Challenges in big data management 

With a huge explosion of data in several organizations, businesses have a keen interest in exploring solutions that provide opportunities and insights to increase profits in the business. However, it is still difficult to manage and maintain big data. Some of the major challenges in the big data management process are stated as follows:

• Expanding data stores: Having an enormous volume of data involved, and the fact that it is continuously growing over time, makes data management very complex and challenging. It is also very critical to perform any sort of operation on this dataset as it can hinder the quality and performance of the analysis. It can be very complex to move a database into an analytical solution due to continuous expansion in data stores and data silos.
• Data and structural complexity: Enterprises typically have both structured data and unstructured data, and that data resides in a very wide range of formats, including JSON, CSV, a document file, a text file, or BLOB data. An enterprise generally has several thousand applications on its systems, and every one of these applications might scan from and write to several distinct databases. As a result, simply cataloging what styles of data an organization has in its storage systems is often extraordinarily tough.
• Assuring data quality: It is one of the essences for enterprises to ensure data reliability and accuracy. As mentioned, the deficit of synchronization across data silos and data warehouses can make it complicated for managers to understand which part of the data is accurate and complete. If a user enters the wrong data, the generated output is also incorrect. This is referred to as garbage in, garbage out (GIGO). This type of error is referred to as a human error. 
• Low staffing: It is difficult and challenging to find qualified staff with decent knowledge about the problem domain. A lack of data scientists, database administrators (DBA), data analysts, data modelers, and different big data professionals makes the job of data management very challenging. 
• Lack of executive support: Senior managers generally do not appreciate the importance and value of good data management. It is very difficult to convince them and show the roadmaps of how these management techniques would be beneficial for the organization. In other words, most of the executive managers are happy with their state-of-the-art solutions for the problem domain.