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Utilising human centred analytics for effective measurement and reporting of ESG

Author: Dr Christina J Phillips from Liverpool John Moores University

Published: 05 Jul 2023

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Dr Christina J Phillips from Liverpool John Moores University explores how Human Centric Analytics (HCA) could be a natural paradigm to use when defining, measuring, and reporting on Environmental and Social Governance (ESG). Tying together the need for a holistic societal approach to tackle the problems around social and environmental sustainability, and the need for reporting and measuring the levers of change in business, she considers what methods we can use to help us to do this, and how we can ensure that behaviours are embedded alongside measurement protocols such that change becomes real, and not just a ‘tick box’ exercise.

As Eli Goldratt famously said, “tell me how you measure me and I’ll tell you how I’ll behave”.

So, why do we need to be so concerned about analytics when it comes to ESG? To quote David Harris, Global Head of Sustainable Finance, Data and Analytics at LSEG, “The finance and investment community can drive the solutions to secure a sustainable and net zero emission future, to achieve this, there is a need for robust data and analytics.” This is true of finance, but it is also true within the firm. A recent report by Accenture on behalf of the UN interviewed over 100 CEOs to reimagine global pathways to resilience growth and sustainability. They spoke of how AI, analytics, and robotics were changing the way procurement and supply chain management can be done. They also saw a need for artificial intelligence and predictive analytics to be used for data driven scenario/risk planning.

Deloitte’s ‘Blueprint for a green workforce’ report found climate change and decarbonisation to be the highest priority for firms regarding their strategy and operations. They point to a persistent digital skills gap unable to satisfy a need for embedded analytics throughout supply chains and development of robust but updateable KPIs. This suggests a need for analytics development that prompts knowledge growth and skills acquisition alongside the data and analytics design pathways.

Operational Research, described as ‘the science of better’ by The OR Society, has many tools for acquiring decision parameters and developing metrics. It is also good for running scenarios using different techniques and even has many methods for handling messy problems! It also has a growing set of human centric frameworks that have codified procedures to bring humans and analytical tools together.

The key to unlocking effective human data interaction is to ensure a bridging of understanding. After all there are usually three, more or less aligned, truths to data: what the data say, what the data are supposed to say, and what people think the data say (technically multiple truths since everyone could have their own version!). This data multi-verse needs bridges to understanding, which means opening up multiple perceptions as well as data sources before attempting to bring these together often across diverse stakeholders.

Human Centric Analytics (HCA) as a design paradigm

Human Centric Analytics (HCA) is a design paradigm (a way of doing design) that follows the principles of human centred design. The idea is to grow knowledge as you design and put into use the analytics required. This needs methods to foster active participation and discussion by the humans involved and ways for them to interact with the analytics. It also needs sympathetic and broadly experienced analysts who can call on multiple methods/dimensions/visualisations to find the ones that make sense in the stakeholder’s context.

When we worked with stakeholders in a complex manufacturer to create a simulation for scenario exploration, there were many occasions when we had to create a space for dialogue between the data/analytics and the stakeholders (both individually and as a group). People needed to understand what their experience looked like in terms of data and analytics to be able to relate to the parameters we would need to make a model work. Without this understanding they could not have agency in the decisions around model parameters or understand what the model was saying. This meant I had to listen to what they were experiencing and relate this to data sources, for instance I created clustering based on their context and knowledge of the products being made. Once I had clusters that we all agreed through a process of statistics/visualisation and discussion, I was able to do further analysis and visualisation such as histograms and box plots that used colours they had chosen. Stakeholders recognised their experience in the way the statistical models were behaving. They were also absorbing a refresher course on statistics, learning about the available data and its systems, and learning to use visual tools.

An acceptable level of granularity

This story brings us to the next step in HCA that of creating an acceptable level of granularity. This is where the measurable parameters are decided upon, for instance the time step, the occurrence rate, the level of detail, or the number of functions to include. When we took the simulation model up to the SLT it had to become a highly simplified conceptual model with the key indicators measured and the highlights of the more complex modelling noted. While that same model was being developed with staff whose processes it was modelling, we had to iterate a couple of times before we got the granularity right and then again before we got the modelling right. The process of achieving an acceptable level of granularity created knowledge and prompted behaviour change by opening up insights into manufacturing process and creating networks of involved stakeholders. This happened for everyone SLT included.

The rest of the HCA process is much like human centred design: there are likely to be multiple iterations of development, the needs to be a chance to reflect and discuss, to be creative and to allow solutions to emerge in use/for use. The process is non-linear and should not be expected to be and it is best to figure out ways around obstacles after having first created ideal solutions. Don’t expect everyone to agree but hope for the best alignment possible!

Roles are important when using HCA. It is a good idea to have a champion from senior management who believes in the project and wants to see it work, someone prepared to commit enough time and energy. One also needs to make sure the evidence is recorded well, so diary keeping can help as well as either audio/video recording/note taking. Of course, there are always the trusty stick it notes, rich pictures and maps (causal, cognitive, feedback, mind map). Collating this evidence well and ensuring it can be, and is, accessed at a future date is a crucial part of HCA since we have to take this qualitative human data as seriously as our systems data.

I believe that HCA is one way to help us create firms that are able to address the serious challenges involved in achieving net zero and the worldwide Sustainable Development Goals (SDGs). As Tariq Fancy (former senior ESG analyst) points out “We’re running out of time: we can no longer answer inconvenient truths with convenient fantasies”. Our KPIs and the people who measure them need to ensure that we are not just ticking boxes but are actually making organisations more responsive and in so doing making our future lives and jobs more sustainable.

If you would like to know more about human centric analytics, or if you are interested in trialling an HCA initiative in your sustainability endeavours, Dr Phillips invites people to contact her directly: c.j.phillips@ljmu.ac.uk.