Disadvantages of Big Data Analytics

Disadvantages of Big Data Analytics

Disadvantages of Big Data Analytics

According to a survey conducted by TDWI (The Data Warehousing Institute), there were certain drawbacks of the Big Data analysis, including: lack of human resources and skills (46%), difficulty in the architecture of a Big Data analysis system (33%), problems with Big Data usability for end users (22%), lack of business sponsorship (38%) and lack of a convincing business argument (28%), lack of database analysis (32%), , Big Data scalability problems (23%), quick queries (22%) and difficulty loading data quickly enough (21%), among others.

Real-time big data demands the ability to conduct sophisticated analyses; companies who fail to do this correctly open themselves up to implementing entirely incorrect strategies organization-wide. Furthermore, many currently used data tools are not able to handle real-time analysis.

Using real-time insights requires a different way of working within your organization: if your organization normally only receives insights once a week, which is very common in a lot of organizations, receiving these insights every second will require a different approach and way of working.

If data masking is not used appropriately, big data analysis could easily reveal the actual individuals who data has been masked. Organizations must establish effective policies, procedures, and processes for using data masking to ensure privacy is preserved.

The data files used for big data analysis can often contain inaccurate data about individuals, use data models that are incorrect as they relate to particular individuals, or simply be flawed algorithms