For decades, large corporations have generally viewed ESG initiatives as “nice to haves.” In fact, at the start of my career when I managed operations for a consumer goods company, ESG wasn’t a term we ever used, but when it came to sustainability practices, we did the best we could in the absence of any guidelines or regulations.

Over the years, I’ve watched as organizations across nearly every industry have increasingly emphasized their environmental, social, and governance initiatives. And in some cases, (many very publicly documented) companies have chosen to terminate lucrative business partnerships because the other failed to invest in and uphold ESG commitments. 

I’ve noticed a tremendous change in how companies invest in their ESG initiatives. No longer is it just their peers or employees holding them accountable; it’s also national and international governing bodies. In 2020, the U.S. Securities and Exchange Commission created an ESG disclosure framework for consistent and comparable reporting metrics, and just recently the organization amended that framework to deepen the level of reporting required from organizations. And in March of this year, the U.K. Task Force on Climate-Related Financial Disclosures mandated U.K.-registered companies and financial institutions to disclose climate-related financial information. 

It’s this very shift that has convinced organizational leaders that just having ESG initiatives isn’t enough anymore. It’s the ability to accurately and consistently report ESG metrics that may ultimately make the difference for a company to thrive in the next era of sound business practices. 

When you look at this new challenge for ESG reporting, there’s simply no denying it: The single most important factor in successfully adhering to ESG standards is data.

Nearly every large organization has a data infrastructure in place already, and naturally, organizational leaders are beginning to think about how they can derive ESG insight and reporting through the existing infrastructure. Unfortunately, what they’ll end up finding is that their infrastructure doesn’t actually stand up to the deep level of reporting that will be required of their organization going forward. 

The best first test of this is in how an organization is currently handling its data to derive valuable business insight. If an organization already lacks accuracy, consistency, and context within its own data, it will find it incredibly difficult to get ESG execution right. In fact, if an organization isn’t already investing in the integrity of its data, it is already behind the curve. And I can guarantee you that the newest ESG regulations will only widen that gap. 

So what’s the secret recipe to not only meet but also exceed metric requirements so that your data infrastructure is ready for the next era? There are four key ingredients:

1. Data integration

A data infrastructure must have the ability to integrate data, regardless of how it was captured or delivered. Through the integration, an organization can see a complete view of all their data, in one place, to spot trends that wouldn’t be visible if the data lived in silos. 

While this seems like a relatively simple concept, it’s incredibly complex. While most large organizations have many internal functions who all conduct business on multiple operating platforms, these organizations also have data siloed across third parties who they do business with. In just the shipping industry alone, a packaged good can change hands multiple times throughout the supply chain going from manufacturer to international carrier to port authority to trucker to distributer to retailer. Accessing data throughout the chain of command and seeing it in one location is an inherent problem that only data integration can resolve. Understanding the profile of that data, the provenance, the implicit and explicit assumptions, and calculations that get made using that data, and finally, observing that data throughout its lifecycle is a baseline requirement for accurate ESG reporting.

2. Data governance and quality

There’s a common expression around data quality: “garbage in, garbage out.” The expression is common because it’s completely accurate. Not all data is created equal, and if you’re working with crummy data, you’re going to report crummy results. A solid data infrastructure not only brings data into one place but has the ability to clean it up — to govern its quality — at the same time. A little cheeky, but I’ve found #yourdatasucks to be very true.

While most data infrastructures do have some ability to govern data, it’s often a very tedious and manual process — and many of these initiatives are solely IT programs. What is required is a board-level mandate on data and business-led and business use case arguments for tools that automate the process, not only to save time but also to offer real-time analytics to inform in-the-moment decisions. The timeliness of data governance and quality is going to emerge very soon within ESG initiatives as organizations must quickly pivot to align with environmental, social, or governance events that take place with little warning.

3. Location intelligence (LI)

LI is probably one of the trendiest abbreviations in technology right now, and there’s good reason for it. Location can bring in the element of context that data on its own tends to lack. Take for instance two facilities built 100 meters apart. Despite the close proximity to one another, each can have radically different environment impacts, hazard exposures, and resiliency indexes — all of which impacts on how your business infrastructure (i.e., supply chains for many of you) will operate as climate continues to impact every part of the world. The social impacts are massive as population migration is considered in the context of livable land, and making capital intensive decisions without these insights is foolhardy.

Location matters. And it matters for every aspect of every business. Data infrastructures should have the ability to know everything possible about every person, place, or thing involved in their commercial enterprise. The breadth of information available on this topic is enormous, but getting it into a digestible, consistent, and accurate form can be very hard.

4. Data enrichment

Like LI, data enrichment is the context in data. It’s the additional attributes to a single piece of data that can create a clearer, more informed decision. I think about data enrichment especially when looking at social data because it allows you to go deeper when looking at metrics around people. 

Diversity, equity, inclusion, and belonging have become business imperatives for many organizations as of late. In the technology industry especially, organizations are all too happy to report on how many women they employ. But this number is meaningless without additional data points. The breakdown of male versus female in the workforce doesn’t tell me how many of these women are in leadership positions, or how many are considered minority, or even how many oare also working caregivers, whether it be for their children or an ailing relative. It’s this kind of insight that matters to people — especially new talent — because they want to be able to see themselves in the people that already work there.

Organizations big and small have undergone so much change in the last two years. With over 30 years in my own career, I’ve never seen this degree or speed of change before. Whether it’s learning to operate in a global pandemic or adapting to a work-from-home culture or just retaining employees during the Great Resignation, we are all witnessing a complete transformation of business operations. And we’re on the precipice of yet another — ESG. How ready is your data to take it on?

Pat McCarthy is Chief Revenue Officer of Precisely.