Big data. It's the latest IT buzzword, and it isn't hard to see why. The ability to parse more information, faster and deeper, is allowing companies, governments, researchers and others to understand the world in a way they could only dream about before.

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All that is true. And yet…
It's also true that in our rush to embrace the possibilities of big data, we may be overlooking the challenges that big data poses—including the way companies interpret the information, manage the politics of data and find the necessary talent to make sense of the flood of new information.
Big data, in other words, introduces high stakes to the data-analytics game. There's a greater potential for privacy invasion, greater financial exposure in fast-moving markets, greater potential for mistaking noise for true insight, and a greater risk of spending lots of money and time chasing poorly defined problems or opportunities.
Unless we understand, and deal with, these challenges, we risk turning all that data from something that has the potential to enhance our organizations into a diversion, an illusion or a paralyzing turf battle.
Let me be more specific about those challenges.
These Are Great Tools, but Who Has the Skills to Use Them?
Getting people qualified to work in such data-analytical tools as Hive, Pig, Cassandra, MongoDB or Hadoop is only the first layer of this onion. Few companies have in-house experts who can even make a business case to justify the cost of hiring big-data experts, let alone assess the quality of the applicants. Many managers also lack basic numeracy, so getting decision makers who can grasp more sophisticated statistical mechanics can be a challenge.
Complicating the matter, big-data tools aren't ready for prime time: They are evolving rapidly, aren't taught in most universities, have less-than-ideal vendor support and require levels of user flexibility that more mature tools don't. That makes finding the right people all the more crucial.
Here's another layer of the onion: For big data to be useful, programmers and analysts also must understand the basics of the industry they are programming for. Imagine, for instance, that data analysts at a pharmaceutical company see a spike in aspirin sales in January as measured by point-of-sale data in near real time. Aha, they say, flu season is intensifying. But before committing sales resources to a big campaign and increasing production, it's worth comparing sales patterns to past years. Maybe lots of people had hangovers after their New Year's Eve parties. If the analysts don't know the business, and the questions to ask, the company risks running down a lot of expensive dead ends.
One final layer is IT security. If it's true that many companies don't have the skills to work with big-data tools, they certainly don't yet have the skills to keep that data secure. As more information is gathered, that's more information that can be leaked or stolen.
Information Is Power. So a Lot of Information Is a Lot of Power.
Control over information is frequently thought to bring power within an organization. Clearly, whoever gets to make decisions about what gets measured in the big-data era will accumulate even greater power.
Moreover, information sharing across organizational boundaries, which is part of the nature of big data, can upset traditional power relationships.
Consider a company with a Canadian plant and a U.S. plant. Streaming data from sensors shows the Canadian plant is churning out engines with 97% reliability, while the U.S. plant's engines clock in at only 80%. Suddenly, the managers of the Canadian plant may see their star rise in the organization—to the consternation of those in the U.S.
The bottom line might benefit, but the internal politics is something that executives must be prepared to manage.
Or consider that big data offers the opportunity to measure what previously was unmeasurable. If a large retailer can now more quickly and easily measure consumer reaction to various marketing campaigns—whether it be a Super Bowl commercial, magazine coupon or newspaper ad—the different stakeholders might find their relative positions within the organization change. They also may resent the social-media team for offering proof with click-through data of their ability (or inability) to move the revenue needle.
Such upheavals will be exacerbated by the fact that processes that traditionally take months to plan and execute might be assessed in minutes. People with years of experience with annual sales reviews often struggle with weekly or even daily revenue tallies. Those who ruled under the old way of doing things might find themselves falling behind in the new world.
Just Because Something Can Be Measured Doesn't Mean It Should Be Measured
Once people know that information is power, they may try to game the system—to the detriment of the company.
Say a large company starts tracking website traffic as a function of Twitter mentions. The results are updated continuously on an executive dashboard.
The manager of one sales team previously had, with great success, generated most of its leads and eventual sales from trade shows and conferences. But once Twitter mentions become the key metric being measured, the manager changes the department's focus, declaring, "We need to win the dashboard." The result is that the department may indeed win the dashboard, but it leads to an unprofitable emphasis on website clicks and social-media traffic with unqualified leads rather than on successful events.
What Do We Do With All These Numbers?
Standard databases have been around for about 35 years, so a substantial body of experience makes these tools relatively easy to understand and use. Big data, by contrast, is just being invented, so the techniques for organizing and understanding the underlying meaning are still in their infancy.
What's more, it isn't easy for us to make sense of information at this scale. "One, two, three, many" is how the security guru Bruce Schneier summarizes many people's math acumen. Spreadsheets, still the main tool for quantitative analysis in many companies, can't remotely scale to convey the number of cars on the road at a given moment in a particular city, or this week's federal spending on transportation projects.
Visualization can be extremely helpful with this kind of data, but the field is still immature, and its special language not widely understood.
The Challenge of Thinking Big
What does it mean to think at such a large scale? How do we learn to ask questions of the transmission of every car on the road in a metropolitan area, of the smartphone of every customer visiting a large retail chain, or of every overnight parcel on a delivery truck? How can more businesspeople learn to think probabilistically rather than anecdotally? Thanks to the book and movie, the Moneyball approach is by now well known among sports fans. But they'll also recall how that approach upended an organization and was copied by competitors.
In some ways, it requires a whole new way of looking at the world.
But also, the principles of good management extend to the domain of big data. Before businesses can profit from big data, managers must refuse to get lost in the noise that can obscure the basic forces represented by customers, value and execution. The volume, velocity and variety of big data can feel foreign, and make it easy to be dazzled by numerical tsunamis.
So it's always crucial to insist on the basics of sound analytical practice. And to remember: Numbers can tell you things you never even knew to ask. But they never speak for themselves.
Dr. Jordan is a professor at the Smeal College of Business at Penn State University. He can be reached at reports@wsj.com.
Corrections & Amplifications 
In an earlier version of this article, the name of MongoDB was incorrectly given as MongoDb.