By now, you’ve probably heard of big data analytics, the process of drawing inferences from large sets of data. These inferences help identify hidden patterns, customer preferences, trends, and more. To uncover these insights, big data analysts, often working for consulting agencies, use data mining, text mining, modeling, predictive analytics, and optimization.

As of late, big data analytics has been touted as a panacea to cure all the woes of business. Big data is the key that unlocks the door to growth and success. Consequently, some experts predict that during 2015, the average company will spend about $7 million on data analysis. However, although big data analytics is a remarkable tool that can help with business decisions, it does have its limitations.

Here are 5 limitations to the use of big data analytics.

Prioritizing correlations

Data analysts use big data to tease out correlation: when one variable is linked to another. However, not all these correlations are substantial or meaningful. More specifically, just because of correlation between 2 variables, it doesn’t mean that a causative relationship exists between them (i.e.,“correlation does not imply causation”). For instance, between 2000 and 2009, the number of divorces in the U.S. state of Maine and the per capita consumption of margarine both similarly decreased. However, margarine and divorce have little to do with each other. A good consultant will help you figure out which correlations mean something to your business and which correlations mean little to your business.

The Wrong Questions

Specialists use big data to discern correlations and insights using an endless array of questions. However, it’s up to the user to figure out which questions are meaningful. If you end up getting a right answer to the wrong question, you do yourself, your clients, and your business, a costly disservice.

Security

As with many technological endeavors, big data analytics is prone to data breach. The information that you provide a third party could get leaked to customers or competitors.

Transferability

Because much of the data you need analyzed lies behind a firewall or on a private cloud, it takes technical know-how to efficiently get this data to an analytics team. Furthermore, it may be difficult to consistently transfer data to specialists for repeat analysis.

Inconsistency in data collection

Sometimes the tools we use to gather big data sets are imprecise. For example, Google is famous for its tweaks and updates that change the search experience in countless ways; the results of a search on one day will likely be different from those on another day. If you were using Google search to generate data sets, and these data sets changed often, then the correlations you derive would change, too.

Ultimately, you need to know how to use big data to your advantage in order for it to be useful. The use of big data analytics is akin to using any other complex and powerful tool. For instance, an electron microscope is a powerful tool, too, but it’s useless if you know little about how it works.