This post is part of an ongoing series exploring the 4 Pillars of ESG Management: Strategy, Data (Part 1), Reporting, and Engagement.
So now that you’ve successfully captured and centralized your ESG data, what’s next? How do you ensure that information is functional and working for you? Organizations can move from data capture to data management with help from sophisticated technology. New ESG management platforms can include features like a powerful calculation engine, providing your organization with an auditable track record and the path forward to seamless, automated analytics and reporting. The demand for transparency has never been greater. And for investors, the quality of your data can be a key indicator of your overall ESG performance.
Quality Data is Complete Data
Data collection is just the beginning. After capture, it is important to control for quality, so you will be able to transform the data, extract valuable insights, and accurately see how well your ESG program is performing. While this may seem daunting with such a large amount of data, collected from multiple sources and locations, there are tools that can help.
A powerful ESG platform with a calculation engine and built-in automations will ensure data hygiene, accuracy, completeness, and scalability. The right technology will be able to pinpoint potential data errors, gaps, and anomalies with simple reports.
But, how to resolve these data issues? A built-in and up-to-date Emission Factor Library will not only automate the calculation of your GHG emissions, but also provide precise estimations based on historical data to fill in any gaps. These calculations can provide you with both location and market-based emission factors, as well as transmission and distribution losses. Save time and let platform tools do the heavy lifting, especially as your organization continues to scale and add sites or suppliers for data tracking, collection, and calculation. This frees up your time so you can actually leverage the insights generated and engage your stakeholders with accurate ESG reporting in real-time.
Data Management Needs to Be Auditable and Secure
As with any form of data management and reporting, your organization’s ESG data must be auditable by a third-party to ensure accountability and confidence in accuracy. Don’t depend on last-minute preparations to make sure your data is trackable and auditable—implement daily, healthy data management practices.
The two key aspects to pay attention to are transparency and security. Is there a clear tracking record of data upload and any changes being made after initial entry? Who in your organization can make these edits? Are there any potential privacy breaches in sensitive organizational, supplier, or employee data that you might be storing?
Maintaining transparency and data security is yet another hurdle, but one that can be easily cleared with the right software tool. A comprehensive ESG platform with industry standard security certifications will be able to instantly log any data changes with timestamps of when the actions were taken and by whom. When it comes to sensitive, personal information collected from social metrics, DEI initiatives, employee training, or accident reports, a certified ESG platform becomes even more critical. The right tool will not only have the proper security measures in place to protect data but also automatically report it in an aggregated, deidentified format to ensure you’re meeting the highest privacy standards.
Make Your Data Work for You
When it comes to comprehensive ESG data management, data capture is just the beginning. The key is what happens next – maintaining quality so you can share accurate insights with your stakeholders, while also managing the auditability and security of your organization’s data. With the proper tools, you can build better analytics and insights to start connecting data with purpose.
Need help making sense of your data? The FigBytes Data Management and Analytics module helps organizations track ESG goals using real data, in real time. Streamline data collection, quality control, and calculations for complex and comprehensive Environmental, Social, and Governance data sets.
