New Metrics

How to Make (Sustainability) Data Work for You

In the business world we are inundated with data on a daily basis: from bill of materials, formulations, and purchasing records to customer transactions, production volumes and utility bills. The amount of data available for use within organizations continuously increases — so much so that we aren’t just talking about “data” anymore, but “big data,” which puts us into a part of the metric system that I’m not sure we even learned back when I was in elementary school (petabytes of data anyone?). We all recognize there is a lot of data embedded within our business systems, but the real question is, how do we access, filter, organize and ultimately analyze all this data so that it turns into information — and more importantly, information that feeds into intelligent decision-making?

By Renee Morin | Published 10 years ago | About a 5 minute read

Sustainability programs are generally made up of various goals and the data-driven analyses needed to measure progress against these goals. As sustainability professionals we have the unique challenge of having access to lots and lots of data, but in most cases the data systems weren’t set up with sustainability questions in mind. Having been an environmental metrics and sustainability consultant for over 15 years, I’ve been exposed to all sorts of data challenges, some more complex than others. One of the most common challenges, especially for the larger multinational companies, is getting access to the data, or specifically, figuring out where it resides.

In order to find the right data, first you need to make sure you know what the question is you intend to answer.

Sustainability covers a wide range of topics from a corporate to product focus and from carbon to water to worker health. Ideally, a sustainability program starts by determining what a company’s priorities are and then setting goals to achieve progress around those priorities. In the best case scenario, data is used to set these goals, though often this step is skipped and goals are set based on peers’ trends, aspirational thinking, etc.

The next logical step is to conduct a baseline analysis that aligns with the goals that have been set. This is where the data mining can start to get interesting. As an example, one of the most common sustainability goals is reducing the corporate carbon footprint. To create the corporate carbon footprint, data types that need to be accessed include utility bills, fuel purchases, and refrigerant purchases from facilities, as well as fuel purchases for fleets. These data can be stored in multiple platforms, including procurement, logistics or in some cases in a stack of PDFs. One important aspect that is sometimes overlooked is the need to match the carbon (insert your metric here) data up with the corporate goal (CO2 per employee, per square foot, per sales). In this case, even more data systems need to be accessed and ultimately sliced and diced in a way that allows for regional, country-wide or facility-level visibility. This is the crucial point where the data become actionable and allows for companies to make decisions on projects that help them make progress toward their goals.

Other examples of sustainability assessments and the types of data and systems that are potentially accessed are included in the following table:

Table

Second, get the lay of the land.

At this point you have an idea of the types of data you need and where you think the data will reside. The next step should entail getting a good overview of the systems and who owns that data. Data owners commonly include procurement managers, brand owners, EHS facility staff and logistics directors. Recently we worked with a client who gave us access to no fewer than 6 different data platforms to help answer the question of how much material they purchase. Until you look under the hood, it can be difficult to know where you will find exactly what you need. Documenting this process can help those who come after you from duplicating the same efforts. We would all like to avoid going down the rabbit hole, but sometimes it’s unavoidable, and at worst, you can still learn something from the process. Even if you aren’t able to use a system for the current study, it may come in handy when tackling your next sustainability goal.

No Pain, No Gain

Unfortunately, it is rare to stumble upon a system that is already set up to handle the myriad data requests that arise in the field of sustainability. If you are lucky enough to get in on the front end of an enterprise resource planning (ERP) software implementation, get the stakeholders around the table, evaluate the current sustainability platform and integrate, integrate, integrate. This is not a simple process but one that will pay off in efficiency dividends.

However, 99 percent of the time we are not so lucky and must cobble together data from various sources. Finding a common denominator to link data sources can be challenging (e.g., companies may measure kilograms of materials coming in, but only have total units or sales going out), so we must be creative yet systematic in our approach. As one client said, facilities are like snowflakes (and the same goes for companies), so what works for one company may not work for yours. What does work though is taking the time to set your goals, determine the baseline, and assess the data systems that will help you conduct the analyses to achieve your sustainability goals.

We’ve gotten our house (mostly) in order, now what?

Once you have faced the challenge of manipulating and analyzing data to support your environmental sustainability goals it may be time to tackle your company’s social impact measurement and reporting matters. Developing a system to track social topics can have even more complications than environmental metrics due to the pseudo- or non-quantitative nature of social sustainability. In Part 2 of this series, we will visit some of the challenges faced, and creative solutions necessary, when dealing with data systems and qualitative responses related to social sustainability.