The Reliability Index for Sustainability Data

While the terms “accuracy” and “audit-ready” are terms often used to indicate above-average data quality, the industry lacks a set of shared definitions and metrics.

To address these important questions, GLYNT.AI developed the Reliability Index for Sustainability Data. The Reliability Index provides a simple quantitative overview of what to expect from any method or system that is used to prepare sustainability data.

“Wait!” you might say, “We use spreadsheets!” Well that is a data preparation method too.

Types of Data Preparation Systems

Here is a description of the various data systems often seen by GLYNT.AI

Annual Reporting

Companies doing just annual reporting often capture and manage a few data fields. After all, they are preparing a one and done output, so hand-keyed systems and spreadsheets don’t seem too bad. Their sustainability teams are a bit frustrated with so much data work, but the ad hoc system meets the company’s needs.

Tracking Progress

After establishing their baselines for a few years, companies often want to track their year-to-year changes. Data errors become more obvious, so a data QA function is added to the semi-automated system – adding hours of effort. One SVP of sustainability told us her rule of thumb is that data QA in a semi-automated system is 3 – 4X the hours of the original data entry. GLYNT.AI’s TCO calculator shows a similar result. These extra hours often lead to data delays, making the data less reliable.

Audit-Ready Data

Reporting standards around the world are expanding and moving towards audited sustainability reporting. All too often sustainability teams equate audits with data accuracy, missing the role of data set consistency. When data sets are incomplete or contain duplicates, reporting errors abound. GLYNT.AI’s experience is that this can lead to 10 – 18% variation in reported emissions year over year. There is a neglected source of error in many audit-ready systems.

Finance-Grade Data

Finance-grade data is the gold standard in sustainability data preparation, as it is data that is as rigorously prepared as financial data. Finance-grade data comes from an Integrated Reporting system, one that uses an external source of truth – such as accounting records – to ensure data set consistency.
Data streams from direct emissions (Scope 1) are added.

Finance-grade data comes from a complete data system, with internal checks and balances. Further, when tied to accounting data, Integrated Reporting gets the benefit of double-entry accounting. Audit-ready systems lack these systemic controls.

Expanded Finance-Grade

Once a finance-grade data system is in place, it can be extended in two ways, with built-in method to reconcile the granular data to the corporate totals.

• Inclusion of sub-metering data. Data smart meters or IoT sensors is used to monitor energy and water use of specific equipment and assets. This operational-level detail and control leads to deeper savings. But it is easy to overestimate the savings if the sub-meter data is not validated and reconciled to the meter-level data on the energy and water invoices.

• Product Carbon Footprint (PCF) reporting. Financial accounting has a cost allocation system to determine the cost of a product, and cost accounting rules ensure that the components of cost are tracked correctly and sum correctly to the total. Similarly, sustainability has a usage allocation system to determine PCFs, and the sum of PCFs cannot exceed other reported totals.

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