Enterprise Analytics: Optimize Performance, Process and Decisions Through Big Data

by Thomas H. Davenport

<!--[if gte mso 9]> Normal 0 false false false MicrosoftInternetExplorer4 <![endif]--> <!--[if gte mso 9]> <![endif]--> <!--[if gte mso 10]> <![endif]--> The Definitive Guide to Enterprise-Level Analytics Strategy, Technology, Implementation, and Management Organizations are capturing exponentially larger amounts of data than ever, and now they have to figure out what to do with it. Using analytics, you can harness this data, discover hidden patterns, and use this knowledge to act meaningfully for competitive advantage. Suddenly, you can go beyond understanding “how, when, and where” events have occurred, to understand why – and use this knowledge to reshape the future. Now, analytics pioneer Tom Davenport and the world-renowned experts at the International Institute for Analytics (IIA) have brought together the latest techniques, best practices, and research on analytics in a single primer for maximizing the value of enterprise data. Enterprise Analytics is today’s definitive guide to analytics strategy, planning, organization, implementation, and usage. It covers everything from building better analytics organizations to gathering data; implementing predictive analytics to linking analysis with organizational performance. The authors offer specific insights for optimizing supply chains, online services, marketing, fraud detection, and many other business functions. They support their powerful techniques with many real-world examples, including chapter-length case studies from healthcare, retail, and financial services. Enterprise Analytics will be an invaluable resource for every business and technical professional who wants to make better data-driven operations, supply chain, and product managers; product, financial, and marketing analysts; CIOs and other IT leaders; data, web, and data warehouse specialists, and many others.

Genres: Nonfiction, Business

268 pages, Hardcover

First published September 4, 2012

About the author

Tom Davenport holds the President's Chair in Information Technology and Management at Babson College. His books and articles on business process reengineering, knowledge management, attention management, knowledge worker productivity, and analytical competition helped to establish each of those business ideas. Over many years he's authored or co-authored nine books for Harvard Business Press, most recently Competing on Analytics: The New Science of Winning (2007) and Analytics at Work: Smarter Decisions, Better Results (2010). His byline has also appeared for publications such as Sloan Management Review, California Management Review, Financial Times, Information Week, CIO, and many others.

Davenport has an extensive background in research and has led research centers at Ernst & Young, McKinsey & Company, CSC Index, and the Accenture Institute of Strategic Change. Davenport holds a B.A. in sociology from Trinity University and M.A. and Ph.D. in sociology from Harvard University. For more from Tom Davenport, visit his website and follow his regular HBR blog.

Reviews

[Abhinav Agarwal · Rating 2 out of 5]

Lectures, Meanders, Pontificates, But Does Not Educate

Or, how a book on Big Data, Enterprise Analytics, and technology neatly skirts any meaningful discussion of Big Data, Enterprise Analytics, and technology.

While a few chapters stand out for their reasoning and clarity, what is jarringly absent from this book is any meaningful, technical discussion about Big Data itself. Without such a discussion, most of the book's content can be recycled with minimum effort ten years from now and applied to the next big thing in technology. Even assuming that this book is targeted at decision makers and so-called C-level executives, an absence of the nuances and complexities of Big Data mean that executives will be as clueless on that dimension of Big Data knowledge after reading the book as before. If you are responsible for selling sausages, you had jolly well get a look at the sausage factory, if not work there a day.

See my full review on either Amazon (http://www.amazon.com/review/R2NZ7EFZ...) or my blog (http://blog.abhinavagarwal.net/2013/0...)

[Johnathan · Rating 3 out of 5]

I had to read this book for my college course ANLY645 Enterprise Analytics. TO be honest I feel I did not learn a lot from this book. A lot of the Case Studies are out of date. An example would be the Sears Holding Corporation. A lot of the information provided from this book was available in other classes, and this book is dry. Other books have a better chance to engage the reader. The textbook has some good information, however the textbook does not provide a lot.

[Mark Underwood · Rating 3 out of 5]

My review on Amazon Vine: This text covers an important topic, "Big Data," an expression which has gained traction in the IT community as well as with the public. As one of relatively few texts available on the topic, it was not possible to downgrade it below "OK" in this rating system's shorthand. But a case could be made that perhaps it had been written prematurely vis a vis the state of the art, or by authors not fully immersed in a realistic, full IT life cycle, or perhaps with too rarified a view of the business processes in which Big Data gains context. The text's title lays claim to a broad swath of what Big Data could achieve -- optimization, decision-making -- which perhaps overreaches what a somewhat disjoint collection of essays from the "International Institute for Analytics" could achieve. What Big Data delivers for, say, Omniture (now part of Adobe's marketing analytics arm) from Fortune 1000 web site traffic is quite different from what an SMB email marketing campaign offers. Both are "Big" relative to enterprise capabilities, but require different skills, roles and, most importantly, expectations. I admit to my bias here as a practitioner of Qlikview, one of a family of in-memory software tools used to tackle Big Data problems in, but not limited to, medium sized IT shops. The problems practitioners encounter there are real, the data volume is Big, and expectations are high. But tactical problem solving, at this stage of the discipline, should begin in the periodical trade press, then perhaps Kimball The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling (Second Edition), and then academic review articles from the ACM or IEEE digital libraries. One comes away from reading this book impressed with the scope of the problems but not with the authors' understanding of possible solutions or what near term solutions will be available to practitioners. And the essayists' often scant mention of connections to business process formalisms is the single most troublesome oversight.

[Ariadna73 · Rating 3 out of 5]

I managed to find at least one quotable paragraph. It describes two methodologies that could be of use and it succeeds on defending analytics as a very good; useful and prosperous field.

This is the links to the notes I wrote in this book:

DAVENPORT - Enterprise Analytics

[Liam · Rating 3 out of 5]

"Excitement about analytics has been augmented even more by excitement about big data. The concept refers to data that is either too voluminous or too unstructured to be managed and analyzed through traditional means." (2)

"Real-time data, it seems, is widely expected to produce better results by those with limited experience. Those who have successfully built and deployed models seem to know that this is not necessarily true." (118)

"It is also important to understand what we don't work on -- what we say 'no' to. We have a rule of thumb that our time is worth $2,000 per hour. Although that is not what I pay my analysts or bill my clients, it is an estimate of the annual value we create for the organization. That rile governs everything from which meetings we attend and which projects we double up on, to how deep we go in the research, when to stop an effort, and most importantly, what projects don't make the priority list." (Carl Schleyer, head of Sears HR analytics, 234-5)

[Nikos Dimitrakopoulos · Rating 3 out of 5]

A good introduction on how to approach analytics/BI/data science in mid to large scale organization, touching most aspects of it (how, what, when, etc).

Doesn't go deep to any aspect but that's a positive thing if you want to start from somewhere.

[Mills College Library]

658.4038 E6114 2013