Big data style analysis may also be of importance in detecting threats while they are still at early stages through the use of complex pattern analysis as well as analyzing multiple data sources. Intrusion detection and Intrusion prevention systems can further be used to enhance the security of Big data by continual adjustments as well as effectively learning good behaviors. Logs should also be gathered and analyzed to detect where the abnormally was when a security incident occurred. Zoning critical systems from each other such as segregating the information systems from the physical premises can also be a good security strategy to ensure that data is not tampered with. In Physical locations still, access controls and CCTVs alongside other security precautions should be installed to scare away or deter attackers from reaching critical systems.
To avoid these challenges, it hence important to structure data carefully before conducting a data analysis, designing systems that can handle large volumes of data processing faster and precisely as well as developing algorithms capable of randomizing personal data among a large data sets for purposes of guaranteeing privacy. It is also crucial to build a system that is flexible enough to expeditiously run diverse workloads. Mechanisms that focus on the legal requirements required for data handling should also be met. Homomorphic encryption that allows certain computation to be conducted on cipher text and obtain encrypted results that if decrypted matches operations conducted in plain text should also be carried out. Through this data in the databases will be kept safe for the user. SMEs can however use open source tools to implement Big data analytics. The business knowledge of IT staff also needs to be enhanced to work with data in their businesses. It could also be great if the acquisition of Big data becomes recognized as innovation for SMEs. (Gupta, Seetharaman & Raj, 2013).
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