Separating the wheat from the chaff when capturing ESG data

Written by Ian Geldenhuys, Programme Director - ESGCloud Team
15 Mar 2021

Where is a good indication of how important capturing environmental, safety and governance (ESG) data is currently: companies are changing their business practices out of recognition for the critical role that it plays to their brands.

There has also been a recognition of the fact that leveraging ESG data to garner value-adding insight can bring significant competitive advantage.

Clearly the awareness around the need to collect performance-related ESG data is there, but not all data is created equal.

The old adage – Garbage In, Garbage out certainly applies to collecting ESG data, with the quality of the insights gleaned largely dependent on the quality of the captured ESG data to start. High quality data will more likely result in high quality insights, which in turn will enable for better decision making, and ultimately, more effective results. Therefore, it is well worth the time to consider what are some of the metrics that can differentiate good quality data from bad data from the outset.

The first is apparent – accuracy. Inaccurate data is not just worthless, it can do more harm than good. Therefore, when gathering ESG data, consider its source. Did the data come from an electronic sensor, and if so, how reliable is the technology that was used? Or did it come from an algorithm or series of assumptions? Was there a potential for human error, or algorithmic bias, that may be tainting the result? As you look for accuracy of ESG data collected, you would also want to consider how data anomalies were flagged and dealt with.

Another metric that data ought to be measured up against is its timeliness. ESG data that was captured last week is certainly going to be timelier, and thus more relevant, than the same data set captured last month. That is not to say that historical data does not have its place.

However, it is valuable to distinguish between data collected for immediate response versus data that is collated to identify long term trends. Furthermore, it is important to assess the regularity of when data is collected. Knowing whether ESG data is collected once a week or once a month is essential, with greater frequency being preferable to extended periods lapsing between the capturing of current ESG data.

The third factor to take into consideration is how complete the data is, and whether there are gaps in the data set. Within this you also want to evaluate whether you are covering all meters, sensors and the like to provide a full picture for the ESG data that is being collected.

Last, but not least, is the veracity of the data being collected. This can be determined by distinguishing between data where its source is known and trusted, versus unknown and not as yet audited.

Evaluating captured ESG data against these four criteria can go a long way to addressing the problem ESG data in particular faces, of often being inconsistent and seldom being well-verified. It can also begin holding the capture of ESG data to a higher standard, leading ultimately to better decision making.

Related perspectives

ESGCloud is a SaaS platform that roots ESG in company performance by connecting ESG effort to competitive strategy and opportunities, and in turn profitability.

The software is innovative and intuitive to use, and features have been created with the end user in mind, making data collection and reporting easy through an all-in-one ESG tool.