Improve Risk Data Quality And Your Competitive Edge
To survive – and thrive – organisations of all sizes, across every industry, need accurate, consistent and trusted risk data to make well-informed decisions – decisions which enable them to not only ward off threats and minimise production interruptions; but also to boost productivity and performance. Such information should be at the heart of business planning, risk mitigation activities, and the allocation of human, financial, and physical assets.
However, in a recent Deloitte Global enterprise risk management survey, 79% of institutions polled voiced concerns about the quality of data in their risk management systems, with 40% of these being extremely or very concerned.
Missing, incomplete and inconsistent risk data can cause significant problems for organisations – consider the potential for OH&S incidents, the loss of sales revenue, wasteful spending and the impact on corporate reputation, simply because your ERM program relied on flawed data.
Traditional approaches to risk data management
There is mounting evidence to suggest that risk assessment programs to date have been poorly designed and are largely ineffective – with much of the data produced bearing little relationship to the actual, real-world risk profile of an organisation. Factors contributing to this can include:
- Inaccurate and incomplete risk registers
- Reliance on the simplistic tools, such as the somewhat coarse and subjective risk matrix, that can lead to false impressions about risk certainty
- Limited ‘snapshots in time’ derived from the experience of a few people or run by external [and expensive] consultants
To complicate matters further, often these data sets will be sitting isolated within departmental, site or business unit silos. As a result, organisations are left with inconsistency in enterprise-wide data; risk and compliance reporting issues; poor allocation of resources, and an inability to make better business decisions.
Technology to improve risk-aware decision-making
How then can boards, executives, and management, make decisions and move ahead with confidence if they are unsure about the quality of the risk data being provided to them? And, how can they know, with greater certainty, where to allocate limited organisational resources to risk-reduction and value-creation measures?
The good news is that technology is starting to play a pivotal role in improving risk data quality. New enterprise risk intelligence software tools such as riskDNA from Relegen are addressing the shortfalls of traditional approaches and enabling organisations to make better risk-aware decisions. Through a systematic and structured control-centred approach [called C2RI], and which aligns to ISO 31000:2009, riskDNA gives organisations
- A centralised risk data repository that delivers significantly improved actionable-information – granular insights into each risk PLUS enterprise-wide visibility of all risks, both vertically and horizontally
- A common standard for risk assessment which includes qualitative and rigorous quantitative evaluation methodologies
- A robust method for risk value scoring so organisations can correlate risks in relation to each other and model the impact of a variety of control measures, ensuring they can direct resources to where it will have the best business impact.
- A visual and easily communicable plan [including bowtie analysis] for risk improvement that meets the needs of all business stakeholders
- The ability to improve corporate risk knowledge over time and become self-sufficient, eliminating the need for ‘snapshot’ risk assessment projects delivered by external consultants
- The ability to integrate risk management with other mission-critical management systems e.g. enterprise asset management, safety, finance and more
Implementing an effective, data-driven decision-making platform, such as riskDNA, coupled with the right organisational strategies, processes, and people, will not only break down the barriers to better enterprise risk management but deliver better business performance as a result.