The Three Big Limitations of Big Data
Big data comes with some big promises. However, this isn’t a tool with limitless capabilities. Making the most of analytics means understating the limitations that exist when harnessing the power of data. What are some of the big limitations that both experienced users and first-time data explorers are facing? Take a look at the three big limitations of big data.
Misinterpretation of Data
Data can reveal the actions of users. However, it can’t tell you why users thought or behaved in the ways that they did. This can be frustrating for marketers and enterprises trying to capture lightning in a bottle. What’s more, relying solely on data to make assumptions could lead an enterprise to start acting based on false correlations. It can even land an enterprise in hot water. For instance, the Princeton Review recently faced criticism because a geographic pricing algorithm used for its SAT tutoring products resulted in people belonging to specific ethnic groups being charged more. The reality is that discerning detected correlations and trying to answer the right questions based on data is a separate job from collecting and processing data. A company may not have the resources, capabilities or staff to sift through real-time streaming analytics or data reports to analyze actionable insights for both short-term and long-term business strategies. Most companies will need to bring in a third-party analytics firm or marketing firm to take over at that point. However, this leads into the second big limitation of big data.
Big data also faces limitations because of security concerns. Companies that collect data are tasked with the big responsibility of safeguarding that data. The consequences of a data leak can include lawsuits, fines and a total loss of credibility as a start. Security concerns can greatly inhibit what you are able to do with your data. For instance, having data analyzed by a third-party group or agency can be difficult because your data may be hidden behind a firewall or private cloud server. This can create some big hiccups if you’re trying to share and transfer data to be analyzed and acted on in a consistent manner.
The Outlier Effect
The third big limitation that goes along with data is that outliers are common. A glitch on the user end or a new upgrade in a popular search engine can create some skewed results once your data is processed and analyzed. The reality is that technology just isn’t at a point yet where the methods for gathering data are completely precise. Google Flu Trends famously highlighted the limitations of big data a few years ago. You may remember that the program was created to provide real-time monitoring of global flu cases by taking data from search queries related to the flu. The promise was that monitoring search engines in real time could help researchers track flu cases much better than the Centers for Disease Control (CDC) can do using slower data sources. However, it was Google’s own algorithms and inability to properly predict search behaviors that turned the project into one of the company’s most spectacular failures to date.
How Are Enterprises Dealing With Big Data’s Limitations?
The only way to bridge the gap between big data and real insights is to keep a human element in the mix. Technology simply isn’t at the point where we can allow data to analyze information and make decisions without injecting human judgment. Using a streaming analytics platform that provides reports and visuals can be a big help because it allows decision makers to grasp what data is truly revealing.