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Today, companies are generating vast amounts of data files. The files are created from multiple sources and existing in different locations across the organization’s data storage infrastructure . In some instances, the files can contain structured data which can easily be processed by RDBMSs such as SQL server, MYSQL, and ORACLE to generate useful information.

However, in most cases, the files will contain unstructured or semi-structured data in form of emails, documents and other forms of digital media. Unlike the structured data that can easily be processed, both semi-structured and unstructured data—what is commonly called the big data—are particularly difficult to manipulate. In most cases, the amount of big data created by the organization can not only put a strain on its storage resources but also strain on processing capabilities. 

From storage perspective, managing any big data in an organization means locating and removing the files that are :

• Obsolete
• Duplicated
• Non-essential

It takes lots of time to search through each of the storage sizes of data that can be archived  or even deleted. When you ignore to archive and delete obsolete, duplicated  and non-essential files, they will continue to consume valuable and limited storage capacity.

From the processing resource perspective, manipulating your data requires the application of appropriate statistical skills that can help you mine useful information for decision making. With proper statistical  and computing skills, you can quickly evaluate the current state of your data and take actionable steps to help you retrieve valuable information while mitigating the risk of compliance-related challenges.