of implementation needs to align the data foundation to an overall information agenda. The information agenda accelerates the organization’s ability to share and deliver trusted information across all applications and processes. It sets up information to serve as a strategic asset for the organization. (see Figure 11.)

The information agenda identifies foundational information practices and tools while aligning IT and business goals through enterprise information plans and financially justified deployment road maps. This agenda helps establish necessary links between those who drive the priorities of the organization by line of business and set the strategy, and those who manage data and information.

A comprehensive agenda also enables analytics to keep pace with changing business goals. An executive at one company, for example, told us it "had it down to a science” when it came to understanding the impact of price changes on single products and single channels. But the company was blindsided when it shifted to a customer-centric strategy, restructuring around bundled products and dynamic pricing across channels. Because its data marts had been developed de facto over time, the company found itself struggling to understand which tools and information were needed to go forward.

Lastly, building the analytic foundation under the guidance of a forward-looking information agenda enables organizations to keep pace with advances in mathematical sciences and technology. Without an enterprise-wide information agenda, units are likely to explore these new developments independently and adopt them inconsistently, a difficult path for gaining full business benefits from analytics.

Outline for an information agenda The information agenda provides a vision and high-level road map for information that aligns business needs to growth in analytics sophistication with the underlying technology and processes spanning:

  • Information governance policies and tool kits — from little oversight to fully implemented policies and practices
  • Data architecture — from ad hoc to optimal physical and logical views of structured and unstructured information and databases
  • Data currency — from only historical data to a real-time view of all information
  • Data management, integration and middleware — from subject area data and content in silos to enterprise information that is fully embedded into business processes with master content and master data management
  • Analytical tool kits based upon user needs — from basic search, query and reporting to advanced analytics and visualization

The information agenda is a key enabler of analytic initiatives by providing the right information and tools at the right times based upon business-driven priorities.

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Set Yourself up for Success

Aware that analytics-driven opportunities are central to growth and success, organizations seek to capture the value. They want to find the best place to begin, but for many, that entry point is elusive.

If you are Aspirational: Assemble the best people and resources to make the case for investments in analytics. To get sponsorship for initial projects, identify the big business challenges that can be addressed by analytics and find the data you have that fits the challenge.

If you are Experienced: Make the move to enterprise analytics and manage it by keeping focus on the big issues that everyone recognizes. Collaborate to drive enterprise opportunities without compromising departmental needs while preventing governance from becoming an objective unto itself.

If you are Transformed: Discover and champion improvements in how you are using analytics. You’ve accomplished a lot already with analytics, but are feeling increased pressure to do more. Focus your analytics and management bandwidth to go deeper rather than broader, but recognize it will be critical to continue to demonstrate new ways of how analytics can move the business toward its goals.

Techniques to Get Started

Pick your spots. Search for your organization’s biggest and highest priority challenge, and create a PADIE diagram to describe it. (See Figure 5, Introducing the PADIE Technique for Operationalizing Analytics.) Show available data sources, models to be built, and processes and applications where analytics will have an impact. Create multiple diagrams if you’re selecting from a strong list of possible initiatives. Keep in mind that your biggest problems, such as customer retention, anti-fraud efforts or advertising mix, are also your biggest opportunities. Change is hard for most, so select an initiative worthy of sustained focus that can make the biggest difference in meeting your most important business goals. Remember that focus is critical during these initial efforts. Do not get distracted once the targeted area is identified.

Prove the value. With your PADIE diagram in hand, use reason and benchmarks for initial executive sponsorship, but use a proof-of-value pilot to keep sponsors engaged. Estimate how much revenue can be gained, how much money can be saved and how much margins can be improved. Employ techniques to embed analytics to illustrate and prioritize the types of organizational changes that are needed to achieve the value. Pull it all together using an implementation road map with a clear starting point and a range of options for future opportunities.

Roll it out for the long haul. The challenge should be big, the model insightful and the business vision complete. However, the first implementation steps can be small, as long as they fit your agenda. Reduce your rework by using business analytic and process management tools that you have selected for the long haul — information governance, business analytics and business rules. As you make progress, don’t forget to analyze feedback and business outcomes to determine where your analytics model and business vision can be improved.

Make Analytics Pay Off

It takes big plans followed by discrete actions to gain the benefits of analytics. But it also takes some very specific management approaches. Each of our recommendations meets three critical management needs:

  • Reduced time to value
  • Increased likelihood of transformation that’s both significant and enduring
  • Greater focus on achievable steps.

