Data at the center of ESG compliance
Our data strategy and a data-backed foundation as a must for ESG compliance
The ESG (Environmental, Social, and Governance) criteria is a set of standards used to evaluate:
1) E: An organization’s performance on how it interacts with the natural environment
2) S: Among other aspects, Social includes gender, racial diversity and well-being of the groups with which it relates.
3) G: And finally in governance criteria: encompass corporate practices such as ethics, codes of conduct and anti-corruption policies.
Until relatively recently, a company’s ESG profile went unnoticed by consumers and investors. However, now these groups recognize that these matters are fundamental, and the social actions linked to sustainability are generating challenges for the organizations.
An analysis by MAS Business reveals that organizations with established sustainability practices perform better in 9 areas: operational costs, stakeholder relationships, talent attraction and retention, market differentiation, innovation, risk management, productivity, finance, and brand/reputation.
In this article, I will show you how, on one hand, ESG regulations have evolved and on the other hand, how important but difficult it is to evaluate this criteria due to data. I will be giving you some tips on Data Strategy to help companies act now!
From Voluntary to Mandatory
In 2019 and 2020 we have witnessed a turning point, marked by regulation and new expectations from supervisors (with the European Union and the United Kingdom leading the change). This paradigm shift takes us from a voluntary and political scenario, to a mandatory and regulated one.
Right now, the biggest focus is on the risks associated with climate change, but everything points to a rapid extension towards broader factors in the whole set of ESG criteria.
Organizations that do not address ESG holistically without integrating it into their operations with data-backed controls to assess, track, and ultimately improve performance against ESG criteria will experience lower-than-expected financial results and their corporate image will be harmed.
The difficulty of navigating an ocean of ESG data
The current way to communicate the impact of sustainability is very subjective: it is represented by performance indicators submerged in reports loaded with text that are open to different interpretations and that have little (or no) traceability.
The increasing number of reports and number of agencies have only complicated the picture. Rating agencies use their own hardly comparable methods to formulate ESG scores, which leads to useless comparisons (in this sense organizations such as EU ESMA and IOSCO are analyzing the characteristics of the data and ratings generated by information providers). The following aspects further expand the challenges we face:
- Disparate data sources: Data sources for ESG information include organizations’ self-published reports, commercial or subscribed data from data aggregators, rating agencies, other industry and regulatory organizations, and social media.
- Expensive data: ESG data comes at a cost, plus a license fee, comes with conditions in terms of use and distribution. The indiscriminate use of ESG data within organizations can lead to considerable costs, so there are restrictions on use, storage, distribution, and geography.
- Non-agnostic data: Third parties use proprietary algorithms to obtain ESG data, therefore they come with an inherent “proprietary analyst bias”, which may differ from the analytical perspective of the organizations using this data.
- Poor data quality: The data formats range from quantitative reports to qualitative comments, so they are not homogeneous in terms of representations, units of measurement and methodologies adopted in the derivation of information. Organizations often report data that is out of sync over time for comparison within their operations or with peers. ESG data coming from social networks further complicates the situation.
ESG data serves as a stimulus for the transformation of business processes and vice versa. If it is obtained from ESG compliance we have to implement new processes that will require new sources of information or a review of those we already have in our ESG data map.
Data strategy as a guide
When it comes to evaluating ESG criteria, a data-backed foundation is a must..
A holistic approach is necessary where data has to be in the middle to provide the different actors with a platform to govern, analyze, measure and disclose in the following lines of action:
Data strategy
- Carry out an assessment of materiality, data that directly impact the factors of the organization’s ESG strategy, and definition of the most appropriate metrics.
- Updating and analysis of the impact of standards/regulations through a service specialized in interpretation and assessment (“observatory”).
- Approach the transformation with a holistic view of the data beyond reporting.
- Integration of ESG data initiatives into business processes; organizational change management in sustainability management.
Data governance
- Deployment of ESG data governance model and capabilities based on viable and impactful use cases, and incremental improvements.
- Establish a continuous process of generation and identification of internal data and external sources.
- Initial focus on the most critical aspects of ESG data governance: data quality, traceability and lineage, and glossary of technical and business terms.
- Implementation of an integration model with third parties (rating agencies, certifying, and verifying organizations, and clients).
- Extension of governance criteria to data received by customers, as part of ESG management in financial entities and as a guarantee of data reliability.
Operating model, data architecture and technologies
- Ability to mobilize from the initiative, with defined responsibilities and functions.
- Definition and measurement of the value delivered and the deployment of the ESG data office.
- Transversal projection of the ESG data initiative in IT transformation programs.
- Contextualize the ESG vision in IT by conducting a materiality assessment.
- Evolution of data platforms and investment in building an ESG data model.
- Align selection of technology providers with shared sustainability goals, progress, and public action.
Conclusions
Consumers, regulators and shareholders are increasingly putting pressure on organisations to obtain tangible information on sustainability and benefits from this…
The time to act is now! As it already happened a few years ago with the EU General Data Protection Regulation (GDPR), we cannot wait for it to be mandatory because reflection and planning are necessary to execute in a focused and efficient manner.
Regulators are working hard to define mechanisms that force organizations to be more transparent. Investors are convinced of the long-term impact that their investments should have and consumers are converging towards much more responsible consumption models.
Consequently, we are experiencing a tipping point for organizations where we need to work on how we will incorporate sustainability mandates into our data strategy so that stakeholders can make informed judgments.
Making sustainable investments starts with the disclosure of meaningful information and the integration of ESG data into business processes and decisions. Governed, quality, reliable, mapped, and cataloged ESG data is the first step we need to take now.
The negative consequences of launching an ESG program without a data strategy that responds to its specific casuistry, can be overwhelming and worsen over time, often preventing an organization from being recognized for its efforts in this area.
Applying a proper data strategy, data governance, and operating model on top of strong architecture and technologies early in our ESG program will prevent us from running into problems down the line by keeping risks low and increasing the attractiveness of our ESG program.
Thank you for your time
Oscar Alonso Llombart
