With Sustainability Analytics, Big Data Can Save the World

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In the coming years, environmental, social, and corporate governance (ESG) data will be as central to making business decisions as financial and marketing data is today. Pressure from regulatory agencies and consumers alike will require brands and manufacturers to accurately report and substantiate the environmental and social impact of their products across the supply chain. This is a scary thought for many companies because, frankly, getting accurate data from raw materials partners, manufacturing partners, logistics partners, and other players in the value chain can feel like the corporate equivalent of herding cats.

The same can hold for many manufacturing facilities and other value chain partners: without a clear understanding of why data is requested and proper incentives, it’s difficult to get the data you need to support accurate and actionable sustainability insights. Hopefully, the day isn’t far off when demonstrating lower environmental and social impact will become a competitive differentiator for manufacturers and other value chain partners, but to get there it will take strong relationships to make decisions based on shared data around carbon emissions, energy consumption, wastewater, and other data points. In the meantime, however, companies need to approach ESG data collection and collation strategically.

5 Things You Can Do Right Now to Sustain the Momentum

Sustainability analytics requires a lot of data from many different sources. Some of that data already exists and is standardized, while other data is coming from new sources with no established standards. In order for companies to collect and organize ESG data in a meaningful and efficient way, I recommend that companies start doing five things as they begin building out their sustainability insights platform.

1. Focus on the problem you’re trying to solve. This sounds obvious, but there is a massive potential for sustainability analytics to become too big to handle. Identify which issues and impacts are your priority and focus on that data first. If your goal is to reduce carbon emissions, identify your biggest energy users and take the time to understand the improvement opportunities. If your goal is to reduce water consumption, understand how water-usage data is gathered from your supply chain partners and the ways that water is used in their facilities.

2.  Don’t collect data without context. There’s a tendency to assume that data alone is useful, but it can be difficult to analyze when taken out of context. For example, measuring energy use alone won’t help if you don’t understand how energy is purchased and used in a facility and how that supports processes. Targeting your data collection based on identified hotspots helps to focus your efforts on areas that matter, and helps to prioritize reduction efforts. Plans don’t have to be perfect to begin, but spending some time determining what data is needed to meet goals can be tremendously helpful.

3. Treat data collection as a dynamic exercise. Gathering data isn’t a one-and-done, rinse-and-repeat process. It’s a dynamic exercise that may undergo a series of iterations before companies “get it right.” Choosing the right KPIs, time frames, context, etc. is a necessary investment if you want reliable, actionable data. Communication and trust with your data partners are critical success factors.

4. Communicate your goals with your partners. Sustainability is a journey we’re all on together, even if some brands may be further along the path than their manufacturing partners. It’s critical that brands and value chain partners communicate their data needs, what they plan to do with that data, and what their environmental/social goals are. Shared incentives lead to greater value for everyone.

5. Understand where your data is coming from. Going back to my herding cats analogy, combining data from different sources can be like herding cats and mice into the same containment area. Before you join data, you need to know the source of that data and its limitations or you risk corrupting your analyses down the road. The details matter.

Sustainability analysis is still a relatively new discipline and, like most new ideas, we can expect changes along the way as our collective understanding and experience grows. The most important takeaway for companies right now is to remember that we are all working towards a collective goal: solving the climate crisis. Creating shared value among brands, manufacturers, consumers, advocacy groups, and governments is the best opportunity to solve this challenge. Today, the collaborative spirit may take the form of top-down requests from brands to their value chain partners. Tomorrow, it may be bottom-up reporting that suppliers use to demonstrate their value to brands. In both cases, working together towards a shared goal is critical if we hope to get to a place where big data can lead to a better use of Earth’s natural resources.

About the Author

Cashion “Cash” East is a long-time sustainability manager, technical analyst, and strategist with over 14 years of experience in the environmental industry. He serves as the Director of Analytics for Higg, where he leads the methodology and data science team working on designing and building the technical underpinnings for sustainability software solutions for manufacturers, brands, and retailers. Cash was formally trained as an environmental manager and an LCA practitioner at the University of Arkansas’ Applied Sustainability Center. There, he focused on large-scale food and agriculture LCA projects. He later joined the Sustainability Consortium, where he used his unique mix of business and scientific skills to successfully lead several international sustainability campaigns. He has an undergraduate degree in Biology from Colorado College, and a Masters in Business (MBA) from the University of Arkansas.

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