Get the Type of Sustainability Data that Fits Your Needs
The four questions below quickly evaluate your current data preparation method and your options for change. GLYNT.AI has built an automated sustainability data system. We’ve been audited and certified ourselves. And as we built our So, we went through every stage of maturity for data preparation. So, the four questions and the results reflect years of experience. We’ve walked in your shoes.
Our experience shows that there are two insights that drive the right fit for sustainability data:
Automation drives data quality and scalability. An effective sustainability data preparation system delivers high-quality data, has low frictions to full scale, and is cost-effective. This also describes financial data systems, which have found that the key to reliable data is to: test, test, test. Automation is needed to do cost-effective testing at scale.
Automation drives cost savings. An automated system is much easier to maintain, improve, and update. This is critically important in the world of sustainability, where change is the norm. So a purpose-built, automated sustainability data system that is the single platform for all types and variations of input files is far more cost-effective than an ad hoc system that is a collection of point solutions
Keep these two insights in mind as you work through the four questions. They enable a crisp summary of options at the end. Let’s get started!
1. How Many Sites are Included in Your Reporting?
Less than 10 sites
10 – 40 sites
40+ sites
Sustainability data preparation tasks are friction and with annual reporting on just a few sites. But as sustainability moves from reporting to action, there must be full site coverage, granular data and audits. If the data system has limited automation, the hours of work skyrocket.
In fact, as the chart below shows, the hours of effort rise faster than the volume of data because of the high rate of change in sustainability. External forces make change the norm with sustainability data, including asset or site changes, new vendors, new requirements and so on. It simply takes more and more hours to maintain data quality.
Automated systems keep the costs down. They scale with less friction, and expand in a step-wise fashion when new features or capacity is needed. This is the well-known story of automation that has been told in business processes for years.
In sum, companies with just a few sites and a light use case might be able to continue with spreadsheet-based efforts. But as soon as the data requirements deepen, automation pays off. For companies with 40 or more sites, the volume of data is simply headache producing. Sustainability teams grow weary with the tedious work and the surge in hours before each deadline. Automation cuts those hours and shifts the workload from data prep to insights, analytics and building the business case for change.
Automation Drives Scalability
2. What Accuracy Rate Do You Need?
Best efforts is fine
We need to track progress to our stated goals
We’re going through an internal audit
We plan to have a third-party audit our data and reporting
We need sustainability data to be as accurate as financial data
Sustainability teams constantly worry about errors. They are often hard to find, and pop up at embarrassing moments, such as when site data is being analyzed for a project to reduce energy and emissions. An internal audit-raises the stakes because that audit team is used to working with a mature financial data preparation system, not a brittle and largely undocumented sustainability data system. And if you want to use sustainability data as part of a financing package, you’ll need a third-party audit.
The accuracy rate you need depends on how you will use the data.
3. What Role Does Sustainability Play at Your Company?
Just getting started
We report and track, but senior executives don’t interact with our data
We want to do one or two reduction projects each year
We’re worried about how to meet our stated climate goals
Our sustainability team aims to be a profit and value creation center
A key change in 2025 is the transformation of the sustainability function from a cost center that does annual reporting to a profit and value creation center that drives the business case for change. When internal finance teams and external investors and lenders rely on sustainability data to drive the detailed calculations behind every savings and reduction project, sustainability teams must have high-quality data. Too many stumbles in this review process, and projects will get shut out of the approval process.
The graph below shows how this change in roles requires high-quality sustainability data. GLYNT.AI has seen how far too many companies with high ambitions still in the lower left quadrant of data preparation: low automation, low accuracy rates. To achieve their goals, the team needs to move data preparation to the upper right, without wasting time and money by putting band-aids on semi-automated systems.
In sum, the value of higher-quality data vastly exceeds its cost. The very role of sustainability can be transformed with better data. And as the next section shows, high-quality data need not cost more. So, the question to readers is: Why are you willing to live with less than finance-grade sustainability data?
Meet Finance-Grade Sustainability Data
Data so good it is trusted by others
4. Cost
We’re a small team and on a tight budget, just doing reporting (1 – 2 FTEs)
We’re a small team, on a tight budget, but we need to drive change (< 3 FTEs)
We have a team, and more than 25% of our time is spend on data (3 – 8 FTEs)
We have a large team, do extensive reporting and while we don’t enter the data, we spend a lot of time rounding it up, checking it, and managing the system (8+ FTEs)
There is no time to waste in sustainability. As you look down the list of questions, notice that everyone is on a tight budget, with activity levels and goals that often exceed the resources available. Shifting hours from data preparation to other activities can change this equation.
The graph below is based on GLYNT.AI’s TCO calculator and reflects repeated runs of the calculations for companies of different sizes and data needs. There are two takeaways. First, automated data services significantly cut costs. It is simply expensive to build a system of data preparation. Second, a complete automated data service is cheaper than an in-house system at all levels. A purpose-built, automated system is quite efficient. Point solutions, such as a utility bill data service, can help to automate certain data sources, but fail to complete all the tasks needed for finance-grade sustainability data.
An automated sustainability data service is simply better, faster and cheaper at every scale.
In sum, regardless of how you answered the questions above, an automated data service is likely to deliver savings.
Automated Data Services Save Money At Every Level of Automation & Data Quality
The Bottom Line
With the power of automation and compelling economics, an automated sustainability data service is key. Here’s how to get started at every level.
My company has 10 sites or less. We’re just getting started or we’ve reported before
Use an automated sustainability data service that connects to your accounting system. It’s simple and cost-effective. You’ll immediately jump to an Integrated Reporting level of data quality. Get the gold standard of sustainability data at a low cost.
My company has not done sustainability reporting before, we’re just getting started. We have more than 10 sites
Start with an automated data service on a selected region or group. Leverage their expertise and experience to put data in your hands. Learn by doing, and keep your focus on insights, analytics and savings.
We have 11 – 40 sites, and we’ve got a semi-automated system in place
An automated sustainability data service will save time and money. Plan ahead to avoid point solutions and costly migrations. Evaluate the path from where you are to full automation. Break it up into phases if needed and prove out the benefits step by step.
We have 40+ sites and we’re already reporting
Run towards an automated service. You’ll find substantial savings and more reliable data. Ad hoc, in-house systems often have multiple streams of incoming source files, lots of bespoke data transformations and customizations. This is a recipe for cost and error. Streamline and get better data with an automated service.
We need to report Product Carbon Footprints (PCFs) to our customers
Lay the foundation with Integrated Reporting, then expand to PCF reporting. A separate PCF feed will not be aligned with the high data quality of corporate reporting, leading to confusion and lack of trust. An automated service will save you money; be sure it includes a built-in customization feature, as you’ll need that for verified bespoke PCF calculations at scale.
We want to gain operational savings by adding in sub-meter or IoT usage sensor data
Lay the foundation with Integrated Reporting, then expand to sub-metering/IoT data streams. Make sure the savings results reconcile at the meter, site and corporate level. It is far to easy to overestimate energy savings. Also, reconcile costs with detailed analysis of your invoice data. Many costs are rather fixed, and don’t contribute to the savings on a reduction project. Use an automated service to save money and deploy in stages derisk this high-volume data project.
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