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Silloo: Hello. This is Silloo, welcoming you to a new episode of the fortnightly EY India Insights podcast. Today we are continuing with our discussion on sustainability, with a focus on the role of data analytics and benchmarking in ESG. As we know, every business is deeply intertwined with ESG concerns. It makes sense, therefore, that a strong ESG proposition can create value. And in today's podcast, we will look at the importance of ESG data analytics and benchmarking and deep dive into the real-life use cases and the actionable insights it creates for the enterprise. To explain this and take us through the importance and depth of the topic, we have with us Nitesh Mehrotra, partner of ESG Consulting Practice at EY India. Nitesh specializes in sustainability data across business processes, analytics, and benchmarking with a sectoral use-case led approach. With over 20 years of EY experience across multiple global offices, Nitesh has been influential in end-to-end ESG project management, driving profitable growth and opportunities focused on sustainability as a value driver for his clients. Welcome to the podcast, Nitesh.

Nitesh: Thanks, Silloo, for inviting me. Clearly, as you mentioned, the two biggest challenges facing humanity are climate change and social inequality. And we all need to drive this revolution from boardroom to classroom, chairman to watchman. So, a pleasure to be here, Silloo.

Silloo: Thank you. Nitesh, data is both critical and challenging in mapping ESG. What are your thoughts on this?

Nitesh: To step back a little, ESG today is a focused boardroom agenda for all leading enterprises to create and protect value. And I think it is very critical to have consistent and comparable scientific measurement of sustainability variables across all our stakeholders. So, clearly, there is a critical need to have a single version of truth with near to real time performance analytics to create actionable insights. Accordingly, I would say our data goals are divided into four pillars that all leading organizations need to follow.

Number one is scientific measurement and baselining, because that is very critical from where we are starting.

Number two is benchmarking and continuous monitoring. It is clearly not a onetime exercise; so how are we assessing how we are doing on a journey?

Number three is improving performance and I think that needs to be aligned with how it is helping in enhancing our revenues, our top line and optimizing cost and working capital. And fourth, but not the least, Silloo, is how do we effectively communicate the impact and value that this journey is creating across all our stakeholders? So, in summary, I would say data has to act both as a telescope and a microscope in all our key business decision-making for an enterprise.

Silloo: Interesting. And how do we contextualize data from an ESG standpoint?

Nitesh: I would say that there are mainly four layers to look at in the ESG data landscape. First is what you know, or what we refer to as ‘the ESG alphabet soup’. How do we decode what is out there, from an ESG standards and sectoral risk and opportunities library? Silloo, just for your listeners’ reference, there are 500-plus ESG frameworks and standards that have been evolving for the last 20-plus years. So leading framework examples like the TCFD (Task Force on Climate-related Financial Disclosure), International Sustainability Standards Board (as it is called under the IFRS Foundation), Science Based Target initiative (SBTi) for net zero; European Union has come out with an EU taxonomy for investors’ measurement; and other investor-led languages like Climate Disclosure Project, Climate Action Hundred, and others.

Back home in India, the SEBI Business Responsibility and Sustainability Reporting (BRSR) is a very important framework for enterprises to look at. And then, there are multiple ESG ratings which are creating the language.

The reason all of these exist is because they are from different stakeholder lenses. Some are from investors, some are from a customer standpoint, and the remaining are from the societal and regulatory aspect. So, we see an increasing movement toward global harmonization and the focus is more on centralization and forward-looking data, something that we can predict, going forward. So that is the first layer to decode a little bit.

The second, which is very important for an organization, is what we call enterprise data that an organization is emitting across its business model. Primarily, it would be around ‘buy, make, move, and sell’ processes, including support processes like Hire to Retire, Record to Report, and governance processes.

Third, is the third-party data that an organization is emitting across its supply chain and value chain. This includes third parties like suppliers, business partners, distributors, outsource service providers and customers. This data has to be segregated based on environmental footprint, social human capital, and governance data points to create traceability, cradle to cradle.

Fourth, and not the least, is the external data that an outside environment creates, like ratings agencies, ESG controversies, competitor intelligence, and sectoral benchmarks.

In summary, we believe that third-party data and external intelligence are critical to differentiate an enterprise ESG transformation journey.

Silloo: You had earlier also mentioned benchmarking. Can you explain how benchmarking helps accelerate an enterprise's ESG transformation journey?

