Thomas H. DavenportReviews

Both authors have solid industry credentials, and each of them has developed significant amounts of experience and research on the topic of artificial intelligence, and analytics as well.

When they tick off three or four things you should know about an aspect of AI, I think it's safe to assume that their casual way of ticking off a few items, belies the amount of thought and work that went in to generating each item. Many times I have added digits in my print margin so I can reflect more deeply on these enumerations later.

If I were able to influence a next edition, I would find it easier to digest the material if more lists were used. Example: instead of sentences in a paragraph, something like (summarizing their points):

Three ways to address the top challenges in AI:

1. Plan on deployment from the start.
2. Put someone in charge of the entire process of development and deployment, such as a product manager for AI-based systems and processes.
3. Assign data scientists and product managers who work closely with stakeholders on the business side from the beginning.

What I liked most about this overview of Big Data is that it shows the potential without participating in the hyperbole. Davenport is an established expert in this field. This book covers what a modern practitioner or business leader needs to know, including the evolution, technology stacks, skill sets and, most importantly, the application.

Winners and losers in the age of smart machines

We do not know unless we first measure.

The authors build on their previous work, which I was glad to see because the analytics space is evolving quickly. A few elements I especially liked about this book include a framework for what analytics can answer, a model for success, and a tie in to employee engagement. In their framework for the questions analytics can answer, they distinguish between information and insight, and then show how each can be applied to past, present and future. The DELTA model outlines key success factors, such as enterprise orientation, leadership and the analysts themselves. Most of the body of the book is an expansion on these factors. Books in this genre that build on a previous work to often repeat the same case studies featuring the same companies. I noticed a return to some of the original firms in this case as well, but there were just enough new stories to convince that the authors work from a growing base of experience. Overall, this book gives practical and insightful perspective on applying analytics in business.

This is an excellent overview about how businesses can - and must - sharpen their analytic and measurement capabilities in order to understand that clients and operate effectively. The concepts are as relevant today as when the book was published nine years ago. If the case studies featured are now well-known examples, their permanence in a fast-moving economy validates the importance of the book. Two key concepts I found especially valuable were the importance of aligning analytics to a strategic strength and the value of understanding the different paths leadership can take in growing an analytic capability. The authors also cover the human factors, the basic technology landscape, and a range of applications spanning sectors and functions.

Since I know next to nothing about Big Data, this book was a welcomed oasis in the rapidly changing world of information technology. Davenport is a business consultant, as such his focus is on the conceptual and business end of the idea. I was looking for something that was closer to the technical end. To be fair, he does make a more than valiant stab at listing what big data can do for actual physical products rather than just virtual products.

I think he did a great job of introducing me to the idea of big data, what it is, what it would take to implement, the million and one considerations that one needs to take into account prior to jumping in with both feet.

I think the key thing that he kept hammering on was that Big Data is more than just analytics on steroids. He made that point abundantly clear. I appreciate the way Davenport presents the information concisely and in detail without losing the reader and without condescending to the reader's lack of knowledge.

Obviously this is not a be all end all book on big data, but it is a great introduction to the topic.

Starts off very badly by applying neoclassical economic models to the alleged markets for information within organizations. Ideas from anthropology and institutional economics would be closer.½

Gaining value from a book like this depends on your starting point. I know a bit about data collection and manipulation and I find little of value in Mr. Davenport's presentation. He seems more interested in allaying the reader's fears of big data than providing concrete examples of how we can use big data to improve the ways we manage our businesses. If you are new to data manipulation or are curious about how business data is seen these days then you might find the book useful. If not, then I suggest that you go on to something more practical.

I received a review copy of Big Data at Work through NetGalley.com.

ARC provided by NetGalley

Big data. What is it and why the heck do we keep hearing people talk about it? Hasn't it been around for years and years? Haven't we always looked at data? Yes..and no. In Big Data at Work author Tom Davenport, expert in analytics, shares with us that at one time he too thought big data was just a retread of old information. But then he started looking into it and he discovered...big data is new. In this book Davenport tells us in a concise, nonsense, and nontechnical way of what big data is and why it should matter to us.

