Posted with permission from ESG Impact Zone.

By Tannis Thorlakson

When I started my career in impact investing many years ago, we had to beg companies for sustainability metrics. Their typical response: "The only number we track is revenue, so why don’t you use that?" Needless to say, my rudimentary Excel skills were entirely up for this simple task.

Today? No longer. We’ve entered an era of big(ger) data in sustainability where data chops matter. Strong data skills are an increasingly important asset for those cutting their teeth in the sustainability world. Those who know their way around data will rise to the top. So what data skills do you need and why do you need them?

The amount of data is growing

Most S&P 500 companies, 92 percent to be exact, publish sustainability reports each year. These are full of increasingly nuanced sustainability metrics, from greenhouse gas emissions to kilograms of food waste. Although they boil down to nice clean numbers in reports, underlying each metric is stacks of data, all that has to be organized and analyzed.

The plethora of reporting is only set to increase with regulations. The EU’s Corporate Sustainability Reporting Directive will require about 50,000 public and private companies doing business in the EU to report on key sustainability metrics starting in 2024. Public companies in the U.S. could also be required to beef up their climate reporting if the U.S. Securities and Exchange Commission’s (SEC) proposed climate disclosure requirements move forward. All indications point to a significant increase in the amount of sustainability data required.

The first core data skill you will need in sustainability is some ability to code in a flexible language. Python is increasingly the gold standard, although I’m a diehard R fan myself.

Data needs wrangling

Given the hype around big data you may think companies have all their information neatly stored in easily accessible platforms. Think again. Sustainability data requires pulling information from a multitude of sources, often running on different platforms or in different databases across a company. Sustainability has always been dispersed across many departments, and so is the data.

The ability to wrangle data (data scientist speak for combining and organizing data into a useable format) is critical to making sure you can transform what is collected into actionable insights. You may think this is possible with the perfect Excel sheet — the place sustainability data has lived for the last 20 years. However, coding allows for repeatable and scalable solutions which we need as databases get bigger and reporting becomes more frequent. For example, if you can develop an elegant coding script that pulls all of your fuel use data, you can then run it each time you need to analyze the data, whether that be daily or yearly without repeating a lot of work.

Data wrangling should be an integral part of your tool belt. The ability to clean, merge, standardize and make sense of messy and disparate datasets is often the unglamourous 90 percent of a data scientist’s work that makes the storytelling and action possible.

Good data means powerful storytelling

Convincing companies to make changes in the name of sustainability requires telling a good story. Clear, crisp graphs that tie sustainability and business metrics are critical to getting executives and other stakeholders on board.

Yes, companies use many standard graphs and dashboards to track sustainability results. But what is that key metric your CEO always talks about? Weaving in key business metrics to communicate your company’s plan to decarbonize will produce much better business buy-in across your firm than a rote GHG report.

Having strong data visualization skills and being able to distill lots of data into a simple visual takes practice. If you can master it, your will be an unmatched persuader in any meeting.

Understanding data makes you think smarter

Increasingly third-party platforms, such as Salesforce Net Zero Cloud or startups such as Altruistiq, provide sustainability data collection, cleaning and analysis services. So why bother learning the skills yourself? Understanding what is possible from a data perspective will make you a better manager, user and communicator of data even if you ultimately aren’t coding yourself. For example, our team recently noticed colleagues spending hours tediously compiling water use data into individual Excel workbooks. By applying our data skills, we helped to standardize and automate the collection into one master database, both saving them time and improving the usability of the data down the line.

Although I haven’t opened my coding software in months, knowing what good data looks like, how it can be effectively visualized, and when to automate key processes are skills I leverage every day. This understanding of how to use data effectively only comes from a foundation built on doing each step yourself.

Finally, don’t think you are the "coding type"? I can relate. I stayed in my Excel sandbox for years believing I couldn’t hack it as a data geek. I didn’t take my first coding class until graduate school. Turns out, I love it. It’s like problem solving on steroids. My advice to anyone wanting to make an impact in corporate sustainability — take a chance. You might even find you are a data geek after all.

Image from ESG Impact Zone.