How the right data and AI foundation can empower a successful ESG strategy

A well-designed data architecture should support business intelligence and analysis, automation, and AI—all of which can help organizations to quickly seize market opportunities, build customer value, drive major efficiencies, and respond to risks such as supply chain disruptions. A well-designed data foundation can also be a game-changer when it comes to managing ESG (environmental, social, and governance) commitments. Fortunately, business benefits and ESG benefits are not mutually exclusive: sustainability efforts can help boost business value for organizations that are committed and effective in execution.

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Integrating data and using insights to help drive environmental initiatives

Having fact-based data can help with understanding performance and measuring progress against broad-based ESG goals; insights generated from this data can help organizations further their ESG programs as well as drive operational efficiency; and credible environmental reporting should be supported by factual data. To further the above, organizations should have the right foundation that consists of a modern data governance approach and data architecture. By implementing a modern data architecture, you can set your organization up to gain insights quickly since users have self-service access to data relevant to their roles regardless of where it resides.

Once you have identified your data requirements, you might need to acquire data from various operational systems and applications, integrate them and organize them for easy access by stakeholders across your organizations. These stakeholders may include real estate, finance, HR, and procurement teams as well as the sustainability team. Everyone would be using the same data set to make informed decisions which may range from goal setting to prioritizing investments in sustainability.

Supporting the increasingly important social and governance pillars

Certain reporting obligations bridge organizational risk and human impact and are thus classified under the social pillar of ESG. As AI may inform more and more decisions within HR such as hiring, evaluation and promotion, an organization’s need to respond to new and expanding regulation will increasingly intersect with its need to address ESG standards.

It’s becoming critical that organizations should adopt a data architecture that supports AI governance. That is, it should support both sound data governance—such as allowing access only by authorized processes and stakeholders—and provide oversight into the use and trustworthiness of AI through transparency and explainability. The approach should be designed to provide enough metadata that key HR decision makers can identify which processes and decisions are informed by AI while maintaining anonymity and privacy in the data itself. Using an architecture like data fabric can enhance an organization’s ability in key governance and oversight areas, while building a company’s ability to understand and manage many forms of risk.

How a data fabric architecture can support ESG efforts

Delivering on ESG initiatives can be enhanced by a data architecture that supports gathering, integrating, and standardizing data from a variety of sources and making it available to a wide variety of organizational stakeholders. A data fabric is an architectural approach designed to simplify data access to facilitate self-service data consumption at scale. Data fabric can help model, integrate and query data sources, build data pipelines, integrate data in near real-time, and run AI-driven applications. This architecture can also deliver reliable data through enhanced data observability and steward data quality by automating tasks across data platforms using machine learning. Given the wide scope of ESG data elements that encompasses so many departments in a company and extends to its partner and supplier ecosystem, a data fabric can help accomplish governance, integration, and data queries and insights at scale.

Reporting on all three pillars of ESG should begin with evaluating your framework and the kinds of data needed to be included to support a credible and transparent disclosure report. The simplified data access that comes with a data fabric architecture can make it easier for teams to both evaluate and update their ESG reporting framework and to efficiently deliver upon a report.

In conclusion

Many successful organizations have long depended on fact-based, consistent, unified data to drive growth, manage costs, and optimize resource allocation. A similar correlation holds true in leveraging data to drive leadership performance in the ESG areas. A modern data fabric architecture can help tackle organizational challenges around capturing, accessing, and operationalizing this data for calculation of impacts in all three pillars of an ESG reporting. This data foundation is crucial for truly effective ESG strategies that can help boost business value and manage evolving sustainability regulations around the globe. Important stakeholders such as employees, business partners, investors, customers and government regulators are all expecting ESG programmatic progress and accurate reporting. The time for data professionals to meet this challenge is now.

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