How data-driven decisions add value

With investors seeking greater ESG impact without sacrificing returns, the need to collect, categorize and assess data at scale and in real-time has only increased.

In days gone by, investors may have relied on gut feelings to guide their decision-making. Today, although fund managers may get lucky once or twice by adopting this approach, it does not represent a long-term strategy. Eventually, assumptions will fail, leaving investors asking what the justification was for any particular backing. 

In the modern world, this justification is likely to come from data. Aside from helping to minimize risk, data can also add value throughout all stages of the investment lifecycle. And one area in particular, where it is being employed concerns investment with an environmental, social and governance focus.

“We are seeing more investors looking for mission-aligned investment opportunities, without sacrificing returns,” says Yohan Hill, director of ESG and responsible investing at Adam Street Partners. “Because of this, we are seeing more interest in investment vehicles that have ESG as a core objective. This drives the necessity of having ESG data available from the outset. It’s not an afterthought anymore.”

With data sources proliferating, as well as regulatory and market pressures increasing, data provides managers with new ways to not only monitor their investments but also identify new opportunities and determine the right moment to make an exit.

Excel is not the answer

Despite the fact that ESG-focused institutional investment is set to reach $33.9 trillion by 2026, according to PwC data, acquiring the data to prove this ESG focus is a major challenge. In fact, an ESG study conducted by Capital Group reveals that almost half of US investors cite “a lack of robust ESG data” as a hurdle to ESG adoption. 

“Data is a major topic throughout investment portfolios, but especially with regard to ESG,” says Jessica Wichser, co-head of private real estate asset management at Partners Group. “We’ve been viewing ESG data the same way that we’ve been viewing financial KPIs. But it’s a significant challenge. ESG data is granular and not standardized. There remains a lot of work to be done in terms of collecting and categorizing ESG data.”

Much of this work will rely on digital solutions – but investment funds are being tasked with evolving their toolsets beyond legacy spreadsheet software. For instance, the World Bank recently relaunched its Sovereign ESG Data Portal, incorporating 71 ESG indicators, including water stress, coastal protection, forest cover loss, and precipitation anomalies. The MSCI Corporate Sustainability Insights tool was also launched in February of this year to provide investors with more detailed ESG data. 

“We use a number of different data providers as well as various internal tools and platforms to enable us to scale our approach to ESG integration,” Hill continues. “We use leading data providers to help us monitor our portfolio on a daily basis for ESG incidents, actively tracking approximately 11,000 portfolio companies. So we manage a huge ESG dataset and we rely, in part, on external data providers using sophisticated technology.”

To provide genuine visibility, ESG tools do not merely have to cover a broad spectrum of metrics; they must also be suitable for a variety of geographies. That’s why international partnerships are becoming more important for ESG disclosure. UK-based asset manager Redwheel confirmed that it has adopted the same ESG tool used by US banking institution Northern Trust, which was developed by Equity Data Science – a provider of purpose-built analytics with offices in India and the US. With the sustainability challenge truly global, the data-driven solutions helping to meet it are, too.

Finding value-add

One of the reasons discussions about data are so often tied to ESG issues is the fact that such data can be applied throughout the investment lifecycle. It can be used to assess whether an asset should be purchased, it can be used to monitor ESG performance over time, and it can help with the crafting of an exit strategy should an asset stray too far from an investor’s sustainability goals. 

“For us, ESG data becomes useful before any acquisition takes place – when we’re conducting due diligence,” explains Wichser. “ESG data helps us with our forward-looking business plans. For example, we want to make sure our buildings are not only meeting current standards but also the standards of the future. We need to account for this both when we acquire a real estate asset and when we carry out upgrades. ESG is not just a downside story. There’s a value-creation story too.”

As Wichser notes, ESG data is about much more than mitigating risk. Multinational conglomerate 3M has saved $2.2 billion since introducing its “pollution prevention pays” (3Ps) program, according to McKinsey data. The data also shows that China’s efforts to combat air pollution are predicted to create an additional $3 trillion in investment opportunities between now and 2030.

“We are seeing a growing number of managers setting ESG KPIs,” Hill acknowledges. “My reading of that is that these managers are going beyond risk mitigation into value creation.”

A cultural shift

The focus on the importance of data for meeting ESG aims must not become a myopic one. ESG needs to be embedded within the wider corporate culture to ensure that investment assets truly are having a positive impact and are not merely an example of greenwashing.

A combined human and the technological approach could provide a more effective way of harnessing ESG data for value creation. That has been the case for UOB Asset Management and its “Man-and-Machine” model, which combines machine learning with human analyses to improve its ESG coverage in the ASEAN region.

“There remains a long way to go,” Wichser says. “The technology for ESG data collection and analysis exists. But I don’t think it’s perfected yet.” However, with the momentum that ESG-focused investment has gathered, it surely will not be long.

Sustainable software

Partners Group has piloted an AI-powered software that autonomously optimizes building management systems

Built in 1981, 501 Kennedy Blvd is a Class A multi-tenant office property in Tampa, Florida that demonstrates the potential of using data to improve the ESG credentials of real estate assets. To collect data and reduce the energy consumption of the property’s HVAC system while maintaining good air quality and thermal comfort for the building’s occupiers, Partners Group piloted the use of a virtual engineering, AI-powered software solution that autonomously optimizes the building management system of commercial properties.

Using 17 months of historical electricity consumption data to set the baseline, 214MWh were saved during the pilot, which prevented the emissions of 83 metric tons of CO2. The achieved building-wide 16.9 percent energy savings are significant, with no capex requirements and the software installed using the current BMS. “We see such data-driven initiatives as a scalable way to reduce the climate footprint of commercial real estate properties,” says Jessica Wichser, co-head of private real estate asset management at Partners Group.