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e-Book Enterprise Analytics: Optimize Performance, Process, and Decisions Through Big Data (FT Press Operations Management) epub download

e-Book Enterprise Analytics: Optimize Performance, Process, and Decisions Through Big Data (FT Press Operations Management) epub download

Author: Thomas H. Davenport
ISBN: 0133039439
Pages: 304 pages
Publisher: Pearson FT Press; 1 edition (September 23, 2012)
Language: English
Category: Programming
Size ePUB: 1245 kb
Size Fb2: 1878 kb
Size DJVU: 1656 kb
Rating: 4.5
Votes: 416
Format: lrf mobi txt doc
Subcategory: Technologies

e-Book Enterprise Analytics: Optimize Performance, Process, and Decisions Through Big Data (FT Press Operations Management) epub download

by Thomas H. Davenport



This book is a collection of eighteen essays on enterprise analytics by fourteen different authors, ten written or. .

The last two words in the subtitle of this book, "Optimize Performance, Process, and Decisions Through Big Data" might be the catalyst for some to pick up a copy of this book, but this is not a book specifically about Big Data.

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This book is a collection of eighteen essays on enterprise analytics by fourteen different authors, ten written or.

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Part of the FT Press Operations Management series. Chapter 4 Analytics on Web Data: The Original Big Data 47 Bill Franks. Sorry, this book is no longer in print. Chapter 5 The Analytics of Online Engagement 71 Eric T. Peterson. Chapter 6 The Path to Next Best Offers for Retail Customers 83 Thomas H. Davenport, John Lucker, and Leandro DalleMule. Part IV: The Human Side of Analytics Chapter 11 Organizing Analysts 157 Robert F. Morison and Thomas H.

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Part II: Application of Analytics Chapter 3 Leveraging Proprietary Data for Analytical Advantage 37 Thomas H. Chapter 12 Engaging Analytical Talent 179 Jeanne G. Harris and Elizabeth Craig. Chapter 13 Governance for Analytics 187 Stacy Blanchard and Robert F. Morison.

Download Free eBook:Enterprise Analytics: Optimize Performance, Process, and Decisions . Using analytics, you can harness this data, discover hidden patterns, and use this knowledge to act meaningfully for competitive advantage.

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.

book by Thomas H. Enterprise Analytics : Optimize Performance, Process, and Decisions Through Big Data. by Thomas H.

Author: Thomas H. Title: Enterprise Analytics: Optimize Performance, Process, and Decisions Through Big Data (FT Press Operations Management). Enterprise Analytics is today’s definitive guide to analytics strategy, planning, organization, implementation, and usage.

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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 decisions: 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.
Mildorah
This book is a collection of eighteen essays on enterprise analytics by fourteen different authors, ten written or co-written by the editor, who also wrote "Competing on Analytics: The New Science of Winning" with Jeanne G. Harris, and "Analytics at Work: Smarter Decisions, Better Results" with Robert Morison and the aforementioned author (see my reviews), both of whom contributed to this book as well. The topics in this text are a bit more varied than in the earlier efforts of the editor. Rather than focusing on how analytics can be utilized to compete in the marketplace, as was the case in the first book, this latest entry moves in the direction of the second book, which is how to practice analytics. Not in the sense of in-the-trenches toolsets or statistical methods, but at a level more akin to enterprise architecture.

After providing an overview of analytics, the authors direct the reader to topics such as applications and technologies of analytics (approximately 50% of the content), analysts and governance, and case studies. While over a dozen authors contributed to the material, in my opinion it was edited well so that the writing style does not vary to the point of distraction, and the editor reasonably constrained the scope. Chapter 1 ("What Do We Talk About When We Talk About Analytics?") and Chapter 2 ("The Return on Investments in Analytics") were especially well done.

It is about time that someone defined the analytics space, and the table that Davenport provides to break down the three types of "business analytics" (as opposed to other types of analytics, such as "web analytics"), along with the accompanying discussion, is well recommended reading so that everyone can finally get on the same page in this area. There exists some overlap with the table that was provided in Chapter 1 of the second book mentioned above ("What It Means to Put Analytics to Work"), but it is clear that more thought has been put into this subject in the ensuing years. Although the questions that are addressed by analytics have stayed virtually the same, they are more specific in some cases, and the new breakdown into three types of analytics (descriptive, predictive, and prescriptive) from the original two types of analytics (information and insight) in my opinion is much more intuitive.

Along these same lines, I also especially appreciated the definitions and material focused on "engagement" in Chapter 5 ("The Analytics of Online Engagement"), and how one firm measures online engagement with a generalized model it devised. The discussion provided in Chapter 9 ("Analytical Technology and the Business User") on how the multipurpose BI environment of the past, which did not serve business users well, is evolving into environments that are either single-purpose for business users and professional analysts, or multipurpose without the assumption that simplification is needed for business users, was also well done.

The last two words in the subtitle of this book, "Optimize Performance, Process, and Decisions Through Big Data" might be the catalyst for some to pick up a copy of this book, but this is not a book specifically about Big Data. As is noted in the introduction to this text, "This book is based primarily on small-data analytics, but occasionally it refers to Big Data, data scientists, and other issues related to the topic. Certainly many of the ideas from traditional analytics are highly relevant to Big Data analytics as well." My recommendation to readers specifically interested in the Big Data space is to check out the O'Reilly Strata series on Big Data (see my reviews).
JoJogar
This book provides a number of actionable techniques to structure analytical groups to enable them to provide business beneficial insights. There are also some good ideas on ways to use different types of analytics to generate business value. This book would be very helpful to people in an analytical enabler role as it describes a number of options to work effectively with business and technical teams.