To start on the fastest path to value, keep everyone focused on the big business issues and select the challenges that analytics can solve today within an agenda for the future. Build on the capabilities you already have. And always keep pressing to embed the insights you’ve gained into business operations.

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About the Research

To understand the challenges and opportunities associated with the use of business analytics, MIT Sloan Management Review, in collaboration with the IBM Institute for Business Value, conducted a survey of more than 3,000 business executives, managers and analysts from organizations located around the world. The survey captured insights from individuals in 108 countries and more than 30 industries, and involved organizations of various sizes. The sample was drawn from a number of different sources, including MIT alumni and MIT Sloan Management Review subscribers, IBM clients and other interested parties.

In addition to these survey results, we also interviewed academic experts and subject matter experts from a number of industries and disciplines to understand the practical issues facing organizations today. Their insights contributed to a richer understanding of the data, and the development of recommendations that respond to strategic and tactical questions senior executives address as they operationalize analytics within their organizations. We also drew upon a number of IBM case studies to illustrate further how organizations are leveraging business analytics and illuminate how real organizations are putting our recommendations into action in different organizational settings.

About the Authors:

Steve LaValle is the global strategy leader for IBM’s Business Analytics and Optimization service line. Michael S. Hopkins is editor-in-chief of the MIT Sloan Management Review. Eric Lesser is the research director and North American leader of the IBM Institute for Business Value. Rebecca Shockley is the business analytics and optimization global lead for the IBM Institute for Business Value. Nina Kruschwitz is an editor and the Special Projects Manager at MIT Sloan Management Review.

Contributors

Attivio, Inc. Attivio’s unified information access software combines enterprise search, BI, data warehousing and analytic capabilities for an integrated view of content and data, regardless of source or format.

Dassault Systèmes. Dassault Systèmes, a leader in 3D and product lifecycle management solutions, enables everyone — from designers to consumers and their communities — to create, share and experience in 3D.

Acknowledgments

John Armstrong, IBM; Marc Berson, IBM; Eric Brynjolfsson, MIT; Dr. Steve Buckley, IBM; William Fuessler, IBM; Bill Hoffman, Best Buy; Christer Johnson, IBM; Richard Lawrence, IBM; Thomas W. Malone, MIT; Andrew McAfee, MIT; Dwight McNeill, IBM; Chris Moore, IBM; Mychelle Mollot, IBM; Mark Ramsey, IBM; Will Reilly, IBM; Jeanne W. Ross, MIT; Michael Schrage, MIT; Michael Schroeck, IBM; Marc Teerlink, IBM; David Turner, IBM; Bruce Tyler, IBM; Andy Warzecha, IBM; Peter Weill, MIT; and Katharyn White, IBM.

Related Publications

M. S. Hopkins, S. LaValle, and F. Balboni, “10 Insights: A First Look at the New Intelligent Enterprise Survey on Winning with Data,” MIT Sloan Management Review 52, no.1 (fall 2010).

N. Kruschwitz and R. Shockley, “10 Data Points: Information and Analytics at Work,” MIT Sloan Management Review 52, no. 1 (fall 2010).

M.S. Hopkins, “The Four Ways IT Is Revolutionizing Innovation: An Interview with Erik Brynjolfsson,” MIT Sloan Management Review 51, no. 3 (spring 2010).

M.S. Hopkins, “Putting the Science in Management Science: An Interview with Andrew McAfee,” MIT Sloan Management Review 51, no. 4 (summer 2010).

IBM Corporation, “Capitalizing on Complexity: Insights from the Global CEO Study,” IBM Institute for Business Value, May 2010.

S. LaValle, “Breaking Away with Business Analytics and Optimization: New Intelligence Meets Enterprise Operations,” IBM Institute for Business Value, November 2009.

S. LaValle, “Business Analytics and Optimization for the Intelligent Enterprise,” IBM Institute for Business Value, December 2009.

References

1. In the performance self-assessment, other respondent options included “somewhat outperforming industry peers” and “on par with industry peers.”

2. R. Christenson, “N.C. and IBM Team up to Ferret out Medicaid Fraud,” March 25, 2010.

3. “Perdue Begins Medicaid Fraud, Waste Prevention Effort,” March 24, 2010.

4. B. Balfour, “Ten Recommendations for North Carolina’s Budget Reform and Advisory Commission (BRAC),” John W Pope Civitas Institute, February 10, 2010.

5. “Perdue Begins Medicaid Fraud, Waste Prevention Effort.”