Big data is a big topic lately. But for all the talk, there’s very little action. In truth, big data can be a difficult topic to wrap your head around. Discussions either take the 20,000-foot perspective of how great it will be with few specifics, or take the down-in-the-weeds view about the merits of Hadoop vs. Hive. There’s very little conversation in between.

But now there is.

Data Divination: Big Data Strategies, by Pam Baker and Bob Gourley, provides the important middle ground that explains just what big data is and what you can do with it. The book gets specific, devoting chapters to particular industries, but doesn’t get too granular on any particular tools, meaning the information will not be irrelevant in six months.

Big data continues to rank on trends lists, even though its promise generally hasn’t been fulfilled. Consequently, it is starting to slip down the Gartner Hype Cycle, going from the Peak of Inflated Expectations to the Trough of Disappointment, where it will probably languish in a year or two.

As numerous reports have demonstrated, companies enamored with the notion of “big data” continue to collect data without any clear idea of how to do the analysis that might lead to insight. Perhaps the hope is that it will reach some critical mass and generate some sort of Lamarckian insight all by itself.

Unfortunately, it doesn’t usually work that way. Addressing a number of the major bugaboos associated with big data, Data Divination offers a lot of actionable information, including:

How to acquire a staff: We already know that it can be difficult to recruit big data experts; in fact Gartner predicts that only a third of big data IT jobs will be filled by 2015. So the book offers tips on picking out staff members—would-be data whisperers—who might be good at big data even if they aren’t fully trained.

How to explain it to executives: Most executives fall into camps on opposite ends of the spectrum. Either they see big data as a crystal ball that will tell them everything, or they see it as just a really big spreadsheet. The book offers insights on how to approach executives in either camp.

How to create reasonable big-data goals: One of the obstacles is determining a reasonable problem for big data to solve. The book talks about how you can figure out questions you want to answer, then determine which data you have. It also explains how to get other (usually free!) data to help answer the questions. There are even ideas on being realistic about return on investment (ROI) expectations. You need to figure out ahead of time what you’re going to do with the big data insights and make sure you have a plan of action. Otherwise, the project is useless.

How to deal with privacy and security: Given the current conflict with European data laws and the issue of data sovereignty, this topic is a critically important moving target. The book reviews the history of security and privacy in the context of user data, and particularly how it could make your company liable (including some pretty hair-raising stories about potential big data applications).

The book concludes with a series of chapters on big data use cases in ten industries, including everything from intelligence, government, security, and healthcare to small business and farming. Farmers use big data? Who knew?

The last chapter offers a 20-page summary of the entire book—ostensibly for executives—but it’s a great way to scan for chapters that are most relevant to your business.

Most of all, Data Divination helps break the “analysis paralysis” logjam that has so many companies obsessively collecting data but fearing action because there’s no clear strategy. Chances are, by the time you finish reading this book, you’ll have some big data ideas—as well as a plan for how to get buy-in from management.

Simplicity 2.0 is where we examine the intricate and transitory world of technology—through a Laserfiche lens. By keeping an eye on larger trends, we aim to make software that’s relevant to modern day workers, rather than build technology for technology’s sake.

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