We’ve all heard about Net Zero objectives. Over the course of the past two decades, international climate organizations have met repeatedly to address climate change and determine how best to prevent its worst consequences. Though at the national and international scales many kinds of laws and resolutions have been adopted to this end, there is unanimous agreement within the scientific community that cutting our carbon emissions to as close to zero as possible by 2050 is the target we should strive towards.
But what you may not know is just how essential the right use of data will be to this international effort. In Data for Net Zero, a new study on the role of data in accelerating the journey towards Net Zero, Capgemini unearths the environmental and business benefits to companies of a robust, data-driven approach to understanding their emissions.
Two important facts from the study stand out. The first is that 53% of organizations that have embedded emissions data in decision-making have experienced an acceleration in their net zero journey; 53% have furthermore experienced an increase in transparency. And secondly, While 85% of organizations recognize the business value that insights driven by emissions data can provide — for instance, by enabling organizations to explore sustainable business models, mitigate business risk, and reduce operational inefficiencies — they are poorly equipped to capture and use emissions data.
The regulatory world has already brought us to new milestones on the journey toward Net Zero — organizations like the U.S. Securities and Exchange Commission (SEC) and the International Sustainability Standards Board (ISSB), as well as the EU’s recent Corporate Sustainability Reporting Directive (CSRD), are at the forefront of this change. The increased focus on ESG targets across all industries has made it all the more important for organizations to have good, reliable data on their own emissions.
Achieving investor grade data on a large scale across the Environment, Social, and Governance dimensions, with reliable and timely insights on climate risks and performance along the relevant value chains, will require companies to be equipped with the right tools and processes with a view to industrializing their data management.
With that in mind, what does it take to build an effective sustainability-driven data strategy?
Navigating the Data Jungle
The data landscape within most companies is almost never simple. Typically, a company’s data comes from multiple sources, takes on a variety of different formats and structures, and is stored across a handful of systems that are not interconnected. So what’s the best way to navigate this complexity?
Firstly, and needless to say, it can be costly and inefficient to dedicate full-time resources to chasing down this data, for weeks on end, in order to put together regular reports. To avoid this, it’s critical to find data ingestion tools that can interface with all of the relevant and most up-to-date environmental data, identify gaps in the data pipeline, and maintain data quality from end to end.
Second, it’s essential that companies work to develop a flexible, future-proof single source of truth for their data. You don't want to have to rebuild everything every time you make an acquisition or divest, adapting to the ever more complex emissions and climate related data on each occasion. Like a well-built ship that can weather any storm, your data platform should keep you afloat even when the seas are at their roughest.
Finally, a strong data operation will always build the right analytics for the right processes. Rather than siloing ESG targets within a single team and reducing them to a fixed set of low-impact, repetitive tasks, data-savvy companies will perform advanced analytics whose insights are shareable across stakeholders. This allows teams to work collaboratively on achieving sustainability targets and will act as a significant transformation catalyst for meeting ESG goals.
As organizations struggle with the complexity of the sustainability data landscape, it is important for them to shift to a data product model to connect applications and ensure a trusted reporting compliance and sustainability performance tracking. The Sustainability Data Hub we provide offers a solid, flexible, and scalable foundation for leveraging this sustainability data within the organization. By partnering with Dataiku, we equip the data teams from analysts to scientists in delivering the right insights to accelerate decision-making of their business stakeholders. — Vincent de Montalivet, Data for Net Zero Offer Leader, Capgemini
Selecting the appropriate data sources and building relevant ESG models can’t be done in isolation from the processes they are to impact. And the variety of processes which need to be ESG-embedded further complicates the challenge.
Finding the Right Tool
How, though, to make the right choice when it comes to selecting the best tools for accelerating your data-driven transformation? It comes down to having an agile analytics and data science platform, one that offers greater capabilities as the maturity of your team grows.
With Dataiku, for example, organizations can embrace all three dimensions of ESG (instead of prioritizing one over the other), foster collaboration between teams and profiles (such as business teams working with data scientists on ESG-specific initiatives), and operationalizing outputs.
In an environment marked by growing regulatory requirements for ESG, a platform-based approach resting on strong explainability and governance principles will guarantee the robustness of a company’s ESG initiatives over time, ensuring auditability both from internal and external control bodies.
Lastly, the right data platform will empower data and sustainability teams to develop data analytics and machine learning capabilities, improving their ability to build powerful models. Many companies hire for sustainability analyst positions, without requiring data science skills. If we want to accelerate our Net Zero transition and provide business teams with relevant, data-anchored insights, it will be crucial to equip those analysts with the right tool — one that grows as they grow.