Davenport starts us at the very beginning of explaining in simple, easy to understand terms and illustrations as to what big data is and why it's different from regular analytics. Big data, as Davenport explains, consists of unstructured data--such as comments on a feedback form; is made up of 100 terabytes or more of information; and that it is a continuous flow of data, it doesn't stop just because a survey ends. Davenport clearly explains to us that not everyone will need big data or people to analyze it, but walks us through the different aspects that might be of interest to us, why it matters, and how we can go about implementing it in our own businesses. He shares with us how companies the size of Netflix and Google are using big data to help change their approach at how they interact with their users, but even more importantly he shares with us how startups are utilizing big data to get ahead of their peers.

Even more importantly for me, Davenport explains to readers about how to get people on board with wanting to examine big data and how to build a strategy and framework into implementing it. I say it's the most important for me, because so many authors put out pie in the sky dreams or hopes, or suggest things that are only practical for businesses the size of Google. Davenport instead talks about how to do this on a practical small scale and gives us examples of how it has worked for different groups already in existence.

For anyone that is interested in the study of data, whether big or small, and how you can utilize it in your place of work, this is a must have book. Davenport's clear and concise terminology will help you understand it and explain it to others that you work with, even if they think that data crunching is looking at 2 spreadsheets at a time. I give this book 4 out of 5 stars and it will definitely have a place on my book shelf.

A seminal work on knowledge management business practices. Some aspects have become a bit dated as technology has evolved. Davenport makes the key statements about the core need for organization knowledge and retention.

I absolutely loved this book. I would have liked more examples of how to apply the knowledge they described, but there were still so many great success stories of companies who have applied data analytics. I recently got into this field and this book was very motivational and exciting!

Since this book is a over ten years old, I found myself wondering why I haven't seen more of the ideas it mentions. Maybe it's just where I work, but I'm not so sure of that. The ideas it has around managing information from an ecological perspective had me nodding my head in agreement many times, and shaking it when thinking of how things are done in the office. Sometimes the reading gets a bit slow, but the content is great and gets you thinking, at least if you're in an organization with communication and knowledge management issues. Well-put together.

Tom and Jeanne have written an excellent new book (building on a paper they wrote some time ago) about what they call "analytic competitors", that is to say companies that use their analytic prowess not just to enhance their operations but as their lead competitive differentiator. The book discusses a number of these analytic competitors and gives an overview of how analytics can be used in different areas of the business and how you can move up the analytic sophistication scale.

The book has two parts - one on the nature of analytical competition and one on building an analytic competency. The first describes an analytical competitor and how this approach can be used in both internal and external processes. The second lays out a roadmap for becoming an analytical competitor, how to manage analytical people, a quick overview of a business intelligence architecture and some predictions for the future.

They define an analytical competitor as an organization that uses analytics extensively and systematically to outthink and outexecute the competition. The analytics are in support of a strategic distinctive competency and they argue, persuasively, that without a distinctive capability you cannot be an analytic competitor.

The book outlines what they call four pillars of analytical competition- a distinctiive capability, enterprise-wide analytics, senior management commitment and large scale ambition. They lay out 5 stages of analytic competition from "analytically impaired" to "analytic competitor". The importance of experimentation is made clear and the book repeatedly emphasizes the need for companies and executives to be willing to run the business "by the numbers".

The book is full of stories about how companies compete analytically and this is one of the book's strengths. It also has a great list of questions to ask about a new initiative and outlines a number of ways to get a competitive advantage from your data. Regardless of the competitive approach, the need for analytical executives to be willing to act on the results of analyses is made clear. The book ends with a great list of changes coming.

This is a very interesting book both for those interested in competing on analytics and those interested simply in making more use of their data.

More evocative of the appeal of competing on analytics than a tutorial on how to compete on analytics... Since it's a general business book, that's not really a criticism (unless the book was 5000 pages long, it can't go into too much detail)

Loveman has hired into Harrah's a number of very analytical senior and middle managers. He also listed three reasons why employees could be fired from Harrah's: "... you don't harass women, you don't steal, and you've got to have a control group."

- page 30½

This was a little thin on actual suggestions and recommendations, but it did raise some valid points.