Disadvantages of Big Data Processing

1. Lack of talent

The lack of big data experts and data scientists has been the biggest challenge in this field for the past three years. Currently, many IT professionals don’t know how to carry out big data analytics as it requires a different skill set. Thus, finding data scientists who are also experts in big data can be challenging.

Big data experts and data scientists are two highly paid careers in the data science field. Therefore, hiring big data analysts can be very expensive for companies, especially for startups. Some companies have to wait for a long time to hire the required staff to continue their big data analytics tasks.

2. Security risks

Most of the time, companies collect sensitive information for big data analytics. Those data need protection, and security risks can be demerits due to the lack of proper maintenance.

Besides, having access to huge data sets can gain unwanted attention from hackers, and your business may be a target of a potential cyber-attack. As you know, data breaches have become the biggest threat to many companies today.

Another risk with big data is that unless you take all necessary precautions, important information can be leaked to competitors.

3. Compliance

The need to have compliance with government legislation is also a drawback of big data. If big data contains personal or confidential information, the company should make sure that they follow government requirements and industry standards to store, handle, maintain, and process that data.

So, data governance tasks, transmission, and storage will become more difficult to manage as the big data volumes increase.

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