GLOSSARY
What is GLYNT.AI’s Sustainability Data Layer?
GLYNT.AI’s sustainability data layer collects raw input files, prepares sustainability data with the same rigor as financial data. It works as sustainability data management software for your energy, water, waste and emissions data, delivering audit-ready data for reporting, business use, and AI.
INTEGRATION
How does GLYNT.AI’s sustainability data layer fit into your systems?
GLYNT.AI’s sustainability data layer sits between your raw data and your applications, handling all sustainability data preparation and data management for you. It collects raw input files from the Inputs Layer and delivers audit-ready, customized reports and data sets to the Application Layer, with no extra work on your end.
WHAT IT IS
5 key takeaways on the sustainability data layer
Here are five things to know about GLYNT.AI’s sustainability data layer.Single system
It’s the one data system for all your sustainability data. A single system is more efficient to run and faster to audit than several separate ones.Source of truth
It serves as your single source of truth for sustainability data. The rigorous preparation built into the data layer gives every stakeholder the data confidence they need.Fits your stack
It fits into your existing software and systems. Use the reporting software, business tools, and AI tools you already have.Built to expand
It expands with your reporting and data-sharing needs. Add data sources, custom fields, or output formats as they come up.Saves time and money
GLYNT.AI provides the complete system that audit-ready data requires, so you don’t have to build one in-house, which is slow, uncertain and expensive.FAQS
Common questions about the sustainability data layer
What does the sustainability data layer do?
It organizes the full process that turns raw sustainability data into structured, audit-ready datasets — collecting source files, then extracting, testing, enriching, and harmonizing the data before it reaches reporting and governance.
Why is it called a "data layer"?
In software, “data layer” refers to the technology that sits between incoming raw files and the finished, structured data that comes out the other side. GLYNT.AI’s data layer is where the actual preparation work happens: data collection, extraction, testing, enrichment, before sustainability data reaches reporting tools, internal systems, or AI models.
What is the advantage of a unified sustainability data layer?
A unified data layer runs on “prepare once, audit once, report everywhere.” The same clean dataset can feed every downstream report without being rebuilt each time.
Is special training needed to use the sustainability data layer?
No. Day to day, the system runs on its own; no special training is required to use it. During setup, we’ll ask your team the key questions: What do you use your sustainability data for? Which fields do you need? Where do your raw input files come from? GLYNT.AI runs a kickoff workshop and a structured onboarding process to answer these questions. If you don’t know the answers upfront, we’ll add discovery exercises that help find them.
What do we need to deploy GLYNT.AI’s sustainability data layer?
This depends on your current setup. Typically GLYNT.AI and the customer collaborate during onboarding to tailor the GLYNT.AI system to your needs. For example, at one customer with a one-person sustainability team, GLYNT.AI handled all the integration and data mapping. For another customer with more complex IT systems, GLYNT.AI worked with the customer’s IT team to specify the integrations and data flows.
How is the sustainability data layer different from finance-grade sustainability data?
These two different terms describe the GLYNT.AI data system from two angles. “Sustainability data layer” describes where it sits: the term comes from enterprise tech-stack architecture, the same way you’d describe a data layer or application layer in any system. “Finance-grade sustainability data” describes what it produces: sustainability data prepared with the same rigor as financial data, so you can trust it the way you trust your financial numbers.