Analytics At Work: Smarter Decisions, Better Results





Analytics At Work: Smarter Decisions, Better ResultsIn recent years the business world has increased its reliance of processes, data management, and computer systems for the best operational and marketplace advantages. Thus, companies have sought to link information with strategic decisions.

According to a survey described in the new book from Thomas Davenport and Jeanne Harris, Analytics At Work: Smarter Decisions, Better Results, “two-thirds of large US companies researched believe they need to improve their enterprise analytics capabilities” and “nearly three quarters said they are working to increase their company’s business analytic usage.”

Thomas Davenport and Jeanne Harris are not strangers to analytics. I have read their previous book, Competing On Analytics, which examined the top performing analytics-based corporations, like Harrah’s, Netflix, and Progressive Insurance, and how each deployed analytics as a competitive advantage in their respective industries. Analytics At Work came to be as the authors discovered that readers of Competing “worked in companies that did not want to be analytical competitors….  They believed that making decisions on facts and analytics was beneficial, but they didn’t necessarily want to build their companies and value propositions around doing so.” Thus Analytics At Work focuses on improving operations based on business intelligence.

I received a review copy from Davenport, a distinguished Babson College IT professor named one of world’s top consultants by Consultant magazine, and Harris, an executive research fellow at Accenture who received a Women Leaders in Consulting Lifetime Achievement award in 2009. With Robert Morison, a highly accomplished researcher  on the subject of business, technology, and human resource management, the authors of Analytics at Work succeed at furthering the business case for analytics.

Success Factors needed for an Analytics Culture

The first chapters describes five success factors, called DELTA. The elements of DELTA are:

  • Data — quality, accessible clean information
  • Enterprise — enterprise orientation
  • Leadership — having an analytical leadership, not just allowing ad-hoc analysis
  • Targets — strategic targets, where the analytics will be applied
  • Analysts — to “build and maintain models that help the business meet its analytical targets”

Companies undergo stages in implementing the DELTA elements. The five stages are (with grossly short descriptions):

Stage 1: Analytically Impaired — Organization lacks one or more DELTA elements
Stage 2: Locallyzed Analytics — Pockets of analytical activity uncoordinated through the organization
Stage 3: Analytically Aspirations — Implementing organization-wide analytical capabilities slowly
Stage 4: Analytical Companies — Company is grounded in analytics, but no competitive advantage in the industry is evident
Stage 5: Analytical Competitors — Company is grounded in analytics, with a competitive advantage in its industry

The stages were first introduced in Competing, but Analytics At Work connects the DELTA element details in a how-to methodology. Lacking any of the DELTA elements creates roadblocks such as overspending on data acquisition, uncoordinated measurement systems that increase complexity costs, and project delays.

Great way to show the best way analytics can work

For mid-size organizations needing an organizational primer for its analytics efforts, the book explains well without salesman dramatics or overwhelming terminology. For example, the Analyst chapter declares the skill differences among a Champion, Professional, and Semi-Professional analyst, then offers short asides from Best Buy and Blue Cross-Blue Shield analysts. Even Will Smith is included as a semiprofessional to show how someone who is not a traditional analyst can deploy some analytical behavior for an advantage (seems analytics practitioners have a lot of love for the rapper-actor; see my comments in the Web Analytics 2.0 book review). There are similar profiles for leadership as well.

A godsend are the book’s Enterprise and Target chapters. The text shows solid how-to’s beyond the “get-good-data-and-analysts” statements that many companies read & hear, but rarely act upon. Commentary on the typical role of Information Technology departments are just awesome. Many businesses unintentionally allow IT to be a sole gatekeeper of implementing analytics solutions. But “without an analytic strategy and road map, most IT organizations will struggle to anticipate and support business requirements.” Analytics must involve other departments to make the data useful for analysis and decision.

