Big data analytics as an effective approach to enterprise risk management
The recent hysteria on big data analytics is hinged on the vast value propositions for business operations. Big data presents volumes of structured and unstructured data in high velocity and from multiple sources.
The value of big data can be derived from its inherent characteristics, the ability to provide management with large volume of real time data that can be transformed into valuable information for better decision-making.
Enterprise risk management refers to the proactive strategic approach of planning and identifying potential risks to business objectives, both operations and earnings and effectively managing them. It also involves identifying potential opportunities that can be exploited towards achieving and surpassing organizational goals.
Integrating analytics into risk management is not a new concept, however, the current shift represents a move from the traditional historical analysis to more predictive and future insights or even a combination of both approaches for better planning, judgement and decisions.
Three major ways big data analytics can be employed for effective enterprise risk management:
Planning and risk assessment
Big data analytics provides access to a plethora of valuable data and information that can help identify and assess risk situations in a timely manner. The celerity of big data entails real time influx of internal and external sources of information that make it possible to make current and forward judgments.
Organizations become privy to information that enables them assess their objectives and associated risk appetite and also brings to light potential opportunities and advantages that can be harnessed towards achieving organizational goals. Given that risks are integrated into an organization’s operations, big data analytics present an avenue for efficient planning towards better operational results.
Better decision making
The predictive advantage of big data analytics enables effective decision making both on operational and strategic levels of an organization. More informed judgments and decisions on bottlenecks or bridges encountered in the course of business processes, operations, market position and revenue optimization are made through the business intelligence and data analytics. Prior proactive data collection and mining provides insights that translate to clear decision making guidelines in the advent of risk and opportunities.
Action and response
Big data analytics drives prompt response to risk and opportunities. It provides a platform for management to envisage the likelihood of a potential risk through data mining and simulations and proactively respond by avoiding, mitigating or controlling it.
Internal controls are effectively administered based on business intelligence and access to information on business risks and opportunities. Initial predictive insights enable quick reaction to the occurrence of sudden disrupting events thus reducing time and resources employed towards risk management processes.
A forward-looking approach towards managing enterprise risks is enhanced through the help of big data analytics. The capture of real time data through sensors for example can highlight needed maintenance for equipments before they breakdown; this springs up quick preventive measures to avert operational loss.
The benefits of big data analytics in enterprise risk management cannot be overemphasized. The effectiveness of proper planning and risk management however depends substantially on capabilities of deployed human capital that specialize in data analysis, risk management and decision-making.
Adeola Ojierenem CPA, CGA, ACCA