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Good Reading: Analytics at Work

Originally published April 6, 2010


The more you read, the more things you will know. The more that you learn, the more places you’ll go.
– Dr. Seuss

Frankly, I haven’t done a book report since fifth grade, but I just had to give this a shot. There is a really new good book out now called: Analytics at Work: Smarter Decisions Better Results. Written by Thomas Davenport, Jeanne Harris, and Robert Morison, it is a follow-up to Davenport and Harris’s Competing on Analytics.

When I read this book, I found I appreciated it for a number of reasons. First, it provides another tool in my unending quest to educate students and faculty about business intelligence (BI). Second, the title of the book reiterates the notion that analytics are not an end result in and of themselves, but a means for achieving something tangible for the organization – smarter decisions and better results.

What makes this book different from Competing on Analytics is that the authors now provide a means and a context for putting BI analytics to work. And this issue is important, especially when the authors cite that 40% of major decisions are currently based on managerial guts!

The fundamental premise of the book is that we always need to be thinking about how to become more analytical and fact based in our decision making and to use the appropriate level of analysis for the decision at hand.

In Part One of their book, the authors introduce their analytical “delta,” which is an acronym for their success factors for analytic initiatives:
   
    D = accessible, high quality data
    E = enterprise orientation
    L = analytical leadership
    T = strategic targets
    A = analysts

The authors dedicate a chapter to each DELTA letter. For example, when discussing data, the authors examine the importance of structure, uniqueness, integration, quality, access, privacy, and governance. Effective data management is stated to be a prerequisite for effective analytics, and the authors provide effective, commonsense reasons for their assertions, supported by real world examples that embellish their arguments.

Of course, not every company is at the same level of development with their BI efforts and the authors acknowledge this fact. Taking a page from Nolan’s stages-of-growth model that explained levels of enterprise IT maturity, the authors provide a similar model to describe the maturing analytical prowess of organizations. For example, the earliest stage of data maturity is where firms are gaining mastery of data importance, whereas in the most advanced stage, senior executives understand the competitive potential of data, unique data is exploited, and the organization employs strong data governance. A critical element in this maturation process is an increasing executive interest in the value of analytics. In organizations where data is problematic, executives may have little exposure to or interest in the value of analytics. However, in firms where data is highly valued, senior executives are said to exhibit a passion for analytics.

Each of the DELTA components is discussed in turn with evolutionary stage transitions being described in each component’s chapter. Each chapter ends with a bullet list of “Keep in Mind…” items that can help to guide the reader in facilitating the understanding and use of analytics in their organization.

Part Two of the book identifies three hallmarks of companies that are developing long-term analytical capabilities:
  1. Analytics are embedded in the major business processes of the enterprise.

  2. These companies build and reinforce a culture of analytical, fact-based decisions.

  3. They continuously monitor business conditions and review their operating assumptions and analytical models.
Each hallmark is then examined in detail. For example, when discussing the importance of embedding analytics in business processes, the authors distinguish between the “craft approach” to analytics versus the industrial approach. A craft approach is defined as an ad hoc effort in support of decision making whereas the industrial approach automates and inculcates analytics into decision-based work processes. These approaches are shown to differ on a number of important dimensions.

Industrial analytics can potentially be automated or assisted, but a firm must decide how extensively these analytically enabled decisions should be automated. The key, they note, is finding the right mix of fully automated decisions, automated decisions with human review, and human decisions informed by analytics.

While the authors describe the characteristics of an “analytical process nirvana,” they admit that few businesses today can claim to have realized this ideal state of analytical being. This gap then presents most firms with an opportunity to explore new ways and means for embedding analytics in action and for making more processes analytically driven. Moreover, since technology is an integral part of most business processes today, firms can and should assess the potential for their IT architecture to provide enhanced technologies and applications that can help to embed analytics into existing and new work processes.

The key message in this book is that data and analytics are steadily becoming more important and influential in every organization and that this trend is inexorable.

This is simply a very readable, interesting, and educational book. The authors write in a very accessible manner and their arguments make a lot of sense. The real-life examples they use are interesting and effective, and their strategies for developing and applying fact-based decision making are informative and directive.

Frankly, I found the book to be inspiring. I am already passionate about business intelligence, but I am always driven to try to educate my colleagues and those uniformed about what business intelligence is and what it can do for organizations. This book is one I would share with those people because it helps to articulate how to do it right, why it is important, what they can accomplish, and why there is a need for urgency.

I also find that the book helps to dispel the notion that business intelligence is just another technology-driven fad. Although the term business intelligence is not used in the title of the book (it is rarely even used in the text for that matter), I think it fair to say that applying analytics for decision making is what business intelligence is all about. That is, the focus of business intelligence is, in fact, effective data-driven decision making. Business intelligence is not a technology or a set of technologies. Business intelligence is what this book is all about!

SOURCE: Good Reading: Analytics at Work

  • Richard HerschelRichard Herschel

    Richard is Chair of the Department of Decision & System Sciences at Saint Joseph's University in Philadelphia. Before becoming an educator, he worked at Maryland National Bank, Schering-Plough Corporation, Johnson & Johnson, and Columbia Pictures as a systems analyst. He received his BA in journalism from Ohio Wesleyan University, his Master’s in Administrative Sciences from Johns Hopkins, and his Ph.D. from Indiana University in Management Information Systems. He has earned the Certified Systems Professional designation, and he has written extensively about both knowledge management and business intelligence. Dr. Herschel can be reached at herschel@sju.edu.

    Editor's Note: More articles and resources are available in Richard Herschel's BeyeNETWORK Expert Channel. Be sure to visit today!

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