The current state of sustainability data is hindered by information silos, but by investing in technology architecture to help integrate such data into overall business operations, companies could unlock new opportunities for growth and decision-making
The current state of sustainability data is marked by the creation of data silos, where data is locked away in applications built for calculating and reporting compliance metrics, especially around environmental, social & governance (ESG) activities.
Indeed, the environmental data silo is a prime example of this issue, says Matthew Sekol, an ESG and Sustainability Advocate and author of the book, ESG Mindset. “For a while now, sustainability offices have been collecting activity greenhouse gas emissions data from across the business and in some cases, going outside the business and getting data from suppliers, Sekol says. “While this data is valuable for compliance purposes, it remains inaccessible to others in the business who may need it for analysis and decision-making.”
This challenge of many isolated data islands also offers a huge opportunity, explains Sekol. “We’re at this unique point where one office is collecting data from across the company — and the last time this happened was when accounting rules were created,” he says. “Back then, data was handwritten in ledgers and manually collected. As we enter this new round of ESG accounting, we have digitized much of the company’s activity data and that of the company value chain. Now we have data solutions that can help bring together business data.”
In fact, many corporate sustainability offices may have insightful data on emissions, water, waste, plastics, energy, and more, but it all remains locked away. This inaccessibility of these data silos creates a significant challenge for businesses looking to integrate sustainability data into their overall operations and decision-making processes.
Data lakehouses as a solution
The current state of data complexity across enterprises is characterized by a multitude of disconnected systems that often contain information that is unreachable. “The evolution of data management technologies — including data lakes and data warehouses — has contributed to this complexity,” Sekol adds.
Data lakes and data warehouses are both storage systems for big data but differ in significant ways. Data lakes are repositories designed to store large volumes of raw data in its original form; and data warehouses are repositories that stores processed and structured data that has been optimized for analysis.
As organizations grapple with the growing importance of sustainability and ESG data, the limitations of current data management strategies become more apparent.
“While data warehouses offered structured storage for quantitative data, they were limited in handling unstructured information. Data lakes emerged to address this gap by allowing for the storage of diverse data types.”
However, both solutions have led to data sprawl, redundancy, and security concerns. The proliferation of purpose-built applications and the practice of extracting and copying data for various business units have further exacerbated the situation. Although API networks have been used to facilitate data access and break down some barriers, this approach is increasingly insufficient for future needs because of the risk of outdated information getting copied when data is used by business functions at different times.
As organizations grapple with the growing importance of sustainability and ESG data, the limitations of current data management strategies become more apparent. This highlights the need for more integrated and flexible solutions that can seamlessly combine structured and unstructured data while maintaining security and governance in one repository.
Thus, data lakehouses have emerged as a powerful solution to address the growing challenges of hard-to-reach data across organizations, including for organizations’ sustainability functions. A data lakehouse is an innovative data management architecture that combines the best features of data warehouses and data lakes, offering the structure, performance, and data management features of data warehouses while maintaining the flexibility, scalability, and low-cost storage of data lakes. A key advantage to the data lakehouses approach is that it is applicable to structured, semi-structured, and unstructured data. This versatility allows for more comprehensive data analysis, including advanced analytics and machine learning capabilities.
How you can build the business case
The collaboration among IT, sustainability offices, and other business functions is crucial for securing and democratizing access to sustainability information. For example, sustainability professionals recognize that environmental data is an asset for multiple uses across the business as well as being a new type of data silo.
As a sustainability professional seeking to unlock the possibility of multiple uses by multiple functions, you should start by engaging with IT to understand the current data infrastructure. IT understands how to protect data while enabling secure collaboration across the company and the current landscape of tools that could help in enabling broader access to information across internal functions. You should discuss the need for secure, centralized data storage that allows for collaboration across departments while maintaining data integrity and security. Additionally, you should familiarize yourself with concepts like application abstraction and data integration to better communicate your needs to IT professionals, Sekol recommends.
When approaching these conversations, you should lead with the business needs rather than sustainability goals alone.
Next, reach out sales, marketing, finance, and other functions to understand their priorities and data needs. Look for opportunities in which sustainability data can provide valuable insights for their operations.
When approaching these conversations, you should lead with the business needs rather than sustainability goals alone. It is helpful to identify intersections between the business function’s goals and materiality issues or stakeholder concerns. These use cases could be easy wins that could demonstrate how integrated data can address both sustainability objectives and core business challenges.
Sekol also advises you to be prepared to discuss the potential benefits of a data lakehouse approach, such as improved data quality, reduced redundancy, and enhanced analytics capabilities. By bridging the gap between sustainability data and broader business insights, you can build a stronger case for investment in modern data infrastructure that serves multiple organizational needs.
The key to unlocking sustainable business growth lies in breaking down data silos and integrating sustainability data into overall operations and decision-making processes. By adopting innovative data management solutions — such as data lakehouses — and fostering partnerships across business functions, companies can harness the power of sustainability data and upgrade their technology architecture at the same time.
You can find more about the challenges that organizations face with sustainability here
