Unifying ESG Data
Transforming scattered sustainability data products into a cohesive product suite.
Role
Lead Product Designer
Time Frame
May - July 2021
Context
S&P Global Market Intelligence began integrating sustainability data into its core platform. Until then, environmental and ESG data was distributed as static PDF reports. However as new datasets were added there weren't any cohesive design guidelines in place and the pages started growing out of control.
The Environmental Profile was the first dataset to migrate. It provided an overview of how companies impact the environment, showing costs from air pollutants, greenhouse gases, waste, water, and other factors. The page itself had too much going on and needed some attention . As I took I took it on myself to fix the page, I started creating styles and rules to inform how we could design the rest of the data suite. In the process creating a scalable suite of sustainability datasets.
My Role
This was my passion project. As I took over as Lead Designer for our ESG datasets I started seeing issues both in how datasets had been implemented and how those designs would extend to new datasets. As new datasets came through the ideas started to simmer I started to shape a vision of how we could unify the disparate data sets.
Persona
All the datasets were primarily being catered to Investment Bankers and ESG Specialists as part of their investment process. The environmental profile, specifically, was answering four key questions:
How does a company impact the environment?
How does it affect their bottom line?
How do the costs compare to the industry and peers?
What are the sources of that cost?
Answering these questions will inform investment decisions in the research & analysis part of the investment workflow.
The Challenge
The Environmental Profile was originally designed to mimic the old PDF reports. But when I dug deeper into the design and data, I noticed:
No consistency: no highlighting or templating in place to guide the user through the pages.
No history or benchmarking: we often weren't showing historical data and benchmarking despite the data being available.
KPI issues: our existing KPI boxes were dated and not flexible enough to accommodate the needs of new datasets.
Expanding scope: new datasets (Carbon, Fossil Fuels, Power Generation) were jammed into an existing design rather than building out separate profiles.
Working through these issues, I saw clearly the parallels to other dataset implementations and I moved forward trying to create wholistic design.
Research & Insight
I combined three sources of insight:
Customer Feedback: Given limited direct access to clients I had to rely on other sources. I analyzed 50+ client comments and complaints from Salesforce, highlighting confusion around navigation, missing resources, and unclear KPIs.
Jobs-to-be-Done: Defined distinct research workflows for Investment Bankers and ESG Specialists. Their key needs were:
Quantify sustainability risk and assessing future costs associated.
Understand the underlying drivers driving that cost.
Understand the company in context to it's peers, industry, and historical performance.
Understand alignment to official governmental goals and legislation.
Heuristic Review: Identified problems with page density, tabbed navigation, overlapping datasets, and metrics divorced from context.
Design Approach
To solve these problems, I treated the Environmental Profile as a pilot for the entire ESG suite. However, I worked to roll out multiple datasets before and during this design process, which also shaped my process. These included:
Sustainability (ESG) Scoring
Alignment to UN Sustainable Development Goals
Carbon Earnings at Risk
Alignment to the Paris Climate Accord
Physical Risk
Exposure to Harmful Industries
These all went through their own design process, some which were directly brought into this new suite and some that needed to be reverted from old designs.
Aligning Page Structure to User Workflow
To improve the flow of each profile, I created a predictable reading order to each profile that aligned with how users conducted research.
Unpacking the Environmental Profile
The catalyst for this project was the Environmental Profile. When I took over design ownership of the profile I already was thinking about a redesign and I decided to dive a little deeper into the data. As it turned out, the data that had been crammed in far exceeded the 4 questions above. In fact, the profile contained 4 distinct data sets with unique value propositions.
In my redesign my first move was to split the 4 datasets into separate profiles:
Environmental Damage
Carbon Emissions
Fossil Fuels
Power Generation
Modular KPI System
The existing metrics module was dated, and not flexible enough to handle the disparate data contained across all our ESG datasets. I redesigned it as a flexible component that could handle:
Monetary values
Percentages
Emission volumes
Rankings and SDG mappings
Benchmarks and budgets (e.g., Paris Accord goals)
This module became the standard across all ESG datasets, ensuring comparability and scalability.
Cross-Dataset Color Coding
To make the suite scannable, I created a visual taxonomy:
Orange = Environmental Damage
Blue = Carbon Emissions (Scopes 1, 2, 3)
Brown = Fossil Fuels
Light Gray = Nuclear
Standardized colors for benchmarks, deficits, and “other” financial metrics
This visual language connected all ESG profiles at a glance. Notable exceptions were the ESG Scoring, which had it's own centralized branding. Also the Sustainable Development Goals that had standard color coding as determined by the UN.
Historical + Benchmark Context
Most of these datasets were new to the market and investment bankers, and many of the metrics were arcane and meaningless on their own. In order to tell a clear story of company performance, we needed to contextualize with the following:
Historical performance charts - to indicate company trajectory
Industry averages - to contextualize against a comparable group of companies
Budget indicators - to compare against established scientific goals (e.g. 2° alignment)
We applied this to every data set to ensure that each metric could be viewed in context.
Transitioning Existing Designs
ESG Scoring
The ESG Scoring dataset went through a data change and a public rebranding, which resulted in a new color palette and some freedom to uproot the current design. Due to the depth of the ESG Scoring dataset we chose to separate the data, moving Score History and Peer Comparison to different pages (not pictured). While providing another page for digging into underlying data. We also focused on deeper exploration of each dimension of the score
Paris Alignment
In the process of transitioning this dataset, we also changed the name to Paris Alignment in order to to encompass the broader set of climate scenario’s in the accord (Well Below 2°C, 3°C, 4°C, 5°C). We created a higher focus on key metrics, and clearer visual indicators as to how the company aligned to the goals.
Carbon Earnings At Risk
In redesigning this dataset, we focused on simplifying the view. At the same time having all future time lines in a view, both for the chart and a more flexible data table. Having two charts didn’t necessarily clarify the story of the dataset, so we simplified.
Key Improvements
Streamlined navigation with separate profiles instead of tabs
Granular metrics with industry comparison across datasets
Standardized KPI modules for consistency and flexibility
Historical and benchmark context embedded across the suite
Cross-dataset color coding for faster scannability
Extensive damage resource breakdowns for transparency
The final screens can be seen below.
Results
What began as a redesign of the Environmental Profile evolved into the foundational design system for the entire ESG suite.
Scalable Templates: Reduced design anddevelopment overhead by creating reusable page flows and components.
Consistent Experience: Investment Bankers and ESG Specialists can now analyze sustainability data across Carbon, Fossil Fuels, SDGs, and more with a familiar structure.
Faster Rollout: Standardization accelerated implementation of future datasets.