Giving meaning to data
Companies produce more ESG data every year than they realize. The challenge lies in their ability to locate, understand, and reuse it from one request to the next.
Temps de lecture estimé : X min
In the first article of this series, Patrice establishedthe conviction that led us to build Harnest. Here, I want to revisit the field diagnostic that confirmed it—the one I have observed with our clients for several years, which we previously touched upon regarding the real cost of ESG data in Excel.
A data volume that grows every year
Every year, companies produce more non-financial information, particularly ESG data, regarding their operations, suppliers, employees, sites, consumption, risks, and commitments.
Added to this is the data requested for audits, investor questionnaires, tenders, certification processes, or ESG and CSRD reportingcampaigns.
The more an organization seeks to manage its business with precision, the more information it produces. The challenge lies in being able to retrieve this information, understand it, and reuse it when a decision requires it.
Producing is not enough; you must also interpret
Some of this data is produced internally: consumption, headcount, incidents, action plans, and more.
However, another part must be sought out and interpreted, even though the company is not the source of it: the composition of its value chain, a critical supplier's exposure to climate or geopolitical risk, or regulatory changes in a country where it subcontracts.
This data must be collected, verified, linked to the relevant activity, and then reassessed as the context changes.
Retrieving data for effective use
The same information may be requested multiple times throughout the year:
- an investor wants to understand the company's exposure to certain risks;
- a bank is analyzing a financing application;
- an insurer is evaluating the robustness of prevention measures;
- a client is incorporating sustainability criteria into their purchasing;
- a regulatory authority is requesting supporting documentation as part of the CSRD.
These requests do not only come from outside the company.
Internally, management needs this same data on an ongoing basis to steer the business without blind spots: anticipating a risk before it becomes critical, making investment decisions, and tracking the progress of an action plan at the right time rather than just at the annual review.
For many companies, every new request triggers a fresh cycle of searching, verifying, reprocessing, and formatting. A significant portion of effort is therefore spent reconstructing information that is already available elsewhere in the company. You need an organization that allows you to mobilize this data easily, regardless of the use case.
This demand-driven collection logic comes at a cost, both in time and reliability. Later in this series, we will return to how things change with a continuous approach to this data, rather than collecting it from scratch for every request.
You also need to understand:
- how the data is defined;
- who is responsible for it;
- which activities it relates to;
- what decisions it informs;
- how it evolves over time.
Isolated data informs. Data connected to its context becomes useful for decision-making.
A diagnosis already familiar to finance
For a long time, the finance function faced the same difficulties that CSR or Risk departments experience today: information spread across multiple departments, different reference systems for different entities, and reprocessing required at every consolidation.
ERPs were not developed to generate reports, but to organize data, structure processes, and enable multiple departments to work from a single source of truth.
Reporting has become a byproduct of this rather than an objective.
Non-financial data is following a similar trajectory today: its volume is increasing, its uses are multiplying, the number of stakeholders requesting it is growing, and its quality directly influences strategic decisions regarding ESG data governance.