(OLD) The Sustainability Data Buying Guide

The Challenge

Many companies have slid into a predicament: Years ago someone thought sustainability data should be reported. ESG or carbon accounting software was purchased. But no one looked closely at how the data got into that software. Surprise! The software expects you to enter data by hand or upload spreadsheets of data.The task of preparing the data for upload is pushed back to the customer.

The challenge of efficiently preparing sustainability data grows larger each year, with more frequent customer requests for your sustainability reports and more granular data needed to drive operational savings in energy and water.

Use this Guide to stop struggling with sustainability data preparation. It will show you how to pick a system that fits your needs, what to expect from automated solutions, and how to de-risk the migration from your current system to a more automated solution.

The Guide reviews the business needs for sustainability data, compares how different data systems perform, and ranks the systems in three key dimensions: Accuracy, Scalability and Cost. Here are the key takeaways:

Sustainability Data Preparation Needs a System

Customers tell us that data preparation is 80% of the time and costs

A flowchart showing raw sustainability data struggling to upload into existing software

#1 Automation saves time and money.

GLYNT.AI has helped dozens of companies, from mid-sized to global behemoths migrate off of spreadsheet, human-powered systems. For every company, we have found that automated systems are more accurate, faster and cheaper. Expect 50 – 90% savings over current systems. With better data and more time, sustainability teams can work insights and savings opportunities.

#2 There is no sweet spot.

Finding #1 applies at every business. Even smaller businesses can save 20 – 30% with automated sustainability data systems.

#3 Finance-grade sustainability data enables business wins.

Finance-grade data is so good that it is trusted by internal finance teams for capital budgets and planning, and by investors and lenders. Finance-grade sustainability data can be used to build the business case for savings, profits and value creation.

Change the Role of Sustainability with High-Quality Data

Trusted finance-grade data opens up business opportunities

A graph showing finance-grade sustainability data is both high quality data and automated

Sustainability Data Preparation in Your Business Systems

To set the stage, consider where sustainability data preparation fits in your business systems. First Source Files are pulled out of your business systems, then sustainability data is prepared, and finally the structured sustainability data flows into the same business systems or others. Sustainability data preparation is needed because this data does not naturally occur in current business systems. For example, accounting systems may house an invoice, accounting has only captured the data needed for payment, not the detailed cost and usage data needed for sustainability reporting.

Where Sustainability Data Preparation Fits In

A flowchart showing source files from business systems flowing into a sustainability preparation system and back out into multiple business systems

*includes ESG reporting, carbon accounting, sustainability, and other software applications

Sustainability Source Files are PDFs from invoices, certificates, bills of lading and so on. They are also files exported from health, safety, operational and environmental systems, often in CSV or XLS format. And Source Files can include vehicle ledgers, expense reports, and readings from IoT systems. Hugely varied, these Source Files also have frequent layout changes.

Your key sustainability data is trapped in these Source Files. It’s the job of your sustainability data preparation system to bring it all together, and deliver a single unified file for use in other systems. No other business system does it.

Select the Data Preparation System that Fits Your Needs

Your company may be trying to just get the data prep job done, or it may be trying to get outside financing that requires fresh and accurate sustainability data to proceed. You need to pick the data preparation system that fits your needs.

Everyone has a sustainability data preparation system, even if that system is spreadsheets or hand-keying data into ESG software. To articulate the levels of data quality and what it takes to deliver, GLYNT.AI has developed a five-category classification of sustainability data preparation. These systems differ by use case, data granularity, the reliability of prepared data, scalability and cost.

1: Reporting

A simple system to report corporate-level sustainability data each year. This typically covers Scope 1 and 2 emissions, energy and water. Estimates are used when it is difficult to get actual data.

2: Tracking

A higher-quality system that shows annual progress in reducing emissions, energy or water use. Year over year changes surface the data errors, so typically a dose of data quality testing (aka Data QA) has been added.

3: Audit-Ready

This system adds internal controls, tests and documentation, and audit-ready archives. Audit-ready systems deliver more accurate data, but often have trouble with the constantly changing assets and data sources that arise in large corporate footprints. Use audit-ready data on key projects to open up business opportunities.

