Data Fluency Is Now a Core Competency for Sustainability Leaders

As the sustainability field becomes increasingly central to corporate strategy, the expectations placed on sustainability teams are expanding rapidly. Once focused on policy writing, reporting, and stakeholder engagement, today's teams are now expected to drive financial impact, support business decision-making, and navigate increasingly complex regulatory environments—all using data.

This shift isn’t theoretical. Over two-thirds of executives now report a skills gap in AI and analytics among sustainability professionals, while job postings for ESG roles frequently cite data analysis, forecasting, and modeling as key requirements. Professionals with data skills in ESG roles earn an 8–10% salary premium—a signal of growing demand for quantitative capabilities.

For corporate sustainability teams, this is both a challenge and an opportunity: how do we upskill to meet this moment?

Data Fluency: A Strategic Imperative

Gone are the days when you could simply "ask the data & analytics team." Today, everyone is the data & analytics team. And so for sustainability professionals to benefit from these changing needs of the business world and to continue to add greater value, this isn’t so much about becoming coders or statisticians, it’s about developing the fluency to:

• Ask the right questions

• Identify and validate the right data

• Interpret results accurately

• Communicate findings clearly

• Support strategic, evidence-based recommendations

Whether you’re evaluating emissions reduction investments, identifying high-risk suppliers, or forecasting climate-related operational impacts, data fluency turns the sustainability role into a business advantage.

The Analytic Process: A Practical Framework

You don’t need to be a data scientist to lead with data—but you do need a process. That’s why we’ve created this seven-step analytic framework to consider for sustainability use cases:

> 1. Define the business problem

Example: How will rising temperatures affect productivity in our Southeast Asian factories?

> 2. Identify relevant data

• Internal: HR logs, audit reports, energy data
• External: Climate projects, labor laws, benchmarking data

> 3. Acquire and clean data

• Normalize units (e.g., °C vs. °F), remove duplicates, align naming conventions.

> 4. Choose the right analysis

• Use descriptive statistics to explore trends, or apply linear regression to test hypotheses (e.g., temperature vs. output).

> 5. Interpret results

• Are differences statistically significant? Are they operationally meaningful? What factors might be confounding your analysis?

> 6. Visualize for insight

• A clean scatterplot often communicates more than a page of text. Choose the right visual for the story.

> 7. Make recommendations

• Example: Present ROI estimates for heat mitigation vs. the cost of lost productivity.

Real-World Use Case: Heat Stress and Supply Chains

Let’s bring this approach to life with a practical example. Imagine you're a sustainability lead for a global company, and you're hearing more from operations teams about heat waves disrupting factory output. Workers are missing shifts. Productivity is falling. You need to build a compelling case for adaptation investments—but how? This is where data fluency comes in. You start by exploring a testable hypothesis, collecting relevant data, and analyzing the relationships. Here's how that might look:

A working hypothesis might be: Factories with indoor temperatures exceeding 35°C for more than four hours per day experience a 10–20% drop in productivity.

To test this, teams might combine:

• Temperature data from IoT sensors or weather models

• Absenteeism and productivity logs

• Local labor laws

• Benchmark data on cooling infrastructure investments

Using regression modeling, they can link heat exposure to operational output. From there, they can estimate financial losses and compare those to the cost of adaptive interventions like schedule shifts, PPE, or cooling systems. The result? A business case for action, rooted in both ESG risk and operational ROI.

Use AI as an Accelerant—Not a Shortcut

With the quick pace of AI advances, it’s no surprise that tools like ChatGPT and Perplexity are transforming the way sustainability professionals work. These tools can dramatically speed up research, automate portions of data discovery, and assist with drafting code or formulas for analysis. For example, a sustainability analyst might use AI to scan biodiversity risks across sourcing regions, pull emissions data from public disclosures, or create draft scripts in Excel or Python to run basic regressions.

These capabilities can save valuable time and help analysts focus on higher-order thinking. However, AI is not a replacement for expertise. It’s a support mechanism—a way to work faster and smarter, not to hand over critical thinking. The judgment of a skilled professional is still required to create the vision, validate sources, challenge assumptions, ensure results are meaningful and accurate, and drive action through business transformation.

Where to Start

So how do teams begin building this fluency in practical ways? It starts with small, accessible steps. Leaders can encourage their teams to get comfortable with basic tools, analytical thinking, and hands-on problem solving that aligns with real business needs.

A few starting points include:

• Framing a business-relevant data question

• Understanding core statistical concepts like correlation, causation, and significance

• Using familiar tools like Excel, Power BI, or AI copilots to explore data

• Communicating results effectively to finance, operations, and leadership

Simple hands-on practice can go a long way. Here are some quick and easy areas to get going:

• Cleaning supplier emissions data (e.g., standardizing units, labeling, benchmarking)

• Running a regression to test a basic sustainability hypothesis

• Building pivot tables to summarize ESG performance by region or supplier tier

The Bottom Line

Sustainability teams have a unique vantage point. Positioned at the crossroads of environmental risk, corporate performance, and long-term resilience, they are being called upon to do more than report and inform the organization—they are being asked to lead and create business impact. And in today’s business environment, leadership demands fluency in data.

Investing in these skills doesn’t just strengthen the reporting—it empowers action. It enables sustainability professionals to go from observers to operators, shaping decisions that drive real impact. When teams build data fluency, they unlock their full strategic potential.

Because in the end, the goal isn’t just to understand and track progress. It’s to accelerate it.

Getting the most out of your approach to data?

Tackling the challenges of today and creating the opportunities for tomorrow requires an end-to-end approach that turns challenge into opportunity. Partner with us to see how a data fluency paired with sustainable innovation can power meaningful business impact.