Understanding the importance and current constraints of Managing and Measuring the Social Risk in ESG
The Challenge to Measure Social Factors
The ‘S’ in ESG (Environment, Social, and Governance) refers to social factors, which can be challenging to measure for a variety of reasons. Many believe it is too complex because of the lack of data and the data that is available is poor quality or inconsistent.
Measuring social factors are essential as they reflect the impact an organisation has beyond its environmental and financial performance. Many stakeholders, including investors, customers, and employees are increasingly interested in the social impact of each organisation. Ignoring the social factors could lead to reputational, and financial risks, coupled with a degradation in productivity and performance. The war on talent is increasingly being influenced by these social factors.
The lack of data availability means there is a need to start from a different place to the convention of a data-centric start-point.
An alternative approach is to start from the available rule-based knowledge.
The Problem with Rule-Based Knowledge
Evidence shows that rule-based knowledge covering what is written down and the way in which it is enacted in practice, is not fit for purpose. The fragmented state of rule-based knowledge nurtures systemic weaknesses. Bias and indiscriminate decision-making prevails. The lack of measurements and auditable decision-making means problems are often hidden in plain sight.
Because of the fragmented state of knowledge, the rules can be spread across many places such as:
Policies
Processes
Procedures
Subject Matter Experts
Forms
Regulations
Statutes
Instructions
Guidelines
Training
Content i.e., portals
Though the interplay between decision-making and rule-based knowledge is often broken, this is generally not well understood. As fragmented rule-based knowledge grows it leads to higher operating costs and inflexibility as more people are needed to manage and understand the complexity. These problems are amplified when cultural cohesion is eroded, and critical knowledge leaves an organisation.
Fragmented Rule Based Knowledge Persists
Many iterations of technologies over the decades have been unable to reverse the growing volume of fragmented rule-based knowledge. The latest advances such as Robotic Process Automation, Machine Learning, Generative AI and Chatbots are all able to process simpler forms of rule-based knowledge. Yet these technologies are unable to make material inroads into the vast volumes of complex rule-based knowledge, which continue to grow unchecked.
The persistence of fragmented rule-based knowledge surviving, across many generations of technology ‘breakthroughs’ means that Knowledge Capital continues to be treated as an intangible. This is because too much of the most important knowledge needed for decision-making is still primarily found in documents and in people’s heads.
The lack of data related to the way complex rule-based knowledge is applied in practice means deep systemic risks will continue to grow unabated. When these deep-rooted problems eventually surface then under-identification and under-reporting of the social risks are commonly cited.
Whilst under-identification remains problematic then the following conditions will prevail:
· Missing and error prone data
· Incomplete understanding
· Inaccurate representation
· Bias in decision-making
· Missed opportunities for early interventions
· Problems becoming more entrenched
· Marginalisation of affected groups
· Manipulation flourishes
Our Df2020 belief is that the rate of change and complexity materially increases the volume of rule-based knowledge. The lack of any form of science means the size of the problem is simply not known. Our hypothesis is that the volume of rule-based knowledge is doubling in ever decreasing time cycles.
Go to Market
Df2020’s go to market strategy is via partnerships that wish to licence our cloud-enabled platform with embedded methodologies.
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Contact
John Rawlings: jrawlings@df2020.com