4: Integrated Reporting

When sustainability data is integrated with financial records, the result is a more accurate set of data and defensible reporting boundaries. CFOs will appreciate that this system prepares sustainability data as rigorously as financial data. Integrated Reporting removes a data silo of badly prepared sustainability data, enabling the use of standard financial planning tools and a systematic scan company-wide for savings opportunities.

5: Operational Reporting

This system extends Integrated Reporting to cost and usage allocations for Product Carbon Footprint (PCF) reporting, and adds sub-metering to equipment and assets for deeper energy and water savings. Operational Reporting handles large volumes of data and many customized data fields.

The Five Types of Sustainability Data Preparation Systems

System

1.
Reporting

2.
Tracking

3.
Audit-Ready

4.
Integrated Reporting

5.
Expanded Reporting

Objective

Anual Reporting

Report progress towards announced climate goals

Reduced errors and disclosure risks with multi-layered system

Increase reliability and further reduce errors by aligning sustainability reporting with financial reporting. Integrate sustainability into financial planning company-wide

Build on Integrated Reporting by adding data and calculations to report Product Carbon Footprints (PCFs). Use detailed data to drive deeper operational savings

Unit of Analysis

Corporate

Site

Business transactions, Asset-level 99.5%

Sub-asset, Equipment and Product-level

System Description

Ad hoc, manual

Ad hoc,, manual. Data QA added

Automated, certified and compliant

Automated, certified and compliant. Increased testing and data handling capabilities

Automated handling of additional data and calculations, reconciliation to Integrated Reporting

Reliability Index Level

40%

50%

88%

99%

95%

Key Benefit

Easy. Learn from first reporting efforts

Reduced disclosure risk. Finance-grade data enables selected business projects.

Trusted by customers and investors. Find, execute and finance savings opportunities company-wide

Rich data sets enables PCF reporting and deep operational savings. Data reconciles to Integrated Reporting for increased confidence

Key Disclosure Risk

Delayed and unreliable reporting

Delayed reporting as tracking data surfaces more errors

Incomplete data leads to unexpected changes each year. Hard to define boundaries and track changes

Reporting risks are minimized. Sustainability data is as rigorous as financial data

If not reconciled to Integrated Reporting, PCF can be misleading, and financial benefits of detailed usage data will be inaccurate estimates

Key Execution Risk

Slow, error-prone system

Many hours of extra review leads to even slower data delivery

Costly multi-layered system takes months to prepare

This is a gib change. Phased implementation by ERP or AP system reduces risk

Unreconciled data is misleading. Reduce risk by deploying Integrated Reporting first

Compare Sustainability Data Preparation Systems by Reliability, Scalability and Cost

Sustainability data preparation can be a tough and frustrating job. But there is no point in making a change unless the new system is accurate, handles the volume and complexity of your needs and is cost effective. Automation should save time and money.

Reliability

While everyone wants more accurate data, we don’t share a common definition of what that is or what it takes to produce accuracy. Using best practices in financial data preparation and our own experience, GLYNT. AI developed the Reliability Index for Sustainability Data, a metric that reflects the quality of prepared data. We use this index internally to monitor and maintain data health in our systems. The Reliability Index has four components:

Character-level accuracy. For example, some systems mix up “0” and “O.”

Field-level accuracy. For example, if the field Amount Due, then $123.45 is extracted correctly at the character level and appears in the structured file in the Amount Due field.

Data set consistency. Summaries of sustainability data, such as carbon emissions by location, are not trusted when the reported levels keep shifting from incomplete data, duplicate data and other exceptions. Without data set consistency, shifts of 12 – 18% are possible.

Timely data delivery. Hand-keyed data is often late or produced months after the fact. But with year-round reporting, businesses need data preparation services that keep sustainability data up to date and deliver fresh data on time.

The Reliability Index is the product of each component, as seen in the table below. Notice how the Reliability Index increases as you move to the right. While you may disagree with a particular value in the table, the overall conclusion is clear.

