Competing on Analytics: The New Science of Winning

High-performance businesses are now building their competitive strategies around data-driven insights that are, in turn, generating impressive business results. Their secret weapon? Analytics: sophisticated quantitative and statistical analysis and predictive modeling supported by powerful information technology and data-savvy senior leaders.

Exemplars of analytics are using new tools to identify their most profitable customers and offer them the right price, to accelerate product innovation, to optimize supply chains, and to identify the true drivers of financial performance. A wealth of examples—from organizations as diverse as Amazon, Barclay's, Capital One, Harrah's, Procter & Gamble, Wachovia and the Boston Red Sox—illuminate how to leverage the power of analytics.

Analytics (as well as data access and reporting) is a subset of what has come to be called business intelligence—a set of technologies and processes that use data to understand and analyze business performance.

Why compete on analytics? At a time when companies in many industries offer similar products and use comparable technology, many of the previous bases for competition are no longer viable. In a global environment, physical location is frequently not a source of advantage, and protectionist regulation is increasingly rare. Proprietary technologies can often be rapidly copied and attempts to achieve breakthrough innovation in products or services often fail. What's left as a basis for competition is execution and smart decision making. An organizational commitment to and developed capability of analytics is enabling market-leading companies to succeed in the rapidly evolving arena of global competition.

More than ever before, business decisions have multimillion-dollar impact and multiyear consequences. In an environment where business judgment can make or break a company, there is less and less room for intuition and instinct—the proverbial "gut feeling"—in the decision-making process.

Which is why high-performance businesses are turning to analytics—the extensive analysis and strategic use of data and other information—to drive key decisions and actions.

Indeed, this fact-based management model, which uses a wide array of quantitative techniques to analyze information, generate insight and optimize processes, is emerging as one of the last remaining forms of sustainable competitive differentiation. This is particularly important as many of the traditional bases of competition are rapidly disappearing; for example, the Internet has all but eliminated geographic barriers, while product innovations bestow only fleeting advantage as they are quickly imitated by competitors.

Analytics enable organizations to stay one step ahead, to out-think and out-execute the competition. While many companies use analytics in selective ways, what sets the leaders apart is their strategic use of this robust analysis across the entire enterprise. For these companies, analytics is the defining component of their distinctive capability, which is one of the three basic building blocks of high performance.

We found that analytics were a critical element in creating value from an organization's investment in information technology. And even if a company doesn't see analytics as a source of competitive advantage today, our research indicates a more analytical future for practically every firm.

Not only is fact-based decision making a superior management model; our research found a clear link between the use of analytics and achieving and sustaining high performance.

Compared to Low performers, High performers are much more likely to:

The resources below are a collection of videos, Outlook Articles, Research Notes and external articles discussing Competing on Analytics.

Videos:
Jeanne Harris discussing Competing on Analytics.

Outlook Articles

Winning with Analytics
At a time when companies in many industries offer similar products and use similar technology, high-performance business processes count among the last remaining points of differentiation. What's left as the basis for competition? Three things: efficient and effective execution, smart decision making and the ability to wring every last drop of value from business processes.

Research Notes

How Consumer Goods Companies Compete on Analytics to Achieve High Performance by Jeanne G. Harris [PDF, 199KB]

“With traditional bases of competitive advantage evaporating, what’s the best outlook for companies that make and sell consumer products? There’s a solid basis for competition: flawless, efficient execution and decision making that outsmarts rivals. The key is found in the sophisticated use of analytics.”

How Pharmaceutical Companies Use Analytics to Achieve High Performance by Jeanne G. Harris [PDF, 177KB]

“In the pharmaceutical industry, business is anything but smooth sailing. Most pharmaceutical companies are under enormous pressure to discover and develop new drugs faster as existing patents expire and generics threaten market share. Leading companies are finding a way forward: they are using analytics to drive better decision making and enhance their execution to outsmart rivals.”

How Retailers Compete on Analytics to Achieve High Performance
Leading retailers are distinguished not so much by their business processes as such, but by the analytical skills and capabilities that allow them to achieve lasting competitive differentiation. Their analytical capability is their competitive advantage and so a route to high performance.

The Architecture of Business Intelligence
This report explores the six elements of business intelligence architecture, with particular attention to data management since it drives all the other architectural decisions.

Harvard Business Review Case Study & Commentary

The Dark Side of Customer Analytics
This HBR case study depicts a fictional insurance company considering buying customer data from a grocery chain to identify correlations between grocery purchases and insurance claims in order to design insurance products based on those correlations. The consideration is how both companies can use such data responsibly.

Accenture Information Management Services

The Accenture Information Management Services page about Competing on Analytics including:

• Listen to a podcast about how to compete on analytics
• View a video on how to compete on analytics
• Read Accenture's insights into Competing on Analytics
• Learn about the business intelligence architecture necessary to supporting analytics

Articles

The Dark Side of Customer Analytics by Thomas H. Davenport and Jeanne G. Harris
Can a business know too much about a consumer’s spending habits? That all depends on how responsibly and ethically a business leverages that data, writes Accenture’s Jeanne Harris in the May 2007 issue of Harvard Business Review. The article gives insight into how companies are using analytics to identify their most profitable customers; accelerate product innovation; optimize supply chains and pricing; and identify the true drivers of financial performance.

What People Want and How to Predict It by Thomas H. Davenport and Jeanne G. Harris
Historically, neither the creators nor the distributors of cultural products such as books or movies have used analytics – data, statistics, predictive modeling – to determine the likely success of their offerings. Instead, companies relied on the brilliance of tastemakers to predict and shape what people would buy. Creative judgment and expertise will always play a vital role in the creation, shaping and marketing of cultural products. But the balance between art and science is shifting.

Today, companies have unprecedented access to data and sophisticated technology that allows even the best-known experts to weigh factors and consider evidence that was unobtainable just a few years ago. And with increased cost and risk associated with the creation of cultural products, it has never been more important to get these decisions right. In this article, the authors describe the results of a study of prediction and recommendation efforts for a variety of cultural products. They discuss different approaches used to make predictions, the contexts in which these predictions are applied and the barriers to more extensive use, including the problem of decision making pre-creation.

The Prediction Lover’s Handbook by Thomas H. Davenport and Jeanne G. Harris
This article profiles in detail, ten critical and effective assessment tools (including prediction and recommendation techniques and technologies) for better-informing decisions. These include:

• Biological Response Analysis
• Cluster Analysis
• Attributized Bayesian Analysis
• Content-Based Filtering/Decision Trees
• Neural Network Analysis
• Collaborative Filtering
• Prediction (Or Opinion) Markets
• Regression Analysis
• Social Network-Based Recommendations
• Textual Analytics