Wick.
Damand
As described. Thanks
Anayajurus
the book was okay
Kigul
Arrived as promised.
Tisicai
This book serves as a great starting point for beginners in the Analytics profession. It gives a comprehensive overview of analytics, covering different aspects such as the application and value of analytics, the technologies involved, the talents needed, and also a few case studies.
Beydar
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.

Big Data, Unstructured Data, the Cloud - if these three buzzwords were not enough, you can add the salsa-ish phrase SoLoMo - i.e. Social, Local, and Mobile, to the mix. Businesses, consultants, enterprises, anyone who is anything in technology wants to know more about what this buzzword alphabet soup is, and how to make sense of it before their competitor does, or worse - a disruptor.

The hope is that if the decision makers, the corner-room occupiers can understand this, they will be better able to drive a coherent process and structure within their organizations to take advantage and benefit. Hence this book.

The book is a collection of eighteen chapters, divided into five parts - the first is an "Overview", "Application", "Technologies", "Human Side", and "Case Studies" of Analytics. Each chapter is written by a different author, with a total of fourteen authors in the fray. A few chapters have been written by the editor himself, Thomas Davenport, and these are among the standout chapters - for their clarity and organization.

Since I work in the technology of analytics, I should be excused for either taking too technical a view of things, or for being too harsh in my criticisms. Having said that, there are at least quibbles that in my opinion leave this book only a middling, mediocre effort, and not one that will be remembered or consulted, much, if at all.

- The close and financial collaboration of at least a few technology vendors with the International Institute of Analytics means that most of the specific examples cited in this book are where the technology vendor's solution was used. Fair enough, but it leaves an aftertaste of an advertorial in the reader's mind.

- When discussing web analytics, there is a lot of ink devoted to the topic of "page views". Page Views are still relevant, but they are becoming increasingly obsolete in the world of AJAX - where parts of a page and its contents can be updated without having to reload the entire page. Web analytics metrics operate at the least granular level of pages, and hence cannot capture a significant chunk of user interactions and engagement that occur on pages and sites that make heavy use of such asynchronous page content refreshes (AJAX does stand for "Asynchronous JavaScript and XML", and no - it does not contain the buzzword "Agile"). More sophisticated measures of user engagement are being built that track more than simple page views. When the chapter's author fixates on page-views without once mentioning the inaccuracies of measurement that AJAX can inject, the credibility of the chapter suffers.

- The chapter on "NBOs", i.e. "Next Best Offers" takes several cheap shots at Amazon (see page 90), which left me wondering whether Amazon had not turned down six-figure consulting offers from either of the authors to warrant this broadside.

- There is a laboured chapter on "engagement" - an attempt to define a compound measure based on essentially a summation of basically arbitrary weighted base measures. For instance, a measure of online engagement is a summation of eight different indices. Putting a "sigma" symbol in the equation makes it look impressive, but in the end it is more arbitrary than methodical. Because decision makers need to have information supplied to them in a simple manner, it is often supplied to them in a simplistic manner, cloaked in technical-sounding phrases.

- Privacy is becoming an increasingly sensitive and relevant issue as more data is collected from customers and users, often without their knowledge, and sometimes without their consent, almost always without providing users a clear picture of what is done with that user-data so collected. Privacy is an important topic in this discussion on enterprise analytics. And it is given short shrift in the book. After a cursory nod, almost as an afterthought, to privacy concerns - use an "abitrary identification number" to anonymize, there are examples cited that are almost creepy in the extent they suggest the invasion of a user's privacy. Sample these: "The next generation of video game offers could have pictures of your friends or your tastes and interests built right in." Or "A company called Sense Networks has developed an application to help infer a person's lifestyle based on his or her location history." Harvesting a user's location and web-click information should require explicit opt-in - it is basic respect for human decency. Take the section where the authors talk about collecting data by anonymizing data. It has been proven that even after anonymizing data, it is possible to individually identify users with a very high degree of accuracy based on only a few attributes. There is no such thing as truly anonymous user-tracking on the web. Only if you use "unsophisticated marketing techniques" do you risk customer ire over harvesting their "spending habits", which they consider "inviolate". Get it? Be sophisticated, and you can get away with pillaging your customers' privacy. Is this really the new normal - customer privacy shmrivacy?

- Then there is this most curious statement that states - "Despite all the hype around the unstructured data component of "big data", it seems that structured data still rules the in predictive analytics." Well, yes! Unstructured data is fairly recent, especially when compared with structured data, that has been around for literally decades. It is but natural that the use of unstructured data in predictive analytics will take time to gain traction, especially as the technology and means of blending structured and unstructured data evolve.

- Even the chapter, "Predictive Analytics in the Cloud" contains phrases that serve absolutely no purpose other than to bump up the chapter's jargon-index. Sample this: "These cloud-based solutions inject predictive analytics into other software that is cloud-based or delivered as SaaS." I counted four buzzord phrases or words in this single sentence: "cloud-based", "inject", "predictive analytics", "SaaS". Would a specific example be too much to ask? "inject" is an impressive-sounding word, especially if you have heard of the phrase "sql-injection" when reading about hacking attacks, but just what does the word "inject" mean in the context of predictive analytics and the cloud? And why does this injection require the cloud? Can it not be done with more traditional, hosted solutions? And what exactly are "cloud-based dashboards"??? Is any dashboard served via a browser "cloud-based"?

I could go on an on, but a short summary of the book would be this: each chapter suggests and promises value, but falls short.