“Big Data” has been one of the fastest fads in high tech history. It’s been roughly a year since it burst on the scene as the next big thing in information technology.

There’s been a lot of hype surrounding Big Data given the many benefits promised. Yet, I’m increasingly convinced, that companies big and small have noticed (how could they not?) the hoopla surrounding Big Data and, wanting to get on the bandwagon — and afraid of being left behind – they’ve rushed out and bought into the technology without really thinking about what they’ll use it for.

That is an expensive mistake, one that will likely lead to frustration. It’s hard to use a technology if you don’t know how valuable it is, and where that value lies.

The fact is that, counterintuitive as it may be, most of the value in Big Data lies not in the quantitative data itself, but in the qualitative questions it answers. It is not in how much data you can now crunch, but in how much better are the decisions that you make each day. That difference can be the hardest thing for companies to get their heads around – and why they don’t see a return on their Big Data investment.

In the real world, it’s easy to quantify hard costs: you save a penny on each call center call and there is a tangible impact on your bottom line. But what’s the value of better serving all of my customers? That is, working on the right customers, on the right deals, the right projects and, ultimately, on the right strategy? How do you value that?

We all profess to believe in the Pareto Principle, the so-called 80/20 rule, but when it comes to Big Data analytics, a shocking number of companies completely ignore it – and instead just crunch everything, to be applied everywhere – and then complain that they aren’t seeing useful results. By comparison, those companies that focus on capturing useful data and then applying it in useful applications – that is, analyzing that 20 percent to make it even more productive – are the ones that are finding great success with Big Data, pulling away from their competitors and further increasing the frustration of those who have failed to see the same results.

The challenge, then, to succeeding with Big Data lies not in measuring something – it’ll do all that and more – but in determining the value of that measure. The bad news is that making this determination is different with every company. The good news is that there are established places to look. Here are six suggestions:

1. Know your business. Yeah, that sounds easy, but Big Data is a lot tougher than it looks. It means understanding, on any given day, all of the decisions big and small that get made in your organization — and then having some sense of how important each of those decisions are to the success of the enterprise. Do you know which ones matter? If not, a good rule of thumb is to ask yourself: is it worth it to pay a lot for that good employee to make that decision? That’s a good proxy for value – and likely a good place for analytics. By comparison, if you’d only pay a minimum wage employee to make that decision, it’s probably not a good place for analytics. In other words, anywhere you think good employees matter, analytics probably matters as well.

2. Know the difference between a good decision and a bad decision. And use analytics to determine that difference. For example, analytics can help you call on a customer who will buy your offering versus one who won’t. And if you can make that decision correctly just twice as often you will quickly pay for your Big Data investment by increasing sales yield without having to increase sales cost.

3. It’s not about the software. Rather, it’s about knowing which decisions you can make better, every day, if you can get just the right data, and knowing what the value of making those decisions would be. I can’t tell you much more than that because every company is different. But what all successful Big Data solutions have in common is that they put the qualitative before the empirical. That is something you’ll likely need to spend some time pondering before you start writing big checks.

4. Pretty pictures aren’t the answer. They are a lot of cool analytical tools out there with good-looking user interfaces and results presentations. Most of them also do their job quite well. . . if they are used for the right applications. Focus on the right fit for your particular needs. Focus on the application. Focus on the data, not on the pretty pictures; go with what works, not merely what is easy or pleasing to use.

5. Make sure incentives are aligned. Employees do what they are paid to do, and they don’t do what they aren’t paid to do. That may seem self-evident; yet an astonishing number of companies fail to align their rewards with their expectations – and then can’t understand why their employees don’t do their ‘job.’ The implementation of Big Data in many companies is a prime example of this disconnection: if your people aren’t being paid to make better decisions – that is, to make decisions based on the results of Big Data analytics – then why would they use those tools? On the other hand, if you are willing to pay more, and align incentives, you’ll inevitably see the kind of Big Data-driven productivity you’ve been looking for.

6. It’s hard to recruit new talent. That part you already know. So what does that have to do with Big Data? Well, what you’ll discover, once you figure out how to use your Big Data solution properly is that analytics can help you fill the talent gap – if you’ll let it. An enterprise that applies Big Data effectively to solve its most value-added challenges, and that enlists employee engagement through the proper application of incentives, soon finds that it can grow without adding the usual amount of staff. That means more profits, increased competitiveness – and enables Big Data to fulfill its promise at last.

Big Data is about great numbers, but even more it is about great decisions. When you know what are the most important decisions you need to make, then Big Data, powered by the right analytical tools, can be the single most powerful competitive tool in today’s high-powered competitive marketplace.