The Reliability Index for Sustainability Data

By type of data system

	1. Reporting	2. Tracking	3. Audit-Ready	4. Integrated Reporting	5. Operational Reporting
Character-level accuracy rate	98%	98%	99%	99%	99%
Field-level accuracy rate	90%	95%	99%	99%	99%
Data Set consistency	65%	75%	90%	99%	97%
Timely data delivery	85%	75%	100%	100%	100%
Reliability Index	49	52	88	97	95

Notice the huge increase in data quality with audit-ready systems. And the second jump as you move to Integrated Reporting. The top three systems produce Finance-Grade Sustainability Data. GLYNT.AI itself started with a more simple system, similar to that of Reporting and Tracking. We also noticed that simply adding a data test structure to an ad hoc system actually slowed data delivery; it’s just many more hours of work. And also we noticed that it is a bit more difficult to keep the data set consistent when working with operational data, due to much higher volume of data from a significantly larger set of data sources. Overall, even small changes in data set consistency affect reliability and data confidence. We have also noticed how our systems underwent a huge improvement as we prepared for our SOC 1 audit. SOC 1 is the standard for financial data handling and reporting by public companies. It is well aligned with the audit standards for sustainability data from the IAASB and the reporting standards set by IFRS/ISSB. These reflect the best practices from financial data preparation learned over the past 25 years of SOX and COSO compliance. GLYNT.AI’s data preparation was audited to these standards, and we know they work!

Scalability

The story on scalability in sustainability data preparation is familiar: Automation reduces hours of effort by customer teams. So the graph below is not surprising. But notice the rather dramatic difference in the three lines. Hours of effort go up faster in sustainability than in other automation or digital transformation projects because of the nature of the Source Files – they have enormous variation and constant change. So volume growth is also growth in complexity of the job to be done. It is very difficult to achieve accurate data without automation. Semi-automated systems see their hours of effort climb quickly.

Scalability Requires an Automated Data Preparation System

A chart showing that automation saves hours of effort and uses a lower amount of data than incomplete automation solutions

The chart above tells the simple story: The more data you have, the more you need automation. Your data needs can grow with the use case, such as PCF reporting. Or they can grow with more extensive reporting, such as adding waste data and emissions from waste. The chart also hints at a second story. Putting this chart together with the Reliability Index, we find that the more automation you have, the more reliable your data. Here’s the reason why: Audit-ready systems watch and test everything. Take a look at the graph below to see how GLYNT.AI is audited and tested each year to achieve a SOC 1 (SOX level) certification. Every audit-ready sustainability data system is examined in the same way, so if you are building an in-house system, these are areas you’ll have to invest in.

Multi-layered audit-ready data systems require automation

GLYNT_WP_TheSustainabilityDataBuyingGuide_MultiLayeredStack

*includes ESG reporting, carbon accounting, sustainability, and other software applications

Only automated systems can do all of these tasks at once, and at scale. Automated systems record their processes software code. Smart automated systems keep learning, so as errors are found, they are identified and corrected and prevented. The code keeps getting better and better, reducing errors and the cost of corrections. The speed of data also gets faster and faster. And finally, automation delivers the scalability you need for AI. Gen AI and other advanced systems need accurate data at scale with enrichment to reduce hallucinations. Enrichment is The addition of business data (also known as meta-tags), and a good data system should add this for you. Advances in AI are coming fast, and will be applied to core business processes. Get ready with accurate, enriched sustainability data at scale. In sum, more automation increases reliability and scalability. And as the next section shows, it also cuts costs.

Cost

Everyone starts with hand-keyed data, the most simple of in-house data preparation systems. It doesn’t seem to cost much, so the system persists. But after a while, everyone becomes impatient with errors, so data quality assurance (QA) testing is added. This increases costs because more skilled staff – including managers – spend time to find and address exceptions. When it comes to audit-ready data, in-house developers face a daunting challenge: How to combine subject matter expertise, experience, and technology to deliver a totally new system with testing, controls and oversight that meets audit standards. It’s a tall order and will take months of work. At this point, the cost of data entry is just a small fraction of the total. The big investment is in secure data handling and audit-ready systems. The graph below summarizes these results for in-house systems using GLYNT.AI’s detailed Total Cost of Ownership (TCO) calculator for sustainability data preparation. To learn more about the TCO of sustainability data, go here

The Relative Cost of In-House Systems

GLYNT_WP_TheSustainabilityDataBuyingGuide_CostBars

The results will vary with the volume of data, the level of data granularity, and the details of data customization, but the key findings remain the same:

Without Data QA reported sustainability data is error-prone, but with Data QA costs rise significantly.

