Turning Data into Action: How Analytics Accelerates Decarbonisation

Every tonne of carbon counts. Companies that do not act risk losing competitive advantage, falling out of regulatory compliance, and eroding investor confidence. 

Across various sectors, achieving net zero has transitioned from a goal to a necessity. Investors demand transparency, regulators tighten disclosure requirements, and consumers increasingly favour companies that can demonstrate tangible progress. However, many organisations still struggle to convert ambition into measurable outcomes. 

The primary challenge lies in execution, not intent. Many carbon reduction targets lack the analytical foundation to identify emission sources, pinpoint impactful actions, and adapt strategies to change. Data and analytics can fill these gaps. By mapping and analysing emissions with the same rigour as financial performance, companies can transition from strategy to effective decarbonisation. 

Establishing a solid foundation 

Every decarbonisation strategy starts with measurement. Setting a baseline for Scope 1, 2, and 3 emissions goes beyond compliance; it is the reference point for progress. Organisations require accurate, consistent data from energy systems, procurement, logistics, and supplier records. 

Often, baselines are based on assumptions rather than precise data. For instance, after gathering detailed information, a global agribusiness discovered that its emissions were underestimated by over 30%. Adjusting the baseline revealed that over 60% of emissions originated from supplier operations, prompting a strategic shift to focus on high-impact areas. 

Even the most ambitious targets lack a solid foundation without an accurate baseline. 

Transitioning from measurement to strategy 

Once emissions are mapped, the next challenge is prioritisation. Not all interventions yield equal returns, and financial resources are limited. Analytics assists leaders in comparing potential actions by assessing their reduction impact, implementation costs, and co-benefits such as energy savings or operational resilience. 

A German beverage manufacturer employed modelling to optimise its energy system, including renewables, storage, and demand flexibility. This approach enabled a 25% reduction in emissions at a 19% lower cost than traditional methods. Advanced simulations identified the most effective interventions under various carbon pricing scenarios, ensuring investments align with long-term net-zero goals. 

Structured prioritisation ensures organisations do not squander resources on low-impact initiatives, while early wins help build momentum for broader transformation. 

From prediction to prescriptive action 

Descriptive analytics reveals past events, while predictive analytics forecasts potential outcomes. Prescriptive analytics takes it further by recommending the optimal course of action.

Walmart utilised supply chain analytics to assess the carbon intensity of thousands of suppliers. By focusing on the top 10% of suppliers responsible for nearly 50% of emissions, the company implemented measures that reduced its global supply chain emissions by approximately 27 million metric tons. Analytics not only facilitated targeted action but also enhanced cost efficiency across operations. 

Prescriptive analytics transforms sustainability from a reporting tool into a decision-making engine, guiding investments, supplier engagement, and operational adjustments in real time. 

Integrating continuous monitoring 

Decarbonisation is not a one-off project. Energy prices fluctuate, new technologies emerge, and regulations evolve. Analytics allows companies to remain agile by establishing continuous feedback loops. 

Dashboards that connect emissions data with operational and financial KPIs enable leaders to monitor real-time progress. In one manufacturing case, deviations from expected energy efficiency savings were detected within weeks, allowing immediate corrective action. 

Incorporating analytics into operations makes decarbonisation an integral part of core business processes rather than a peripheral initiative. 

Decarbonisation in action 

• PepsiCo, a global food and beverage company, has pledged to reduce direct emissions by 75% and value-chain emissions by 40% by 2030. Advanced analytics platforms track energy efficiency projects, renewable sourcing, and supplier interventions, aligning global operations with ambitious targets. 

• Olam, a multinational agribusiness, recalibrated its strategy after analytics revealed that over a third had underestimated its baseline. The insights enabled the company to prioritise supplier engagement and target interventions that accounted for 60% of emissions hotspots. 

• European industrial manufacturers increasingly use digital twins to simulate energy systems and model various abatement pathways. One facility achieved a 22% reduction in energy-related emissions while maintaining production efficiency, demonstrating how analytics can provide both environmental and operational benefits. 

  
  

These examples illustrate that analytics drives scalable decarbonisation, transforming initiatives from isolated projects to systemic change. 

A structured approach to analytics-driven decarbonisation 

Effective decarbonisation requires a structured approach. A phased strategy ensures data, analytics, and governance align with business priorities. 

• Data readiness audit – Assess maturity, quality, and integration gaps. 

  
  

• Baseline development – Accurately map emissions across operations and supply chains.

• Scenario analysis – Identify high-impact levers and model outcomes. 

  
  

• Prescriptive decision engine – Recommend project sequencing and capital allocation. 

  
  

• Monitoring and feedback – Create dashboards for live tracking and accountability. 

  
  

• Governance integration – Embed decarbonisation goals into operational and financial processes. 

  
  

This framework ensures sustainability is integrated into decision-making rather than treated as a separate initiative. 

Practical steps to initiate today 

• Focus on data hygiene: Clean, trustworthy data is your currency. Prioritise fixing gaps in your energy, procurement, and facilities data. 

  
  

• Model multiple scenarios: Don’t pick a single pathway. Run sensitivity tests under different carbon prices, energy costs, and regulatory changes. 

  
  

• Embed accountability: Assign business units targets derived from analytic insights. Tie their performance to emissions reduction. 

  
  

• Invest in adaptive knobs: Flexibility in operations pays dividends in volatile contexts. 

  
  

• Use analytics in supplier engagement: Score your suppliers by carbon intensity; incentivise improvements; shift sourcing where you can. 

  
  

Conclusion: Transforming Strategy into Measurable Impact 

Decarbonisation tests resilience, competitiveness, and operational excellence. Companies incorporating analytics into their carbon strategy gain clarity, agility, and measurable results. Analytics identifies the right interventions, sequences projects for maximum impact, and ensures that progress is continuously monitored and optimised. 

When carbon reduction is viewed as a strategic capability rather than a compliance task, companies can accelerate towards net zero while enhancing operations, reducing costs, and building stakeholder confidence.