An ingredient that’s sometimes missing from otherwise-worthwhile business books is the “how,” as in, how to do what the author or authors say it is you should be doing. The writers of the new book Analytics at Work seem to have understood this fact well and have filled their book with solid, actionable advice.

Authors Thomas H. Davenport and Jeanne G. Harris have written (this time with the help of Robert Morison) a follow-up of sorts to their 2007 book Competing on Analytics. This newest effort arose from a feeling that they had missed a large audience with the first book: firms who wanted to add more analysis to their day-to-day approach but who didn’t want to build their companies or value propositions around analysis.

Much of interest

Analytics are spoken of in necessarily broad terms and the analysis of marketing research data is but one small part of the whole data picture presented in the book, but researchers will certainly find much of interest here.

(The book’s list of typical logic errors in decision-making will sound very familiar to researchers: not asking the right questions; making incorrect assumptions and failing to test them; using analytics to justify what you want to do [gaming or rigging the model/data] instead of letting the facts guide you to the right answer; failing to take the time to understand all the alternatives or interpret the data correctly.)

Despite the 30,000-foot view, the authors do a great job of elaborating on their observations with real-world examples from a range of entities, from unexpected ones like the Royal Shakespeare Company to data-loving stalwarts like Best Buy and Nike. They write in an engaging style, acknowledging the potential dryness of the subject matter while explaining complex concepts in clear, digestible prose.

Examining the role

They spend the first half of the book examining the role of data and data analysis within organizations, using the acronym DELTA to describe the factors necessary for success in putting analytics to work: D for accessible, high-quality data; E for an enterprise orientation; L for analytical leadership; T for strategic targets; and A for analysts.

The “Targets” chapter, for example, looks at the whole notion of what should be tracked and analyzed and how to determine what to track and analyze, given your internal capabilities, your needs and your industry’s data-use practices.

In “Analysts” they examine the qualities of those who are analytics-minded, looking at everyone from actor Will Smith to a Best Buy district manager, and discuss how to motivate, organize and manage analysts within a firm.

They also explore the assessment of your organization’s status as an information-gathering entity, laying out the five-stage model of progress, which readers of Competing on Analytics may recall, and talking about the factors required to move from stage to stage.

Later chapters outline analytical process nirvana, seven sticking points to embedding analytics into business processes (and how to overcome them) and tips on building the business appetite for analytics, satisfying that appetite and encouraging a healthy analytical diet across an organization. The helpful “Keep in Mind…” sections that close many of the chapters are good reference points for how to put each chapter’s insights into practice.

Best road map

It’s not exactly a step-by-step approach (each industry likely warrants its own book of specific techniques and examples) but for the employee who wants to start doing his or her part to move an organization toward a focus on analysis, this is certainly the best road map I’ve come across.

And in fact, after acknowledging they’re guilty of a previous level of over-attention to the role and impact that the CEO can have on increasing a company’s use of analytics, the authors argue that the drive toward greater use of analytics doesn’t only have to come from the top. A data-loving CEO doesn’t hurt, of course, but much can be done by those who are below C-level to champion the use of analytics.

To show what can be accomplished by CEOs and non-CEO types, they look at four case studies of analytical leaders at different levels within their respective organizations - the head of an analytical department; the head of a business function; a business-unit head and entrepreneur; and a CEO/president team - and also detail the specific behaviors of analytical leaders (set a hands-on example; sign up for results, etc.).

Non-violent data revolution

Obviously organizational differences and variables are great, and no one book can answer all questions specific to each reader’s unique situation. But there is enough here, such as in the enterprise chapter, which contains broad steps to take if you are a “lonely proponent of analytics” within your firm and wish to start a non-violent data revolution, to get you started on the path of better data analysis.

With the book’s singular focus, the authors’ acknowledgements that analysis isn’t everything and that analytics can be wrong, etc., are welcome. They proselytize, to be sure, but with the admission that an analysis of any kind is only as good as the skills of those doing the analyzing and the quality of the decisions and actions that result from it. Reading this book should help you improve your chops in each of those areas.

Analytics at Work: Smarter Decisions, Better Results (240 pages; $29.95), by Thomas H. Davenport, Jeanne G. Harris, Robert Morison, is published by Harvard Business Press (http://hbr.org/books).