Industry
Sustainability Services
Ecosystem-led Approach to Power Your Sustainability Strategy
HIGHLIGHTS
- In keeping with the 2015 Paris Agreement, numerous initiatives have been identified for technological rectification, human adaptation, and procedural corrections, but nearly all of them require real-time availability of high-quality ESG data.
- We propose a schema for simplifying ESG data that starts with identifying the data sources and establishing the purpose, then goes on to setting up the physical database, and finally, use the data for supplier evaluation, visualization and simulation led forecasting and planning, and so on.
- A strategic IT and sustainability transformation partner will help run the full course seamlessly, right from laying down the ESG data architecture, to implementing the physical infrastructure, and finally in use case enablement.
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ESG data is the key to sustainability transformation
As the world continues to evolve at a fascinating pace, we witness how human intelligence is embracing sustainability and artificial Intelligence (AI), signifying the rise in world’s consciousness for sustainable prosperity.
In 2015, the Paris Agreement was signed by a majority of world leaders, marking a historic moment that underscored the need for all of us to be mindful of our actions. While we may have embarked on the journey, but the path is not easy, as is evident in evolving regulations, material sensitivity, and data consolidation.
Many functions and authorities across the globe are working tirelessly to ensure sustainable development, especially at the grassroots level, which is the foundational premise of the United Nations 17 Sustainable Development Goals (UN SDGs).
Numerous initiatives for technological rectification, human adaptation, and procedural corrections have been identified, but many of them are hinged on the availability of ESG data. Therefore, it is essential to simplify the ESG data landscape, while ensuring its quality.
Figure 1 shows a high-level schema for simplifying the design, development, and realization of ESG data.
Figure 1: The design-develop-realize-service schema for ESG data journey
Design – a solid blueprint is half the job done
Although everyone recognizes the importance of ESG data, but due to differing priorities of the various functions involved, finding the starting point is seldom easy.
A data engineer may specify the challenges, an architect may seek detailed requirements, functional experts may present use cases, but CxOs need to address the broad ‘why’ and ‘what’ of the program – the broader objective. Bringing all this together calls for a systematic approach.
To begin with, organizations should crystallize the purpose of ESG data. This can be easily identified through double materiality assessment, regulatory requirement analysis, industrial benchmarking, and defining ESG strategy. Once the purpose is defined in terms of ESG data domains such as energy, water, waste, and the like, then it is imperative to elicit the data requirements.
At this junction, an ESG data governance body for ESG data stewardship and quality controls can be established. In parallel, a data analyst can start identifying ESG data points, metrics, and their respective sources. Then, the data architect can present logical data models and position the ESG data architecture for approval.
It is to be noted here that this is an iterative task, which means we have to make space for a few cycles of assessing and fixing the gaps, before finalizing the ESG data architecture. Such a ’review and revamp’ exercise will build the confidence needed by the executive management in and pave way for the long-term vision.
Develop – constructing the landscape is crucial
Further down the course, the code base would be ready to process data pipelines and insert the data into the physical database.
The approved ESG data architecture will assist in:
- Setting up the physical database and configuring the ESG data model
- Building the pipelines and creating the validation and mapping rules
Organizations will need to keep in mind a few key considerations, such as:
- Data cleansing and transformation will take place at the source system, the staging tool, or the target system.
- Global, regulations and standards for data security, such as GDPR, must be factored in during the design and development phases.
- The right-fit technologies must be available to fulfill long-term goals.
- Standard processes should be adopted to deliver fixes and schedule releases.
Henceforth, ESG data can be ingested or migrated from the source, triggering the aggregation logics to insert the data into storage. Testers play a vital role in assurance during system testing, user acceptance testing, performance testing, and so on. They should therefore be provided with requisite time and necessary tools.
A crucial aspect is to archive, erase, or repurpose data over time so that it doesn’t pile up, which will in turn consume resources and hinder agility. Data circularity could be an approach. It is a complex and sensitive topic, but can be handled by staying mindful of compliances and the rules of business.
Realize – putting ESG data to use
Once the quality of the ESG data is ensured, organizations can pull out ESG reports, design AI and GenAI use cases, conduct supplier evaluation, create dashboards, run advanced analytics, and so on.
By using digital twin and simulation technologies, ESG data can be leveraged for decision making and risk management. Fundamentally, ESG data storage behaves as single source of truth.
Based on the intended use or outcome of ESG data, the corresponding data harmonization layer will have to be built.
Benefit – enjoying the fruits of labor
The proposed schema adapts to numerous technologies and agile methodologies, and works well across businesses and IT environments.
Having navigated the design, develop, and realize phases of the ESG data journey, organizations will be well-placed to develop a comprehensive sustainability roadmap with a modular approach that allows for optimal time and resource utilization. This strategy roadmap will have to be fully aligned with industry-standard agile methodologies and adapted to the latest technologies.
Early detection and fixing of gaps help in building a robust system. Activities, when designed for parallel execution, along with inclusion of skillsets, will promote better utilization of time and resources.
Moreover, the schema gradually sets the data landscape and builds the foundational layer for multiple use cases.
A strategic partner can ensure seamless transformation
Organizations typically have their own core functions where IT landscape is an essential enabler.
Partnering with external IT experts is therefore a common practice in achieving IT goals. ESG data strategy is one such program, where an IT partner may offer rich expertise in addition to a range of services and products. For instance:
- Advisory to lay the foundation for ESG data: For greenfield IT development, advisory service helps in laying out the ESG data architecture with governance.
- Assess and fix the gaps: If an enterprise has already established a well-rounded IT landscape, it is an efficient way to assess the existing components for ESG data and plug the gaps.
- System implementation: Once the vision is developed, then comes building the IT system from ESG data architecture to a physical database (as shown in Figure 1).
- Assure and fix the bugs: This stage involves proofing and assessing the compliance of the IT system. An ESG data audit and quality assurance service can be leveraged to ease the path of launch.
- Use case enablement: Once ESG data is ready to use, a variety of use cases can be enabled, based on clients’ needs, including respective data harmonization.
Once the right service and product are employed, ESG data will serve well across the landscape.