Effective CSRD data collection

Introduction

The Corporate Sustainability Reporting Directive (CSRD) presents companies with the challenge of producing comprehensive and accurate sustainability reports. In order to make these reports effective and compliant, structured and careful data collection is essential.

Define data point list

In order to determine the relevant data points for your company and to create a list of data points, the requirements of the CSRD and the associated disclosure requirements must be understood. First, a double materiality assessment must be performed to identify material topics, sub-topics and sub-sub-topics. Companies must provide this data as part of their CSRD reporting.

The double materiality assessment does not only define which topic-specific standards (e.g. E1 Climate Change) are material for companies, but also which sub-topics and, for some ESRS categories, also sub-sub-topics are material. In order to compile a concrete list of data points, the allocation of disclosure requirements is relevant.

The allocation of disclosure requirements is crucial, as each disclosure requirement in turn contains a precise list of data points that must be reported on. These disclosure requirements range from general disclosure requirements to metrics and targets.

While EFRAG precisely defines the relationship between disclosure requirements and associated data points, it only provides an assessment of their connection to sub-(sub-)topics, without offering a comprehensive mapping. Tanso’s software, developed in collaboration with auditors, enables companies to automatically align their reportable data points with ESRS requirements, significantly reducing effort and complexity.

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Differentiate between data point types

Once the list of data points has been created, it is advisable to familiarize yourself with the types of data points defined by EFRAG. This will help you to better understand and narrow down the specific reporting requirements for your company.

Data points can be divided into five categories

Mandatory data points

Must be reported, regardless of company sector or size.

• Example: E1-6_01: Gross scope 1, 2, 3 and total GHG emissions per scope.

Phase-in data points

Have a transitional period and do not have to be reported in the first reporting year if the company has less than 750 employees.

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Dependent data points

Are dependent on other data points and only need to be reported under certain conditions.

• Example: E1-6_11: Gross GHG emissions from Scope 3

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Voluntary data points

Do not necessarily have to be reported, regardless of the company sector or size.

Alternative data points

Offer the possibility to choose between different data points.

• Example: E1-6_20
  Percentage of market-based Scope 2 GHG emissions associated with purchased electricity bundled with instruments.
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• Example: E1-6_04
  Gross GHG emissions of scopes 1, 2, 3 and total GHG emissions (GHG Protocol)
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• Example: E1-6_05
  Gross GHG emissions of scopes 1, 2, 3 and total GHG emissions (ISO 14064-1)

When talking about data points, the contextual relationships of the data points should be understood. Intuitively, data points are primarily associated with numerical values. However, data points in the sense of CSRD must be understood more broadly. Differentiating the content and understanding what is required is crucial in order to develop a process that can be repeated annually.

Set up the data collection process

Once it is clear what data is required, the next step is to plan the actual data collection. In the beginning, this always requires more effort, yet it is essential in order to minimize future expenses. Therefore, it is advisable to establish an effective process from the outset and define clear responsibilities.

The differentiation of data points in terms of content sets a baseline through which processes for data collection can be structured:

rioritize data collection

The prioritization of data collection according to controllability, complexity and data availability is crucial. That's because extensive data points and CSRD as a sustainability management element require a clear focus in order to be able to work effectively with often limited resources.

• Example:
  E1 Disclosure requirements show challenges with transition plans and Scope 3 emissions, which are highly complex and difficult to access data for.

Define clear responsibilities

Clear responsibilities prevent ambiguity, optimize the use of resources and facilitate the tracking and monitoring of processes. You should therefore define the following responsibilities when setting up CSRD data collection processes:

Collect quantitative data

Quantitative data points are characterized by the fact that they must be collected systematically and usually annually. As they are often collected on a site- or company-specific basis, many people are involved across different locations. A clear and repeatable process is therefore particularly important.

Processes for collecting quantitative data must be designed in a global setting so that different departments and locations are involved in the data collection. For example, financial data can come from Controlling and logistics data from the Logistics department. This quantitative data is then bundled centrally and forwarded to the head of sustainability for approval.

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Collect qualitative data

For qualitative, text-based data, existing documents and guidelines should be collected and consolidated. Qualitative data points can range from policies and strategies to process descriptions. It is therefore crucial that these data points are well aligned. The process for consolidating these data points should be collaborative and ensure that they have gone through a management approval process. Existing documents can serve as a basis, which need to be carefully reviewed and aligned to ensure that all relevant information is captured consistently and comprehensively.

CSRD reporting with Tanso

Tanso enables the efficient creation of reportable figures, tables and reports. This allows data queries to be coordinated centrally with KPI managers in specialist departments and locations, tailored to the company's organizational structure with extensive options for process design and visibility control. The use of artificial intelligence (AI) optimizes the processing of existing information from other documents in data collection. In addition to functions for data collection and reporting as well as central process coordination via task management, Tanso offers active management of strategies and transition plans as well as dashboards for progress monitoring, thus enabling effective sustainability controlling.

Outlook

The ESRS data points collected will be included in the sustainability statement in the non-financial section of the management report and supplemented with additional information, such as from the EU-Taxonomy. They are also converted into a machine-readable XBRL format that meets the requirements of the CSRD. The information is tagged for better identification and allocation.