QMI SAI Global

QMI provides verification services for organizations that want an independent affirmation of their Corporate Sustainability or Environmental Report, or greenhouse gas emission assertion.

The purpose of verification is to determine if the claim or assertion is accurate, complete, consistent, relevant and transparent. Leading organizations are requesting verifications to enhance the credibility of their public claims, to meet association requirements and to qualify for particular programs. Verifications are very much focused on data, although there is a component that addresses the data management system.

A previous article discussed the verification sample plan and the role it plays in focusing the verification work on the data and practices most at risk of having material discrepancies. As promised, this article will address the practices of actually verifying the data in the report or assertion. Click here to read Part I

Overview of Data

It is important to recognize the stages that data goes through, as discrepancies can develop at any point, given the fact that it is being handled and modified throughout the process. The dictionary defines data as “factual information used as a basis for reasoning, discussion, or calculation.”

The typical full cycle begins with an initial measurement. This task might be accomplished with the use of an instrument or not, it might be automated or not, and it might be quantitative or not. It is then recorded and collected. At this point, it is often transferred and stored temporarily until it is manipulated in some manner before being stored again. The final steps usually involve analysis, transfer and reporting of the data. An example for waste follows:

• Physical: something that can be seen or touched (e.g. real time monitoring results)
• Documentary: results that have been recorded (e.g. logs or invoices)
• Testimonial: information obtained from interviews

Preparing to Verify Data

The right data needs to be available to verify. There is no point in verification if there is not enough data; the results will not be valid. It is also inefficient to overkill the verification by amassing and analyzing significantly more data than necessary. The “right data” can be defined in terms of whether it is sufficient, reliable and relevant. In other words,

• Is there enough data?
• Is it physical, documentary or testimonial?
• Is it related to the assertion being verified?

There is an inverse relationship between sufficiency and reliability/relevancy. The more reliable and relevant the data, the less is required in order to arrive at a credible conclusion in an efficient manner.

Verification Methods

There are various methods available to verify data, each taking a different perspective on the data. The selection of which to use is based on which fits best with the data on hand and the value in using a range of methods over the course of the verification.

• Vouching: follow the audit trail back to the raw data and determine if there is evidence to support the reported data. This approach often uncovers errors in the data.
• Re-computation: check the calculations applied to the data. Take input data, apply the calculations and compare it to the recorded data. This approach also often uncovers errors in the data.
• Re-tracing: Locate the original source data and follow it through to the report or assertion. This approach often uncovers omitted data.
• Confirmation: Check with another party if the data is correct.

If errors are uncovered, the verifier will need to determine whether the errors are material or not. Material discrepancies indicate that the assertion has not passed the test of being accurate, complete, consistent, relevant and transparent enough. The verifier and organization will have clearly defined what constitutes a material discrepancy at the start of the verification process.

Conclusion

The ability to verify is a requisite skill for a verifier, but it should also be considered valuable to the owner of the data. The ability and desire to verify ones own data will lead to long term benefits in terms of a reduced need to investigate and resolve data problems, better data for improved decision making, reduced liability from erroneous reporting and improved credibility.