Gartner recently predicted that “through 2017, 60% of big data projects will fail to go beyond piloting and experimentation and will be abandoned.” This reflects the difficulty of generating value from existing customer, operational and service data, let alone the reams of unstructured internal and external data generated from social media, mobile devices and online activity.

Yet some leading users of big data have managed to create data-driven business models that win in the marketplace. Auto insurer Progressive PGR -1.22%, for instance, uses plug-in devices to track driver behavior. Progressive mines the data to micro-target its customer base and determine pricing in real time. Capital One, the financial services company, relies heavily on advanced analytics to shape its customer risk scoring and loyalty and offer optimization initiatives. It exploits multiple types of customer data, including advanced text and voice analytics.

I believe what most people miss when they hear these success stories is the amount of human capital that gets thrown at these problems. Hundreds of data scientists create thousands of models, of which very few are actually incorporated into final production. The reason the Gartner stats ring true is most companies don't have the kind of resources to throw at the problem and most companies won't realize an ROI even if they could throw these types of resources at a problem.

Promise 1: The technology will identify business opportunities all by itself.

This is the direction the technology is moving towards, but it is not there yet. The technology enables a group of data scientists to identify the opportunities, it's not magic.

Promise 2: Harvesting more data will automatically generate more value.

The temptation to acquire and mine new data sets has intensified, yet many large organizations are already drowning in data, much of it held in silos where it cannot easily be accessed, organized, linked or interrogated.

More data does not mean better ROI on your initiatives. In fact, most companies don't take advantage of the data they already have to generate the maximum ROI. I always use a rule of thumb when purchasing new technology. If as an organization you don't believe you are already using the technology you currently posses to its fullest, then its not time to move on to something better. Your current technology should be preventing you from innovating, if its not then you either have the wrong technology or the wrong people.

Promise 3: Good data scientists will find value for you.

To profit consistently from big data, you need an operating model that deploys advanced analytics in a repeatable manner. And that involves many more people than data scientists.

Remember, data + insight = action. Actionable data is a combination or art and science. data scientists provide the science, however you need the team with the business acumen to provide the insight, this is the art. Data scientists will create a lot of questions that you never thought to ask of your data, but they cannot provide a solution in and of themselves.

Remember to walk before you run when it comes to data initiatives. It's always good to have a goal of using "big data" to improve your business and create ROI from where it didn't previously exist, however the journey to "big data" is more important. These examples of success with "big data" did not happen over night. They happened because advanced companies were butting up against the limits of their current technology and they were ready to take the next step.