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After any information gathering campaign, the question of how to use CSR reporting data arises. In short, how can you make your data speak for itself in order to better contribute to the management of the company's overall performance?
Why analyze CSR reporting data?
Once they have collected their CSR reporting data, companies proceed to analyze it. There are many benefits to conducting an in-depth analysis.
Analyzing non-financial data enables companies to better manage their material issues. Companies evaluate and compare the results of sites, subsidiaries, geographic areas, branches, business lines, etc.
What are the limitations of "traditional" analyses?
What kind of analysis are we talking about here? The analysis of raw data and the qualitative information that illustrates it. This allows trend analyses to be carried out on different axes (site size, country, business, region, continent, etc.).
To assess and understand these trends at the group level, we often start by comparing the curves of indicators corresponding to material issues. For example, we will study changes in water consumption, energy consumption, and the quantity of products manufactured at the same time. The idea is to observe whether or not they are moving in the same direction or, more generally, whether they are correlated with each other.
That said, working with raw data does not allow for detailed trend analysis.
As a result, the second step is often to calculate "intensities." These are ratios such as "energy consumption per product manufactured," "water consumption persquare meter of surface area," "number of training days per employee," etc. These intensities provide useful information. They allow us to put raw data such as "energy consumption," "water consumption," or "number of training days" into perspective.
However, exploiting these intensities is still difficult and sometimes even misleading:
- Intensity represents only one facet of a subject and does not take into account the actual complexity of the business. For example, the energy consumption of an industrial site depends on the number of products manufactured, but it may also depend on the technology used, the size of the buildings, the number of employees on site, the weather, etc. This allows us to assess the site's actual performance in terms of energy consumption. It is clear that intensity per product manufactured alone is not sufficient.
- When consolidated at the group level, intensity no longer means much. The reason: it can mask a wide variety of situations.
- Analyzing changes in consolidated intensity over time at the group level is much more complex than it seems. For example, a calculation such as "change in overall intensity from one year to the next at the group level" may give the impression of improvement when in fact this is not the case. For example, the intensity of "energy consumption/quantity of products manufactured" at the group level may decrease. The reason for this is that the most energy-intensive activity will have declined significantly (due, for example, to a drop in sales). In this case, it would certainly be wrong to conclude that the group's energy performance has improved.
How can we go further?
It is clear that understanding a company's actual performance requires a more detailed analysis of CSR reporting data. This requires combining a business perspective on the data with the use of statistical models. Such an approach requires an initial understanding of the factors (i.e., other indicators) that may influence the raw data we are interested in (such as energy or water consumption, to use the examples mentioned above).
Next, analyzing the data and its correlations with these factors using statistical models provides a clearer picture of the company's performance. Which sites or activities are performing best? How is their performance changing over time?
This type of analysis builds bridges between financial and non-financial data. It also opens up possibilities for the publication of future integrated reports. It promotes the sharing of useful information and best practices, as well as the (re)definition of priorities. It contributes to the development of innovation. This feeds into the company's portfolio of best practices.
The implementation of statistical models also makes it easy to identify sites with high potential for improvement. This exercise can be complex when working with hundreds of sites, each with different characteristics.
This type of analysis will also lead to the definition of realistic improvement objectives and the action plan to be implemented to achieve them.
It should be noted that, in line with this approach of continuous improvement, the analysis of CSR reporting data must include an analysis of "coverage rates." Why? In order to ask the right questions about the evolution of your CSR reporting protocol, your set of indicators (for example, to eliminate unnecessary questions), your validation process, etc.
Conclusion
Making data speak CSR reporting: this is a key challenge in managing and improving a company's overall performance. This complex and exciting exercise requires combining business perspectives with mathematical and statistical analyses.
This will only be possible for those who have reliable CSR reporting data and the necessary time. This requires having extra-financial reporting software. This software facilitates data collection, promotes the reliability of the data collected, and enables multidimensional data analysis.
Photo credit: UX Indonesia