Managing Risk: Is Your Data Telling You the Truth?
The corporate risk map is becoming increasingly complex and board-level decision makers have never been more aware of the financial and reputational damage that can be caused by supply chain disruption. With back-end systems, processes and governance often falling short of expectations, it is essential for businesses to build a strong data foundation and adopt a structured approach to mitigating supply chain risk. Below CEO Today hears from Grant Millard, Head of Technology at management consultancy, Vendigital.
As well as opportunities, globalisation and the increased pace of mergers and acquisitions present greater risk challenges for many businesses. More complex corporate structures and global supply chains can make it increasingly difficult to manage and interpret risk data, which is often stored across multiple platforms and in multiple formats.
Aside from the more traditional risks associated with interruption to supply and quality failures there is an increased emphasis on business’ ethical and environmental credentials driven by legal and regulatory requirements, shareholders and customers. This means that access to new types of external data is vital, but drawing these data sources together in one place can be challenging.
In order to extract meaningful risk insights from the wealth of internal and external data available, it is important for businesses to establish a ‘single source of truth’. Without access to an accurate and standardised data source, organisations lose the ability to make fully-informed decisions, which could expose them to unforeseen operational risks as well as potentially having a significant impact on their overall strategy and performance in the long term.
The first step to achieving a ‘single source of truth’ as part of an overall risk management and digital strategy, starts with reliable data. Asking the right questions to ensure meaningful risk data is being captured and checking it for quality and consistency are essential underpinning requirements. Unfortunately, however, the effort required to cleanse and structure disparate data sets is often an impediment to this endeavour or the process gets delegated to the wrong individuals lacking the right level of business knowledge.
The only way to achieve a clear understanding of supply chain risks is by taking a systematic, data led approach, driven by a deep knowledge of supply chain risk factors. The various (clean) data sets can then be combined in one place such that board-level decision makers are able to view risks from across departments and geographies (the Digital Boardroom) and apply the appropriate mitigation responses.
However, sometimes there are still legacy cultural issues to overcome, such as a tendency to procure goods and services at the lowest possible cost, almost irrespective of risk. Without strong leadership, clear trade off guidelines and the right incentives, good data on its own is no guarantee of success.
Emergent technologies are beginning to find practical application in this space. With the ability to generate real-time predictive algorithms, AI or deep-learning technology can help businesses to forecast different risk scenarios. For example, these technologies could help to spot patterns where similar events in the past have led to significant supply chain disruption; allowing the business to intervene to protect operational performance.
With business data predicted to continue to proliferate, and acquisitions becoming increasingly common, its management is set to become even more challenging in the future. Businesses should aim to address this now by taking control of their risk data and ensuring it is fit for purpose, accurate and up to date. With the fundamentals of good data management in place and the application of the latest AI technologies, businesses could even find they are equipped to address the problems of tomorrow, today.
