Inflection Point: Realizing the Value of Data in Responsible Sourcing
By Matt Moshiri and Donna Westerman
“Garbage in, garbage out” is a phrase often used in computer modeling to express the idea that poor-quality input typically leads to poor-quality output. Responsible sourcing teams should view the data that they use to evaluate risk in exactly the same way.
Measuring and understanding risk are paramount for responsible sourcing managers, which is why many have willingly jumped on the data bandwagon. However, given the plethora of open and closed sources of data available, determining which are optimal or most appropriate can be a serious challenge. You can have the best and brightest responsible sourcing team, but if it is working with suboptimal or inappropriate data, the business will fail to derive the full benefit. Equally, top-notch data and analytics in the hands of teams without data expertise are at best wasted or at worst potentially damaging to the business.
Building a culture of data
The effective evaluation of economic, social, environmental, and natural hazard risks throughout the supply chain is critical for business and requires data that is transparent and up to date and that captures inherent risk with sufficient depth to inform strategic decision making. It serves all companies well to bring the best global risk analytics into the process early for responsible sourcing and risk mitigation through every part of the supply chain to avoid potential reputational and legal ramifications.
Whatever a business’s uses for risk data—whether for simple compliance purposes, to assess risks for key commodities, or to provide the framework for a best-in-class responsible sourcing program—the aims must align with the trajectory and values of the corporate agenda. In our experience, if a responsible sourcing team is unable to make clear to the wider business the important influence that data can have, they will fail to unlock the value that a data-integrated approach is designed to create; and this can lead to a range of problems, including the loss of executive support. In other words, they need to effectively instill a culture of data upwards in the organization.
But to embed a culture of data successfully, sourcing teams must go beyond simply making sure that everyone understands how data is being used. Data should be viewed as an integral part of responsible sourcing and a key enabler in helping drive value creation, as opposed to simply a “nice-to-have.” Data requirements evolve, and responsible sourcing managers need to secure the resources to ensure their program can effectively keep the business clear of potential reputational and legal entanglements. This is one of the major hurdles of trying to implement a beyond-compliance responsible sourcing program, particularly if the manager is looking to gain additional internal buy-in and budget approval. Money can make all the difference when it comes to accessing data analytics.
Identifying the good stuff
All too often, we see responsible sourcing programs that reveal a lack of true understanding of the underlying drivers of risk. The problem may lie with the analytics, because much of the risk data available—particularly free, open-source data—tends to be derived only from issues that are easily quantifiable, such as reported human trafficking incidents. In such cases, the limitations to the data could cloud the ability of a responsible sourcing team to see the real picture as to potential human rights violations in a country.
Figure 1 below visually represents all the key indices that make up Verisk Maplecroft’s Human Rights Index and illustrates clearly how general human rights risk is driven by a whole set of secondary issues. In addition to recording the number of human trafficking violations, for example, our data also measures the laws in place to address human trafficking and the efficacy of the enforcement of those laws. This provides businesses with a much truer—and more actionable—view of the human trafficking risk posed in a country. As the figure demonstrates, the fact that a country is rated medium risk overall when it comes to human rights does not convey the reality that there are areas where the country is categorized as extreme risk (or, conversely, low risk).
Figure 1: Revealing the drivers of human rights risk (example country)
Responsible sourcing teams that ensure the data they use covers the full spectrum of risk—and then study the many and various indicators—will be able to achieve a whole new level of insight, allowing a company to better understand and appropriately design or adjust its responsible sourcing strategy.
By being knowledgeable about choosing data sources and understanding differing methodologies, teams can also “future proof” their responsible sourcing programs. Sourced correctly, risk-related data can drive and shape strategy. So, the key takeaway here is: buy the data you need for tomorrow, not today. Building insight over time is valuable, and changing the data that you use will ultimately change your view.
Looking into the future
Using predictive analytics to make accurate forecasts about future unknown events represents an inflection point for the responsible sourcing community and will fundamentally enhance a company’s ability to understand and get ahead of the emerging risks within a supply chain.
The potential is vast for predictive analytics to revolutionize auditing and supplier selection and identify likely supply chain disruptions. However, with predictive analytics being touted as the next big thing in the responsible sourcing toolkit, managers must remember that data is only as good as the individuals used to construct it—or indeed the colleagues charged with applying the data to the business.
Figure 2: What do we mean by predictive analytics?
In search of a data expert
Every responsible sourcing program requires people with the expertise to source, manage, and even generate data. These individuals need to understand the capabilities of the data they are using, fully comprehend what the output means, and ultimately differentiate between good data and bad data (in other words, the “garbage”). They will also need to communicate the purpose the data serves to the wider business and be able to address, and at times defend, unavoidable questions from within the team as well as from all corners of the business (including legal, compliance, and procurement) as to the validity and applicability of that data.
These internal experts must also be able to work with both internal teams and external partners to design new approaches and look at implementing advanced capabilities such as data mashing—combining, for example, SAQ and audit results with inherent country risk data and predictive analysis. However, as a responsible sourcing team advances its data capabilities and works to fuse internal and external data sources, it must very carefully assess the construction, robustness, and applicability of the external data upfront. Combining data is a powerful but also complex business.
At the end of the day, data analysis cannot and should not replace human judgment and expertise; instead, it should serve as a pivotal tool for responsible sourcing teams in making more targeted and better-informed decisions.