5 Things We All Hate About Data Analytics

Nov 7, 2017

Data analytics has become one of the most important aspects of modern business. They are put to various business applications to help companies innovate and improvise in their operations. It has become highly essential for companies with a huge customer base to use customer data effectively for making strategic business decisions. However, on the flip side, data analytics can prove to be a bane as well. Curious to know why?

Breaking the Privacy Code

Data analytics dives deep into the details of customers based on the information provided by them and their shopping behavior. The customers are not aware that the businesses are using their personal information to market products to them.

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Right Answer for The Wrong Question

Data analytics can help companies to arrive at various correlations using an array of questions. However, the challenge here is to identify the right questions. Big data analytics becomes completely meaningless if the user is not able to formulate the right questions.

Not A Laymen’s Cup of Tea

Much of the data used for big data analytics lies behind a firewall or a private cloud; therefore, it requires adequate technical-know-how to retrieve essential data from the database and put this data for various analysis. Also, transferring data to specialists for repeat analysis may be a difficult task to do on a regular basis.

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Finding the Right Correlation

Big data tools can sift through unstructured data to find correlations in data sets. However, not all the correlations are substantial and meaningful. A company needs to identify correlations that have a meaningful relationship and would be useful for the business for making strategic business decisions.

Accuracy Issues

While it cannot be denied that big data analytics tools are powerful, the predictions and conclusions drawn from this data need not always be accurate. Big data analytics tools can use inaccurate data about individuals or use flawed or incorrect data models while extracting data from the database. As more data is added to the database, the data sets become complex; thus, increasing the risk of errors in the analysis.

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