Improving Sustainability Through Data: What Truly Creates Real Impact?

In many organizations, they already exist across a wide range of systems, reports, or Excel spreadsheets: sustainability data. But as soon as the goal is to use this data strategically, a central question quickly arises: What exactly should we analyze? Which metrics are truly relevant, and how do we identify them? Sustainability analytics does not create value through the sheer volume of data, but through focus. In this article, we show how organizations can select the right metrics and use their sustainability data in a targeted way for analysis and decision-making. We also introduce tools and strategies that have proven effective in our consulting work.

Why choosing the right KPIs is critical

Those who try to measure everything end up measuring nothing properly. Especially in the context of sustainability, the temptation is strong to capture as many metrics as possible: CO₂ emissions, water consumption, biodiversity, gender diversity, training hours, supplier ratings, social impact. The list is long and continues to grow with every new regulation.

Real steering impact, however, does not come from collecting as many KPIs as possible, but from strategically relevant use cases. Many organizations orient themselves around external standards and frameworks, which is fundamentally helpful. But the real value does not come from simply following checklists. It comes from consciously engaging with the specifics of one’s own organization.

Helpful guiding questions include:

Which sustainability topics are material for our organization? As a side note, we will take a closer look at how materiality assessments contribute to this in one of our upcoming blog posts.

• Which sustainability goals does our organization pursue?
• Which decisions do we want to make based on data?
• Which metrics help us make progress visible and manageable?

How do we develop impactful use cases within the organization?

Good analytics starts with the right questions, not with dashboards. A proven approach is to talk to different business units, because the best ideas often emerge where everyday challenges are most tangible. To structure this process, we use methods from the data driver toolbox, in particular the Business Model Canvas.

The Business Model Canvas can be used to identify use cases from different perspectives:

• Which problem does the use case address?
• Which solution can be improved through the use case?
• What benefit do we expect from a specific use case?

Another canvas is used to analyze the value chain. This analysis can also help identify use cases that address topics along the value chain. The identification of use cases is not completed after a single workshop. It should be repeated regularly to question, refine, and discover new use cases. Just as an organization evolves and changes, its use cases evolve with it.

Use Cases Identified, What’s Next?

Especially after an initial analysis using the format described above, a number of use cases will emerge that are all strategically sound. At this stage, further prioritization is essential. Not everything delivers the same value, and not everything is equally feasible to implement. This is where a prioritization matrix helps, allowing use cases to be ranked by feasibility and business impact. It is useful to define both business impact and feasibility in more detail and, if needed, design scoring scales for evaluation. This makes the assessment and comparison of use cases much easier.

Turning Data into Insights

Highly prioritized use cases deserve a deeper dive. The next step is to analyze which insights are expected from a given use case and which information is required to generate them. One thing is crucial here: individual metrics are rarely meaningful on their own. Real value emerges only when they are viewed in the right context:

• Over time: Are we moving in the right direction?
• Compared to targets: Are we on track?
• Compared to peers or the industry: Where do we stand in the market?
• In the context of the value chain: Where are the biggest effects created?

For developing impactful solutions such as dashboards, Datentreiber also offers an Analytics and AI Use Case Canvas. The aspects mentioned above play a central role in translating strategically important use cases into meaningful KPIs and data models, turning numbers into real steering impulses.

Conclusion: Relevance Beats Completeness

Anyone who wants to stay on top of their sustainability data needs clarity about what should be analyzed and why. Only then can data be turned into real insights. And only then does sustainability move beyond documentation to active management. The key is not the sheer volume of data, but a clear focus on what truly matters: strategically relevant metrics that support decision-making and make real impact visible.