Nitesh: Benchmarking is key to create competitive differentiation. Leading companies can look at it through two lenses – outside-in and inside-out. I will give a few examples to bring this to life, starting with outside-in, which would include things around how we assess the material theme and sectoral weightages for an enterprise across ESG. I think that is the first input an organization can assess. How do we benchmark with our select peers and competitors to gather intelligence around KPIs, goals, and targets? As an organization sets out on the net zero journey, diving into the Science Based Targets initiative (SBTi) benchmarking for 4,000-odd companies that are already on the roadmap could give us some good, actionable insights.

Then there are some others around climate risk scenarios, such as the TCFD, which talks about scenarios that an organization needs to assess, and some of the other sectoral benchmarks embedded within that. We also believe (outside-in) that ESG performance ratings – which assess disclosures, performance, and risk rating, and are out in the public domain – could also give clients some wonderful insights regarding perception and reputation that clients have around the data points. Just to give you some context, I think there are 25,000-30,000 companies globally that are rated and their ratings are in the public domain.

In India, there are about 1,000 companies that are rated by multiple rating agencies. Engagement with these rating agencies, including for proxy firms, is key to embed within the governance of an organization.

For inside-out examples also there are some good data points.

One is the value-reporting benchmark, which has about 2,000 companies are reporting on sectoral KPIs on a consistent basis for the last few years. That is a good database to connect to.

Another one, Silloo, is the BRSR database that we have built. As you know, last year about 130 companies voluntarily underwent BRSR on about 400 data points. We have created a database to create benchmarks for our clients. That gives a sense of the maturity and performance these companies have relative to sectoral peers. We are also helping our clients on benchmarking of certain other governance and board matters.

In summary, these are some of the real-life use cases on both inside-out and outside-in analytics and benchmarking for our clients.

Silloo: That is great. It is a lot of important data. Thanks for sharing this with our listeners, Nitesh. Can you provide some real-life examples of ESG analytics that you might have created for your clients?

Nitesh: We are co-creating several with our clients on an ongoing basis. But to name a few, we are helping our clients with master data analytics across the enterprise data to assess ESG hotspots.  Bill of Material (BOM) analytics – what is going into a bank, or into a product – is a great use case. Another one is assessing suppliers. Analyzing the logistics fleet and fuel analysis could give quick wins on some actionable insights.

There are some significant use cases on carbon analytics. As an organization looks at the carbon footprint across the value chain and products, some relevant things are how to leverage that data model for net zero modeling, marginal abatement, cost-curve analysis for the decarbonization journey, and more.

Water is a very important area for our clients to focus on, especially in India. So, we have created certain water analytics scenarios in terms of analyzing the sources, how and where we are consuming, what are the hotspots, and how we are discharging. The aim is to improve the performance from a water standpoint.

We also work on circularity use cases, which include things around waste and recycling efforts to both save cost and look at it as a new revenue stream.

Silloo: How can we help clients bring this to life? What assets or accelerators have we created to support our clients?

Nitesh: We have been working with and co-creating some of this with our clients. For example, we look at how to create an integrated digital platform that creates a single version of truth and a near to real-time monitoring. At EY, with our Global Center of Excellence, we have created a platform, the ESG Compass Platform, which can give a 360-degree view of the ESG journey to our clients. To create this asset and accelerator, we looked at certain principles. We have taken an ERP-first approach because we believe that sustainability data across the value chain of an organization already exists. But how do we make sure the current business applications have the variables to capture that? So, that is the first thing to take a modular approach because different sectors and clients in terms of maturity have different use case scenarios.

Another one that we have been working on is how to embed sectoral metrics. We believe that environmental and social are very sectoral subjects, while governance is sector-agnostic. So, we have been working on embedding sector metrics for this asset.

Looking at the importance of external data and benchmarking, we are creating a database that has a lot of external intelligence and benchmarks so that the client not only gets the internal performance but is also able to assess how it compares to others as well as benchmarks from a maturity standpoint.

Some of the analytics use cases that we are building on the back of this platform include those around net zero journey, decarb modeling climate risk assessment scenarios, and some others.

So, in summary, I would say the whole idea of this asset that we have been working on with our clients, is how technology at speed and innovation at a scale with humans at heart, can solve some of these very meaningful challenges from a sustainability standpoint.

Silloo: Thanks, Nitesh, for this very informative and interesting conversation and I am sure our listeners have derived a lot of value from these insights.

Nitesh: Thank you. Hopefully, our conversation can provide a sharper direction and a 360-degree view on the ESG transformation journey.

Silloo: I am sure it will. With this, we come to the end of the episode. Visit our website www. ey.com/in to know more about the total valuation approach and access our ESG compass assessment. And please leave us comments on other such topics on ESG that you would like us to deep dove into.

Silloo: This is your host, Silloo, signing off. Thanks for listening in.

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