#takeaways ESG webinar 4: how to choose and implement the right tool for ESG data collection

CSRD & Data

We see six pragmatic steps as a best practice in your CSRD sustainability journey. In this webinar, we zoom in on step five: define detailed business requirements for the data management model & roll out the implementation with regards to data-gathering, data-input, consolidation of the data and output reports (KPI & metrics).

Companies must report on a substantial volume of data points, 1134 to be precise. There are different kind of data points:

• Numerical (30%) = monetary values, volumes, percentages, decimal and more
• Semi-narrative (13%) = data types used to enrich narrative disclosures (yes/no, lists, summary and more)
• Narrative (57%) = text blocks.

For a breakdown of all data types per ESRS, we advise you to download the EFRAG spreadsheet here.

The importance of ESG data goes beyond regulatory compliance. Think about enhanced reporting, meeting stakeholder expectations as there is a growing interest in non-financial data and overall transparency and risk management as informed decision making needs reliable data. Furthermore, efficient data management systems can facilitate day-to-day operations and strategic decision making in the long run.

Define your data management strategy

Since data is becoming more and more important to steer your organization and to meet compliance purposes, it is important to have a well defined strategy on how to proceed with (ESG) data in your company.

How to get started with the definition of a data strategy:

• Business strategy and environment: identify which data is crucial for your business to achieve your strategy. ESG will be definitely part of it, next to other relevant data (such f.e. Financials, operational data, sales data, etc.).
• Data inventory: make an overview of the most important KPIs, their definitions, KPI owners, source systems, etc. The input from your DMA analysis and fit gap analysis will be very useful input for this step.
• Data process and organization: how to govern the data quality and consistency. Which process & roles do you need and what is the current situation in your organization?
• Data infrastructure: how will data from different systems be collected and integrated so it can be used in the most effective way. Understand the current and define the desired maturity for the next 5 years.
• Data objectives and KPIs: based on the desired processes, organization and infrastructure you will need to define concrete goals you would like to achieve in the next period (f.e. 5 years). Make sure you can measure and monitor the progress of these goals.
• Roadmap: based on the defined objectives, identify a roadmap of initiatives and projects needed to achieve the goals of your data management strategy in the next few years.

Depending on the size of the company, and the importance of data for your company, different roles are involved to define your data management strategy:

• C-level to discuss strategy and the impact of data
• Middle management (all process owners) to define relevant KPIs (incl. ESG managers, HR managers,...)
• IT & Analytics for the infrastructure
• Data governance & HR for the processes and organization
• Finance, Risk and legal for legal and regulatory requirements
• Other relevant stakeholders (controllers, external experts,….)

How to capture ESG data

There are specific needs for ESG reporting. Think about the different types of data, complex calculations (e.g. carbon emission), combination of new and existing data, integrations with multiple source systems, … to name a few.

Different solutions can help to facilitate the ESG reporting process. We see 5 solution types to capture ESG data, including pros and cons.

1. Excel or custom built solutions - tailored to your needs, yet high maintenance, scalability is limited and implementation can be very expensive.
2. ESG reporting platforms (Examples: Diligent, UL Solutions, Sphera, Cority, Clarity AI… (Verdantix Green Quadrant) - for more complex organizations, designed for ESG reporting, yet can be expensive due to licenses and often external support needed and often financials are not included.
3. CPM / EPM Reporting Platforms (Examples: Tagetik, Workiva, Jedox, Vena, ... ) - for more complex organizations, can be extension if CPM / EPM is already used for FP&A or Consolidation, yet can be expensive due to licenses and often external support needed.
4. Topic specific ESG tools (Examples: Microsoft cloud for sustainability, SAP Sustainability footprint mgt, Salesforce Net zero cloud, Sweep, Sphera, … (Verdantix Green Quadrant carbon management) - for specific ESG process or data, f.e. DMA analysis, carbon emission calculation, carbon accounting, Taxonomy, …., yet can be expensive due to licenses and often external support needed, need for integrations with other tooling and you are dependent on the roadmap of these specific solutions.
5. Data & Analytics solutions (Examples: Microsoft Fabric/Synapse Analytics, Google BigQuery, Amazon AWS, Power BI, Tableau,...) - Central data layer; Reporting tools can be used to report on all sorts of data, incl. ESG related data, yet can be expensive due to licenses and often external support needed, a higher data maturity level needed and this approach is always a partial solution as other solutions will be needed to cover the full reporting scope.

How to select your ESG reporting tool

• Start from the data management strategy
• Perform a fit gap analysis: know what data and tooling is available and what isn’t
• Based on the gaps, determine if the current tooling is sufficient or additional software is needed

There are several criteria to take into account when selecting your ESG tool. Think about:

• Which steps do we want to support with an ESG solution (DMA analysis, data captation, …)
• How complex is the ESG reporting process and how important is it to be able to track the progress in a tool
• How many people are involved in the ESG reporting process
• Is there already a CPM/EPM solution in place in the company
• What is the level of detail of data we want to capture: very granular of very high level
• How much relevant ESG data is already available in an analytics solution (f.e. DW, Lakehouse,...)
• What integrations with other systems are required
• How important is auditability and version control
• What type of reporting is required
• Which complex calculations do we need to perform
• What frameworks are already supported and maintained in the ESG solution
• How is implementation consultancy and support available
• What is the roadmap of the solution

In conclusion, take the time to make the right choice for your tool selection and implementation as this decision will significantly influence your data collection approach in the coming years. During this fourth TriFinance ESG webinar we provided an overview of the different solutions to capture ESG data. Each solution has its pros and cons, which vary depending on the size, complexity, and requirements of the organization. 

Many companies have not yet made a final choice for a tool to collect ESG data. Keep in mind to develop a data management strategy early on in the process and look further than the regulatory requirements. Realizing insights that improve your efficiency and competitiveness go beyond compliance and will impact the future success of your company.