Emerging technologies enabling diverse forms of data creation and their integration with traditional data is generating voluminous information for organizations. Businesses – large and small alike – endeavor to derive valuable insights by processing and analyzing the big data. Efficient application of big data and analytics benefits organizations by enhanced assessment of emerging risks. Using big data strategy improves institutions’ risk profiles and paves the way to approach risk in a profitable manner.
However, despite enterprises’ efforts to gain competitive advantage not too many have succeeded, while the majority has failed to convert data into valuable insights. According to IDC, only 22% of digital data was a candidate for analysis, while less than 5% was actually analyzed.
Although it is easy to attribute this to the big data challenges, in reality this is a reflection of organizations’ inability to effectively leverage big data to their advantage.
Such failures do not bode well for any organization as they represent sizeable financial loss to go with equally significant loss of time. Also, smart firms would claim these are opportunities lost that impact present business performance and stifle future growth. To stay profitable, businesses need to view the vast amounts of data as assets and put practices in place to manage them well throughout the data lifecycle. Complementing these strategies with the deployment of evolved data management tools before finally using the insights to monitor and mitigate a broad spectrum of risks would place enterprises ahead of the competition.
However, to use big data as a key driver in risk management it is imperative to assess the possibilities of big data in:
Data is a critical foundation for efficient risk management, but establishing the context(s) for data analysis is an equally important prerequisite to benefit from the broader role of big data in risk management. Contexts render the analysis strategic and help in defining the scope to obtain a closer-to-expected result. An approach of this nature will ascertain that the process initiated has more to do with enhancing business performance rather than avoiding a compliance concern.
Upon establishment of the context comes the need to identify the potential threats facing the organization. Data collected from authentic sources through valid means enables true risk identification while a flawed approach yields bad data, resulting in imperfect risk assessment. Data sources should include minute analysis of past risk events and scenario analysis. Risk detection must be followed by recognition that portrayed threats are real and often function at the most unlikely times, leaving unforeseen consequences behind.
Once identified, risks must be analyzed with the help of appropriate technology capable of processing large data sets. While traditional tools and the use of open source platforms generally provide a good overview, specific software solutions built precisely for the purpose offer targeted analysis that can be easily translated into business advantage. An advanced risk management tool acts as a potential market differentiator with timely insights and meets regulators’ expectations.
While data-enriched insights facilitate putting tailored defenses in place, technology-enabled control measures help in monitoring risks effectively. Just as risks vary depending on the industry type or a particular segment within it, recent advances in technology have created the specific tools to efficiently control threats, without over-controlling risks which choke business innovation. While analysis of big data improves risk assessment performance to identify all potential risk types, it also helps to deploy relevant controls in place, averting potential threats.
Of all the steps in risk management, risk reporting is perhaps the most neglected one as it is overlooked in a hasty search for finding countermeasures. A risk report, prepared post efficient data-gathering and analysis, should elaborate on the data insights in the context of the organization’s sector, geography, assets and commitments, and suggest control implementation and mitigation strategies. Smart enterprises use a qualified and documented risk reporting procedure to escalate the findings to the competent level, involvement and decision making from which acts to mitigate risks for a safely longer period of time. Access to the risk report making way for active participation of senior leadership ensures that:
Application of big data strategy in risk management will help in eliminating the culture of gut-level decision-making on the part of businesses. Emphasis on data-driven decision-making helps in devising out rational decisions while moving the maturity of the organization’s security culture forward. In an era of rising compliance expectations and increased security concerns, enterprises that embrace rapidly advancing big data solutions will create robust risk management posture and achieve sustainable business growth.
The use of a proven, yet simple to use, product such as Risk Management Studio has increased the effectiveness of risk strategies on a global scale for small to large enterprises. The application is free to try, easy to set up and start using, optimized for the latest international standards, including but not limited to, ISO 27001:2013 and PCI DSS 3.0. Send your inquiries and questions to email@example.com.