Thomas H. Davenport
Visiting Professor of Business Administration
Tom Davenport is a Visiting Professor at Harvard Business School, where he teaches in the Technology and Operations Management unit. He also serves as the President’s Distinguished Professor of Information Technology and Management at Babson College, the co-founder and research director of the International Institute for Analytics, and a Senior Advisor to Deloitte Analytics. In the past he has been a partner or consultant at McKinsey and Co., Ernst & Young, and Accenture, and a tenured professor at the University of Texas at Austin and Boston University.
He has published widely on the topics of analytics in business, process management, information and knowledge management, and enterprise systems. He pioneered the concept of “competing on analytics” with his best-selling 2006 Harvard Business Review article (and his 2007 book by the same name). The article was named one of ten “Must Reads” in HBR’s ninety-year history. His article and book on business process reengineering were the first ones on the topic, and his co-authored book, Working Knowledge: How Organizations Manage What They Know, was the first and best-selling book on knowledge management.
His most recent book is Judgment Calls: Twelve Stories of Big Decisions and the Teams that Got Them Right, with Brook Manville. He wrote, co-authored, or edited fourteen other books, which have been translated into 24 different languages. He has written over 100 articles for such publications as Harvard Business Review, Sloan Management Review, the Financial Times, and many other publications. In 2003 he was named one of the world’s “Top 25 Consultants” by Consulting magazine. In 2005 Optimize magazine’s readers named him among the top 3 business and technology analysts in the world. In 2007 and 2008 he was named one of the most 100 influential people in the information technology industry by Ziff-Davis magazines.
Davenport received his BA degree from Trinity University, and his A.M. and Ph.D. degrees from Harvard, all in Sociology. He lives in Cambridge, MA with his wife Joan, and has two grown sons.
Keeping Up with the Quants: Your Guide to Understanding and Using Analytics
Managers today need to be able to analyze and make sense of data. They need to be conversant with analytical technology and methods and to make decisions on quantitative analysis. This book offers a variety of practical tools and examples to improve a manager's understanding of business analytics, and to enhance their thinking and decision processes.
Davenport, Thomas H., and Jinho Kim. Keeping Up with the Quants: Your Guide to Understanding and Using Analytics
. Harvard Business Review Press, 2013.
Analytics at Work: Smarter Decisions, Better Results
Davenport, Thomas H., Jeanne Harris, and Robert Morison. Analytics at Work: Smarter Decisions, Better Results
. Harvard Business Press, 2010.
Enterprise Analytics: Optimize Performance, Process, and Decisions Through Big Data
This book, an edited collection of research papers from the International Institute of Analytics, addresses a wide variety of key topics in managing business analytics and big data at the enterprise level. It includes key applications of analytics, human and organizational issues in building analytical capabilities, and case studies of the application of analytics in several industries.
Keywords: business analytics;
Judgment Calls: Twelve Stories of Big Decisions and the Teams That Got Them Right
This book includes twelve detailed stories of organizations that have successfully tapped their data assets, diverse perspectives, and deep knowledge to build an organizational decision-making capability. The book introduces a model that utilizes the collective judgment of an organization so that the right decisions are made, and the entire organization profits.
Keywords: organizational judgment;
Recorded Future: Analyzing Internet Ideas About What Comes Next
Recorded Future is a "big data" startup company that uses Internet data to make predictions about events, people, and entities. The company primarily serves government intelligence agencies, but has some private sector clients and is considering taking on more. The CEO, Christopher Ahlberg, is wrestling with several key decisions about where to take the company in the future.
Keywords: big data;
Strategies for Preventing a Knowledge-Loss Crisis
When employees leave an organization, they depart with more than what they know; they also leave with critical knowledge about who they know. Thus, the departure of key people can significantly affect the relationship structure and consequent functioning of an organization. In particular, companies should be aware of the unique knowledge held by three important types of employees: "central connectors" (those who are regularly asked for help, typically because they have a high level of expertise in one or more areas), "brokers" (those who act as bridges across subgroups) and "peripheral players" (those who reside on the boundaries of a network but could still possess valuable niche expertise and outside knowledge). Departure of an employee who filled any one of these roles presents knowledge-loss risks that need to be addressed. The departure of a handful of key brokers, for example, could fracture the social network of an organization into isolated subgroups. Thus companies need to take various measures to (1) identify key knowledge vulnerabilities by virtue of both what a person knows and how that individual's departure will affect a network and (2) address specific knowledge-loss issues based on the different roles that employees play in the network.
