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ESG Management: Breaking Down the Data Barrier

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This post is the second in a series exploring the 4 Pillars of ESG Management: Strategy, Data, Reporting, and Engagement. Here we explore Data Collection. Stayed tuned for Data Part 2: Data Management.

Is data holding you back? Verdantix recently reported 94% of organizations cite disparate & complex data as a significant barrier to improving on ESG and sustainability data. Supporting your big picture thinking with real-time, investor-grade data gives you the confidence to demonstrate progress, comply with reporting requirements, and meet your stakeholders’ expectations, but disconnected systems, manual processes, and disparate information result in lost time and resources that could be better applied to achieving your sustainability goals. The benefits to deploying a comprehensive ESG data management program are clear—but the obstacles are many. So, where to start?

ESG and Sustainability Data Barriers as reported by Verdantix, September 24, 2021

Let Your ESG Strategy Guide the Way

Organizations that have already done the work to create a strong ESG strategy will be better prepared to pinpoint the data and metrics relevant to their organization and industry. Emissions, waste, executive compensation, charitable giving—what’s on your data wish list? And where does that data live both within and outside your organization? Be comprehensive. Start by identifying both the qualitative and quantitative sources across operations, business units, and locations. Remember, data doesn’t just reside in systems, it’s in your people, which means you will need to understand who can contribute to your data picture.

When you’re just starting on your ESG journey, limited time, money, or staffing may make it necessary to simplify—maybe you will only be able to prioritize one operational site or supply chain partner. That is still a start. Try not to be overwhelmed or discouraged into inaction. A comprehensive plan will provide a roadmap for scaling up your data management in the future as resources allow.

Explore Ways to Automate and Integrate

Without the proper tools, efficiently centralizing accurate, timely ESG data is a formidable task. Organizations can better manage data collection from every operational facet by deploying the right combination of tools that streamline data management efforts and employ both people and devices to give you the full data picture.

Accessing that external data doesn’t need to be so cumbersome either. APIs can deliver external data seamlessly into an ESG platform and connect you to primary data sources like utility websites and public databases. A single, comprehensive platform for data collection, calculation, and analysis is the most efficient way to ensure data is captured consistently and accurately.

Improve Accuracy and Fill in the Gaps

Data automation tools can help companies efficiently measure & track their ESG performance with high accuracy. Artificial intelligence may sound buzzy, but beyond the hype there are practical ESG applications. Despite good intentions, Boston Consulting Group (BCG) recently reported that more than 90% of firms are incorrectly measuring emissions. The solution? AI. In addition to ensuring data accuracy, AI may also be the most efficient path to achieving sustainability targets. According to the same BCG report, AI can help companies reduce emissions by as much as 40% by identifying the best initiatives, tracking results, and optimizing operations. Bring on the robots.

There are many barriers to the ESG data game but making a real commitment to establishing or improving an accurate, efficient ESG data management system is the only way to discover insights and improve performance, moving an organization forward on their sustainability journey.

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.

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