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: - Take the Competing on Analytics Quick Quiz
- 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
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