Throughout the book, and particularly in the Enterprise chapter, the authors explain how analytics is integrated through an organization. When examining the actions of analytical leaders, the authors suggest developing analytic skills of employees and being inquisitive for more analysis. Examining the Enterprise element requires leaders to break silos to reduce cost. An example from Best Buy showed that “by streamlining 293 analytical systems and data feeds…it could improve quality and cut costs.”  Avis’ European operations, another example, used analytics to forecast precisely its utilization, which matches demand for its rental fleet to vehicle locations and thus generate revenue.  The result helped Avis “increase its fleet utilization…by $19 million.”

The “right bowl of porridge” for everyone

I like Analytics At Work, mostly because the research and benefits are explained well.  Just like in the tale of  Goldilocks, the text is the “right bowl of porridge” for readers. It is never too scholarly for the mid-sized manager who wants to skim to the bottom line.  It’s never too shallow for those expecting an in-depth approach. A convenient appendix table summarizes the book’s first half on the success factors and stages.

The authors do not try to sell analytics as the ultimate business panacea. The chapter “Toward More Analytical Decisions and Better Results” gets right the art and science in analytics by stating “what we promise and what we don’t.” There are segments about potential pitfalls, while other segments help readers to understand when analytics is impractical. But the authors are certain of analytics’ value:

“The underlying power of analytics comes in making connections — recognizing patterns in business activities, isolating drivers of performance, and anticipating the effects of the decisions and actions.”

Analytics At Work will serve as the informed guide that will move your organization very quickly to profitable actions.

9 Comments ▼

Pierre DeBois - Associate Book Editor


Pierre DeBois Pierre Debois is Associate Book Editor for Small Business Trends. He is the Founder of Zimana, a consultancy providing strategic analysis to small and medium sized businesses that rely on web analytics data. A Gary, Indiana native, Pierre is currently based in Brooklyn. He blogs about marketing, finance, social media, and analytics at Zimana blog.

9 Reactions

  1. Prasad Thammineni

    Davenport’s both books are targeted at enterprises and draw upon examples from enterprises where resources are in abundance to measure, analyze and take action on data. I would love to see research done on how small businesses can do the same but using their limited resources.

  2. Pierre DeBois

    Hi Prasad,

    You raise a good point that Davenport draws examples from enterprises larger than a typical small business. But it does not mean that the resources are abundant (There is a shortage of good analysts; Moreover, not all companies value analytics the same way, also how AatW was created).

    Small businesses do use “analytics”, it’s just that (a) we don’t see our behavior as analytics-oriented in nature, and (b) the sources we have available are not integrated. An example is when we sync solutions together to give us a fairly good picture of an interest. Anita has a great example in one of her past presentations regarding syncing Freshbooks with Outright, Expensify, etc. to gain an instant track of expenses. Now a SMB is not using enterprise – level solution in that example, but it is using some facets of DELTA if the information is being used to make strategic decisions quickly.

    Moreover, many small businesses have been introduced to analytics through web analytics, so I think the closest analytics data would be there. I am not familiar with a particular study, but the best source that may have small business study would be the Web Analytics Association.

    Finally, the use of analytics in small businesses — and I am going on Pierre-tuition here! (no data, shame on me! 🙂 ) — is still embryonic because so many businesses associate website for the data, when the data in some situations can be used for inferring a bit more. For example, I recall an owner making a software development planning decision based on the OS of website visitor data in Google Analytics. That’s a great use of GA beyond keyword-adsense usage. And I know I have used GA data to help guide suggestions and develop ideas based on how visitors came to a site from offline events.

    There are many who have worked with web analytics longer than me that will certainly say there has been a lot of sophisticated refinement for small business available. Yahoo! Web Analytics has made some inroads making enterprise-level analytics affordable, while GA has offered better features. There is fertile soil for predictive analytics, and I am sure all the solution players are working to develop more features that can take advantage of it. Again, no data here. 🙂

  3. @prasad, we’re trying to solve the analytics problem for much smaller business like the ones with less than 25 employees, they’re the ones that can see lot of immediate benefits. Check our analytics module demo. http://www.lytecube.com/demos.html

  4. Anybody have a 5-page book report on this Book? Please email me at kyreemarshall@comcast.net

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