Audit-ready sustainability data systems built in-house cost a minimum of $250,000, in line with estimates from the EU and SEC and experience with the cost of SOX systems.

At every scale of data volume, automated data services save money when compared to in-house systems. There is no sweet spot.

Think of it this way: Very few businesses do their own payroll, most businesses use payroll services. The services are the experts, and they do the work better, faster and cheaper than in-house teams. Payroll service providers leverage their pre-built platform, and the high costs of compliance and auditability across many customers. Sustainability data preparation is just the same. GLYNT.AI’s experience is that upgrading to Integrated Reporting is fairly straightforward. The costs will depend on the number of AP and ERP systems used. Operational Reporting should be added once the Integrated Reporting system is in place, so that every additional data flow can be reconciled to the accurate summary measure reported. The costs will depend on the nature of the data added, the number of sources of data and the additional customized fields. Overall, the jump to Audit-Ready data sets the stage for cost-effective additions to data quality and volume.

The Bottom Line

The graph below brings the three key buying factors together. The size of the bubbles show the relative cost of various systems, and the placement of the bubbles shows how difficult and expensive it is to reach finance-grade data quality levels without automation. While an in-house team can build an audit-ready system, it is far cheaper to use a purpose-built automated service. So the bottom line advice is: Consider your use case with its current and future data needs. You may need to build your capabilities in phases, but many companies can jump to significantly higher data quality, at a lower cost, by using an automated service today.

An Automated sustainability data solution is simply better, faster, and cheaper

The size of the circle shows the relative cost per year

GLYNT_WP_TheSustainabilityDataBuyingGuide_Quads

Sustainability Source Files are PDFs from invoices, certificates, bills of lading and so on. They are also files exported from health, safety, operational and environmental systems, often in CSV or XLS format. And Source Files can include vehicle ledgers, expense reports, and readings from IoT systems. Hugely varied, these Source Files also have frequent layout changes.

Your key sustainability data is trapped in these Source Files. It’s the job of your sustainability data preparation system to bring it all together, and deliver a single unified file for use in other systems. No other business system does it.

Ready to Talk with GLYNT about Sustainability Data Buying?

Please contact us, we’d love to hear your data story! Liked this content? Want to take it to-go? Download a PDF version Or you can download a PDF summary of the checklist We have specific recommendations for CFO’s and the teams that support them, too

GET STARTED WITH GLYNT.AI

Special Advice for Smaller Companies

With just a few sites and limited reporting needs, smaller companies may decide that they don’t need an automated data service. Manually preparing reports for customers, regulators and investors is fine. This assumption is wrong.

An automated sustainability data service can save time and money. For smaller companies GLYNT.AI implements Integrated Reporting, working with your accounting software from the start. This is simply the most cost-effective solution for GLYNT.AI to deliver.

We’ve developed a bundle that enables smaller companies to get a monthly “Sustainability Close” five business days after the financial close. What was an annoying data entry task has turned into streamlined reliable data service.

Let GLYNT.AI take care of the sustainability data preparation task for you

Energy & Water Efficiency in Sustainability Data Preparation

Generative AI (also known as Large Language Models, e.g. LLMs) has jumped to the forefront of the corporate agenda in 2025. A key question facing every business is how to use this technology. But a consideration for sustainability teams is the heavy energy and water use of Gen AI. Recent studies have shown that Gen AI uses 5 – 10 times more energy than standard AI tools from cloud providers. GLYNT.AI built machine learning algorithms that do the same tasks as Gen AI. We released a white paper in late 2024 that shows how energy and water used by GLYNT.AI is less than 5% of that used by Gen AI for comparable tasks, “The Sustainable Way to Prepare Sustainability Data.” Use this white paper as a framework for evaluating the carbon, energy and water footprint of data preparation systems. It is also a window into GLYNT.AI’s purpose-built AI system, how it works and its significant efficiency advantage. Use GLYNT.AI to align your technology choice with your carbon budget

More Like This

For CFOs: Four Key Sustainability Data Risks to Watch

While this blog is aimed at CFOs, we encourage sustainability teams to read on. Read This

The Business Case for Sustainability Data

Sustainability management is no longer just about reporting, but about delivering key insights and quantifiable wins Read This

The Sustainability Data Buying Guide