The growing use of social media across the globe has had a significant impact on risk analysis, especially in the financial sector. Big data has enabled more effective engagement, review, and calculations that may be related to financial risk. Big brands like Infosys have already identified several uses of big data for assessing opportunities, risks, weaknesses, and other factors in capital markets.
Big data is widely used to characterize the buildup of data that is too large to be collected using conventional techniques. The data may contain countless information ranging from personal information to birth details and social security numbers, to transactional data and electronic court records.
Professionals handling this information should carefully establish a compliance framework around Big Data that can scale and that all stakeholders understand. We’d like to share three tips for mitigating risk using big data:
Hiring the Right Team
This is a key. Technology is constantly changing, but foundational knowledge is the real core to getting the most from your data. More and more data analytics masters programs are popping up, and hiring team members with this training can really pay off.
Access big data compliance efforts
Remember that there aren’t many policies that address security, privacy, and ownership of data if a company elects to sell that data.
Review the way you secure and protect documents
Many organizations keep paper records by default. Some of them are now digitizing and adding to these records. While some information may not be crucial, most of it is quite sensitive and includes customers’ personal details and financial records.
Various security measures like limiting room and system access may be implemented – but what if your big data strategy determines that some kind of meld of this data with third-party data is important? Remember that the compliance needs to be revisited when the lines between traditional data repositories rules begin to blur.
Define new strategies for managing big data compliance
In the past, it was easy to identify and retrieve sensitive data, as the methods of data searches were straightforward, especially in the case of relational databases with structured data. Well, that is not the case with big data anymore. It can be totally unstructured and unpredictable, which makes it quite difficult to search for sensitive data that requires protection under regulatory guidelines. This makes it important for you to implement new strategies in order to make sure data is being managed safely while using big data.
Mitigating risk with big data requires organizations to go beyond awareness, to developing strategies and building workable privacy protections, procedures, and products. This is not an easy task as privacy laws can be onerous. Moreover, the relevant laws cannot keep up with the developments in big data, especially at the state level, resulting in unintended consequences and regulatory gray areas.