Sustainability data management: The limitations of a data lake for ESG metrics

by | January 31, 2024

Navigating the complex landscape of sustainability data can be daunting for any organization. One crucial decision lies in implementing an ESG data architecture to store your sustainability metrics. Should you dive into the vast expanse of a data lake, or opt for a more structured approach? 

This article delves into the pros and cons of utilizing a data lake for sustainability data management, helping you identify the ideal path for your organization’s green journey.

The challenges of sustainability data

Imagine a sustainability professional posing this seemingly simple query: “Show me all 2018 SASB metrics for the Consumer Goods vertical, segmented by business unit, with data provenance from its sources.” 

Embedded within this request lies the inherent complexity of sustainability data management, characterized by:

  • Data variation. ESG metrics span diverse domains like human resources, supply chain, emissions, and waste, encompassing primary and secondary data.
  • Evolving standards. Sustainability frameworks like CSRD, SFDR, and CDP are always evolve, demanding data structures to adapt year-on-year.
  • Diverse sources. Data originates from a dizzying array of applications, databases, spreadsheets, and even human sources, often requiring upwards of 26 integrations.
  • Historical depth. Data needs to span years, months, weeks, and even days, further complicated by yearly structure changes.
  • Business unit granularity. Different units might require subsets of the entire data universe. Imagine the contrasting metrics for a healthcare and chip manufacturing company!
  • Geographic nuances. Emission factors in Japan differ from those in Mexico, and regulatory reporting requirements like EU’s CSRD and Japan’s disclosure regulations often overlap with subtle variations.
  • Cascading data collection. Data resides with personnel and systems across diverse organizational levels, requiring multi-layered aggregations with unique calculations for each metric. For example, scope 1 emissions are a straight addition. Meanwhile, a child labor policy metric might require aggregating “yes” responses across levels.

Pitfalls of data lakes for sustainability data management

Faced with such overwhelming complexity, IT teams often stumble towards data lakes, mimicking the multi-dimensional structure with “maniac spreadsheets” for organization, geography, units, periods, and sources. The initial allure of a seemingly boundless repository, however, can quickly fade as limitations emerge:

  • Data lakes are static. Data lake schemas lack dynamism, making adapting to any of the aforementioned data dimensions a laborious coding and engineering feat.
  • Data lakes don’t prioritize data quality. Data lakes often become “data swamps” where quality is an afterthought. Batch jobs attempt data cleansing, but true quality control lies at the source – with humans or upon system integration.
  • Data lakes aren’t built for auditing. Versioning and tracing calculation sources (like emission factors) prove immensely challenging within data lakes.
  • Data lakes can’t provision lineage. Unraveling the data journey from source to metric computation is practically impossible in a data lake.
  • Data lakes don’t handle workflows. Orchestrating tasks like populating 600 metrics across 400 countries in 12 languages for 20+ units becomes an IT nightmare due to multi-dimensionality and organizational hierarchy limitations.
  • Data lakes don’t integrate easily. Data integration becomes a batch-oriented, often human-dependent process due to the inability to handle multi-dimensionality and data variations.
  • Data lakes aren’t self-service. Business users get stranded, waiting weeks for reports, hindering self-service and agility.

A dedicated sustainability data management solution

While data lakes hold value for analytics and visualization, similar to how ERP and CRM systems support core business operations but still benefit from data lakes for insights, they shouldn’t be the sole answer for sustainability data management. 

A dedicated sustainability data management platform emerges as the true captain’s choice, offering distinct advantages:

  • Dynamic data modeling. Sustainability data management solutions adapt to evolving data structures and standards, eliminating schema rigidity.
  • Real-time data quality. Data quality becomes an inherent part of the process, ensuring clean and trustworthy information at its source.
  • Enhanced auditability. Versioning and calculation source tracking are embedded within the platform, fostering transparency and trust.
  • Clear data lineage. Data journeys are mapped and easily traced, providing crucial context for informed decision-making.
  • Automated workflows. Sustainability data management solutions orchestrate data flows and tasks, empowering self-service and accelerating insights.
  • Seamless integrations. Native integration capabilities handle multi-dimensionality and data variations, streamlining data collection and analysis.
  • Empowered business users. Sustainability data management platforms grant business users direct access to reliable data, fostering agility and data-driven decision-making.

Navigating the complexities of sustainability data requires a nuanced approach. While data lakes offer storage potential, their limitations can outweigh the benefits. By adopting a comprehensive data management strategy that combines specialized platforms, source-level governance, and user empowerment, organizations can transform their ESG data from a swamp of uncertainty into a lake of clarity, enabling informed decision-making and sustainable growth.

Pulsora’s sustainability data management platform

At Pulsora, we understand the complexities of navigating the ever-evolving ESG landscape. That’s why we’ve built a comprehensive sustainability management platform designed to help businesses with complex operations conquer the data challenge. 

Pulsora goes beyond data lakes, offering a dedicated ESG data management solution that automates, simplifies, and centralizes every aspect of your sustainability operations. From data collection and management to reporting and compliance, emissions calculations, and performance tracking, Pulsora empowers you with real-time insights and actionable data to drive informed decisions and achieve your sustainability goals.

Pulsora is your trusted partner in navigating the ever-evolving world of ESG data. Don’t get lost in the data swamp—let Pulsora guide you towards a clear and sustainable future. Speak with one of our sustainability data management experts to learn how we can help you unlock the power of your ESG data.