By Reed Shapiro
Greenhouse Gas emissions increasingly play a part in customer, investor and employee considerations when interacting with companies. Tightening environmental and social justice policy, and demand for better products and cleaner performance are having material effects on how companies operate. How can the headache of capturing and adequately communicating performance in every nook and cranny of your company be turned from a hurdle to an opportunity for greater success?
Science-Based Targets, S.M.A.R.T. (Specific, Measurable, Achievable, Relevant, Time-bound) Goals, Key Performance Indicators, Benchmarks may all sound like the consultants’ equivalent of snake language. However, when it comes to showcasing best in practice performance, having sensibly constructed, lean data infrastructures with clean arteries can help you minimize risk and highlight opportunities alike. Beyond ensuring core performance, companies need to build confidence about how they will thrive in an increasingly uncertain future. Clear frameworks to track actual performance keep companies honest about whether they’ve actually progressed, or have simply tread water.
The chasm between the plan on paper, and clunky realities lies in collecting a myriad of data points across multiple business departments, facilities, teams, and other relevant inputs. Typically, one person or a small team, who typically run philanthropic giving and corporate citizenship performance, must now track down GHG data that is often alien to them, or to which they might not have access, and which may not even systematically exist at a company.
These teams must now analyze a product’s environmental impact for possible improvements, or whether a product (or service) with lower negative impact and comparable or superior performance could be developed—a calling far beyond the traditional scope of their role.
The typical result? Chaos. CSR teams and whole companies (especially the big ones) are spending up to half a year to a full year simply to translate “what’s going on,” into sometimes up to a dozen disclosures to what are often essentially the same stakeholders. This time could be used for taking action on the metrics presented, as opposed to talking about it.
To make it across the hazy abyss that is the quantification of Scope 3 emissions throughout complex value chains, to a have data reporting frameworks that help you run your business better, here are some quick tips to set up reliable and efficient systems for Scope 3 data collection, analysis, and decision making.
1. Define Scope
Without a clear understanding of what you’re trying to measure, one quickly becomes mired in ambiguity. It helps to start small with questions like, “what is our in-office electricity use?” From there, companies can move on to harder data to collect, such as, “what are the greenhouse gas emissions of the raw materials of this or that product?” Determine what departments, and what potential data points within those departments would be required for analysis. Sometimes you’ll only need one metric to capture what seems like a complex analysis. Sometimes you need millions, but don’t go down a rabbit hole when you need only to look under one specific rock. You’ll thank yourself later for taking the time to get it right in the beginning.
2. Identify Opportunities for Compartmentalization
Once departments and data points are identified, determine which role within those departments knows them off the top of their head, and can get them to you in 10 minutes. It pays to discuss what you are looking for with potential collaborators to confirm that they have access to and working knowledge of the metric you need. Developing respect and regular communication with these specific points of contact will make aggregating data much simpler when the time comes. Remember, the alternative doing it alone, possibly to be seen as a prying outsider. Having allies who know exactly what they need to do to uphold their end of the alliance makes for peaceable data collection, and allows them to feel part of a whole, while giving you neatly organized data streams that can be stacked up or looked at individually.
3. Create Minimally-Invasive Metrics and Reporting Structures
Great, you’ve found the right person with the right data. However, never-ending reporting syndrome will persist if the process and timing of data collection is at odds with gatekeeper’s day-to-day workflow. People have primary responsibilities, likely none of which include your carbon inventory. If the cadence or content of reporting conflicts with their tasks, their effectiveness an employee drops, and makes you look like a drag.
Instead, create reporting structures that are as simple as performing one’s day-to-day tasks. Often, the data points necessary for analysis, like monthly electricity use, or quarterly business travel, can simply be highlighted and sent out as they otherwise would, with analysts copied. Because analysts have determined a scope and built departmental relationships, they can pull from existing reports efficiently. The particular data points you need can be pieced together on your own time, as opposed to asking someone with a full plate to lay it out for you. The key is to make the collection of specified data points as much a part of an individuals’ work flow as is punching in in the morning. It’s OK to do a little leg-work behind the scenes (i.e. making more in depth calculations or analyses with raw data)—there’s time for that when information comes in on time and with no fuss.
4. Formalization of Process and Integration Into Workflow
The steps described so far cover strategies for efficient data collection of individual data points. However, one of the most critical aspects of greatly reducing the time it takes to conduct and make sense of Scope 3 emissions lies in the system-level data aggregation framework you create to parse through troves of data coming from across your organization. This is especially important at the enterprise level, where firms operate internationally, across hundreds of facilities, with dozens to hundreds of product lines, each with their own inputs, outputs, and value chains.
Due care must be taken at the beginning of the process to develop foresight as to what data will come from where, how it will be analyzed, and which other data points or sets it will need to interact with to make your analysis. Simple formulas, sheets, and models that can take data reported in from around the organization, and quickly spit out insights and metrics go a long way. An example might be a pre-set formula and spreadsheet that calculates emissions from transportation, with fleet vehicles, and their corresponding emissions factors (emissions per vehicle-mile) identified. All an analyst would have to do is import the miles reported by fleet drivers (something they should be tracking themselves already), and then, in a matter of seconds, out pops total emissions by truck, and by fleet, perhaps even by driver. Larger firms can consider tools that can scrape that data from existing reports, and thereby fully automate such emissions calculations.
By this point, any organization completing steps 1-4 should be able to collect large amounts of data (hundreds of thousands, millions of data points), and deliver key analysis to decision-makers in a matter of minutes from when data is collected—not days, weeks, or months.
5. Run the Business, Yield the Analysis
Companies taking the time up-front to develop fully integrated Scope 3 reporting throughout all departments, at all levels, in thoughtful, minimalistic, streamlined frameworks will achieve more telling insights more quickly. Whether it’s ESG analysis, or simply general reporting on a business segment, individual analysts should not be left to their own devices if a company wants prompt, reliable reporting. Analysts are only so good as the information they receive, the time it takes for them to get that data, and the definition of the analysis they are asked to make. Creating easy-to-fulfil reporting mandates for data keepers to get critical information to analysts should be a priority. Doing so doesn’t simply yield better analysis, it helps create a better understanding of why a company does X, Y, or Z for employees across the organization. More informed employees are usually more engaged employees. And the more engaged employees are, the more profitable your business.
Do it all right, and sooner than later, simply showing up, punching in, and selling product or service will drive reliable, insightful analysis, streamlined for helping improve the day-to-day operations of the company. Don’t take on data projects without taking these practices into consideration.