ESG Reporting

ESG — From boardroom discussions to a tailored data strategy (Part 3)

Deep Parekh, Managing Partner, Epistemy BV

ESG Reporting — proposed solutions and roadmap approach

In Part 2 of this series, we discussed the burning platform and the nexus of three critical factors that are making ESG reporting both important and urgent. In Part 3, we offer solutions to ESG reporting and propose a pragmatic and effective roadmap.

Reporting metrics combine performance, risk and controversy

If we take a look at the MSCI reporting parameters, we quickly notice that they are split into Performance, Risk Exposure and Controversies. They depart from traditional mainly performance-based metrics, which companies are pretty good at already and with which they are quite comfortable.

MSCI reporting

Risk Exposure and Controversies add unknown dimensions to traditional Performance reporting; they are composite metrics that require both structured and unstructured data, internal and external enterprise data sources, and tools to truth-test, validate and parse into the required structural combination to be interpreted.

What are the practical implications?

Take the example of the MSCI ESG evaluation framework:

ESG score
MSCI ( -

Using the Coca-Cola Company as an example, the Climate Change metrics may use the GHG protocol for measuring the Product Carbon Footprint. Using the metrics characterization shown below, Coca-Cola is responsible to report not only its own carbon emissions through its direct manufacturing operations, but also that of its suppliers upstream as well as its distribution and retail chain downstream. Further, indirect emissions in terms of corporate travel, headquarters and office-related emissions, including daily commuting of its workforce are also a part of the equation.

GHG protocol
GHG Protocol (

Now, layer in Risk Exposure and Controversies, where you need to:

  • Put in place internal and external mechanisms to gather and collate structured and unstructured data, catalog it, compare against benchmark values and truth-test it.
  • Develop AI to drive learnings and predict possible future risks.
  • Find patterns between independent data sources and measuring / monitoring mechanisms through AI engines and logic.
  • Tie in (e.g.) social networks and user-initiated sources into the AI engine to provide warning signs of issues and monitor social well-being.
  • Link IoT and sensors with other data sources for a fine grained, dynamic view of the ESG factors.

These are all quite onerous, and with no clear roadmap of how to get there.

Proposed ESG reporting solutions

To address these challenges, we propose a suite of solutions:

targeted data strategy
ESG8 (

With the plethora of data sources, types and constituents who need to review them, it is best to have them as modular as possible. Further, instead of discrete data collection events, it is better to have a perpetual passive data gathering mechanism in the background to build internal trust and reliability and create a credible ‘steady state’ of information flow. Lastly, create new data sources and tie them in with your data platform and power it with algorithms for quick feedback loops and actionable insights.

What does it take?

According to a 2020 McKinsey & Company survey, executives and investment professionals cited two key reasons that ESG reporting is a challenge: 1) data availability; and 2) expertise to analyze and generate insight.

Making data available
ESG8 (

  • Multiple data sources with different rules and access protocols need to be woven together.
  • Multiple data types are needed to create a composite view and prompt the right inclusion within metrics.
  • IoT data must be connected to the right contextual referential information to make sense of it.
  • Data transparency is necessary between network partners within a business ecosystem.
  • A common data playbook is necessary for ecosystem partners.
  • Ecosystem partners must create mechanisms for data collaboration.
  • New data tools are required for truth-testing, manipulating, relating and contextualizing the information.
  • Learning algorithms are required to act on the disparate data to seek patterns and insight.
  • Interdisciplinary teams must be set up to enable data cognition within and across enterprises.

A pragmatic and implementable roadmap

ESG8 ( and Juvo (

The recommended path is to move from the lower left quadrant to the upper right quadrant, via the lower right quadrant. It is easier, cheaper and less risky to start by making data abundant and usable. Next, creating an interdisciplinary team of competence to work through the data, release insights and turn those insights into actions is harder and riskier. Along the way, the business can exploit emergent ESG opportunities, as a step toward making ESG reporting actionable and fully embed it into the business model.

A business will need five key thrusts:

  1. Catalog all relevant ESG data to highlight what is available and what is needed.
  2. Identify the relevant stakeholders in the right sequence and timing.
  3. Target what ESG reporting maturity level it is seeking to achieve.
  4. Build an ESG data sandbox to try out different approaches, algorithms, methods and pilots.
  5. Bring knowledge in through the ESG community of practice.

Of course, this is iterative, as you will always need to expand the data set, add new tools, reach new maturity levels, expand the stakeholder group and add further incremental capabilities to process the data and insights. This is a simple and pragmatic way to proceed and is manageable by most companies.

About the author

Dr. Deep Parekh, Managing Partner, Epistemy BV

Dr. Parekh is a serial entrepreneur and currently serves as the Managing Partner of Epistemy BV, a Belgian firm dedicated to developing sustainable strategies.

Deep has served in executive leadership roles at Please Platform BVBA and Triamant NV in Belgium at the intersection of digital transformation and life, health and social technologies, and helping to shape Belgian legislation on the on-demand work economy. Prior to this, he co-founded an investment and advisory firm Asteroidea AG in Switzerland. Deep also co-founded and served as Managing Partner of management consulting and advisory services firm Equus Group in the US and Latin America. Prior to his journey on the course of serial entrepreneurship, Deep held executive advisory positions at various companies, including Unilever, Ernst & Young, Booz Allen Hamilton and IBM.

This article series is a result of a recent webinar titled ‘Powering Your ESG Ambitions through Data’, hosted by Juvo, a professional services company in Belgium that specializes in making data profitable, and ESG8, a Belgium-Netherlands based ESG advisory services firm.