Why unified data models are key to accelerating ESG reporting

Director, Analytics and Automation, PwC Switzerland

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In the previous two blog posts, we explored how effective ESG data management can fuel compliance, strategic decision-making, and operational efficiencies, all of which depend on a robust organisational framework. We elaborated the importance of establishing a clear ESG data operating model with strong governance, well-defined roles, standardised processes, and a sound data architecture. Unlocking the full potential of ESG data isn't just about organisational readiness—technology plays an equally critical role. In this final blogpost of the series, we’ll shift our focus to the technological infrastructure required to manage ESG data effectively. We'll examine how a unified data model can drive improvements in data accuracy, consistency, traceability, scalability, and real-time access, ensuring that your ESG initiatives are not only well-managed but also future-proof.

With more and more ESG regulations entering into force, companies are having a difficult time keeping up with the regulatory demands. In 2021, PwC’s Global Investor Survey found that for 79% of investors, ESG is an important factor in investment decision-making.

However, as ESG topics have become even more important over the last few years, organisations found themselves challenged by the need to manage a significant number of different, inconsistent data sources dispersed across a large number of systems.

Using a unified data model for ESG reporting can ease many pain points at once, while ensuring data traceability, reporting consistency and integration with a variety of source systems.

Discover the blog series

Unlocking value in ESG performance

Through data management and advanced analytics

ESG data operating model

Align strategy with execution

What are unified data models?

In short, unified data models (UDM) combine many different data sources in one central location – mostly cloud environments like Microsoft, Snowflake, AWS or Google GCP. When properly defined, a UDM is built on multiple layers to ensure data can be ingested, standardised and easy to consume. In the financial services sector especially, where there’s an abundance of legacy and monolith systems, UDM has been a cornerstone of creating a basis for ESG reporting.

As a first step, data is ingested in its source system format. This serves as the staging layer allowing traceability of origin for all data points, while establishing a data history. Luckily, as storage becomes increasingly cheaper, the cost of storing data in this format is insignificant for most organisations.

Once the data is ingested, it’s then abstracted into a semantic layer where it’s combined and aggregated into meaningful business objects, rather than raw tables and fields (data mapping). In addition, routines to remove duplicate data entries and redundancies are often employed to ensure the data is in a useable format.

Depending on the concrete architecture, but often in the third layer, KPIs are calculated and data tables are created for data consumption. This consumption is designed for multiple stakeholder groups and tools, allowing easy and flexible access for business users. When built properly, UDMs promise four major advantages:

An accurate and consistent data source for multiple use cases and performance monitoring.
Traceable data points, which ensure compliance and auditability of the data used.
Scalable, flexible infrastructure with centrally managed data access, especially when deployed in a cloud environment.
Real-time data access that can be used for decision-making.

"In one of our latest projects, we achieved a 30% reduction in data platform operating costs, ensuring a ROI within 18 months. Additionally, we improved querying speed, data access, availability, and reduced legacy pipelines."

Peter BlankDirector Analytics & Automation, PwC Switzerland

Conclusion

Unified data models already provide significant value when implemented for single functions. Overall, the centralisation, ease of access and management typically outweigh the cost of implementation within the first three years. In one of the recent, large-scale client case studies, PwC found that data platform operating costs were reduced by about 30%. What’s more, a return on investment within the first 18 months was ensured thanks to the speed of querying, data access and availability as well as the reduction of legacy pipelines.