The Prediction Lover's Handbook
When picking assessment tools to inform better decisions about future paths, executives are faced with a wide variety of options--some of which are well established, while others are in early stages of development. The authors provide an insider's guide to prediction and recommendation techniques and technologies. They cover prediction tools including attributized Bayesian analysis, biological responses analysis, cluster analysis, collaborative filtering, content-based filtering/decision trees, neural network analysis, prediction (or opinion) markets, regression analysis, social network-based recommendations and textual analytics. With each potential tool, they briefly describe the technique, who uses it and for what purpose, its strengths and weaknesses, and its future prospects as a prediction tool. Finally, the authors offer up an indication of the best time in the decision process to begin using the tool.
The Secrets to Managing Business Analytics Projects
Managers have used business analytics to inform their decision making for years. And while few companies would qualify as being what management innovation and strategy expert Thomas H. Davenport has dubbed "analytic competitors," more and more businesses are moving in that direction. Which best practices do the most experienced project managers involved in business analytics projects employ, and how would they advise their less experienced peers? The authors found that the most important qualities could be sorted into five areas: having a delivery orientation and a bias towards execution; seeing value in use and value of learning; working to gain commitment; relying on intelligent experimentation; and promoting smart use of information technology. Although many of the business analytics project managers the authors interviewed report to the IT department, they identify with the business side of their organizations. Best-in-class CIOs realize that IT and business can't afford to continue to be at loggerheads with one another. IT should pursue opportunities to deliver faster implementation cycles, maintaining just enough process and architectural hygiene to ensure quality and professional support.
Cognizant 2.0: Embedding Community and Knowledge Into Work Processes
Knowledge management has been a high priority for Cognizant Technology Solutions since its inception since its global delivery model requires the global sharing of knowledge. Its first major tool was called the Knowledge Management Appliance but as Web 2.0 tools came into wider use, this evolved into what the company called "Cognizant 2.0" (C2) which was designed to ensure that the KM Appliance capabilities for storing documents and participative tools such as blogs and wikis were directed towards supporting business goals. This required the development of a set of structured work process guidelines and tasks for each major type of work performed internally and for clients. Increasing awareness amongst its clients about C2 has led the company into considering whether it should turn this into a client-facing service offering itself. As its clients become more interested in knowledge management within their own companies, the interest in a C2-based offering could grow.
Keywords: Knowledge Management;
Knowledge Use and Leverage;
Information Technology Industry;
What People Want (and How to Predict It)
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. They then discuss two aspects of the prediction market. First, the need for better prediction for distributors of cultural products, and second, the potential for business models around prediction techniques.
Keywords: Product Development;
Forecasting and Prediction;
Motion Pictures and Video Industry;
How Fast and Flexible Do You Want Your Information, Really?
Almost all executives want more and faster information, and almost all companies are racing to provide it. What many of them are overlooking is that the real aim should not be faster information but faster decision making, and those aren't the same things. Executives tend to request more information than they can use, and oftentimes the cycle time of decision making, not the speed of information flow, is the real bottleneck. While it is critical to identify what information is needed, it is just as important to deliver it at the right time. The types of information needed most quickly are not static and can depend on several conditions, including whether the current state of the economy is growing or contracting. The right cycle time for particular types of information can vary widely by industry, but in general the highest requested frequency is for competitor news, sales and customer news. Based on responses to their surveys and interviews of senior executives and managers, the authors provide suggestions and guidelines to enable IT organizations to deliver information more quickly and flexibly. That requires changes to both human and technical capabilities, but those changes could make all the difference in having the correct information to avert a crisis.
Keywords: Management Teams;