Resolve Big Data Security Problem by Altering The 80-20 Rule


The 80-20 rule, conceived by management consultant Joseph Juran is a method that is usually used in all types of business. For instance, an organization can conduct 80% of its business by utilizing 20% of its available analytics reports. However, the rule also creates problems for big data analysts when they need to use security rules to big data. The major problem that most data analysts face is that they spend 80% of their time in the process of data preparation. A major part of their efforts go into ensuring that sensitive data fields embedded in big data are safe before importing data into analytics network. As a result, they are left with 20% of their time to work on analytics algorithms and reporting for end business.

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The Challenges

Big data from Internet of Thing(IoT) sources such as machines comes in a totally unedited form. It includes around 5% of highly confidential data elements along with the reams of sentiment data, clickstream data, and raw logging data. As a result, big data analysts face a tough time deciding where security controls are necessary. What makes data cleaning and security processes even more challenging is that the data enters at high speed velocities.

Way To Address The Issue

To complicate things even further, businesses want to combine data from different sources in their analytics. So, all contributing raw data source need to be cleaned and adjusted for quality and security prior to its use as a central data repository for utilization in analytics. However, the issue can be resolved. A data analyst can perform masking of security sensitive data element to rearrange it from all incoming big data streams that contain the element. The installation of data mask is usually permanent.

Encryption is another method that a data analyst can use on data elements that are security sensitive to ensure that they cannot be seen or reused. One of the major plus points of encryption is that it has greater flexibility as compared to masking. It allows you to select to decrypt any data element and make it available for analytics use at any time. Encryption controls may also be used with security permissions within an organization to enable specific power users to have access to data. It is classified as off-limits for others.

Privacy Holds The Key

Privacy is a major area that all businesses pay attention to. Therefore, analysts need to be capable in selectively applying security restrictions to incoming big data streams. Strict security regulations exist in specific industries like healthcare, finance and insurance.There is also the risk of employees exploiting any security sensitive data element. Big Data analysts can make use of a security tool that assists sites in flagging specific data fields that need to be secured only. They can run it against incoming big data without causing any negative impact on system performance.