Growing environmental, social, and governance (ESG) reporting requirements and local and national government and industry regulations, are adding pressure for enterprises, particularly for those in the oil and gas, port, mining and manufacturing sectors. Reducing CO2 emissions is a business imperative across the entire value chain, and even the most energy-intensive industries, such as chemical manufacturing, must balance sustainability efforts with enhancing profitability and productivity.

By improving operational efficiencies, industries can lower their energy consumption, reduce waste and unlock new competitive advantages. To do so, however, they must find ways to access accurate data to benchmark their current environmental impact, identify areas for improvement, and track progress over time. For many, this can be fraught with difficulties, with technical limitations creating hurdles. In 2023, IBM reported that 41% of executives cited inadequate data as a top barrier to ESG progress.

The challenge of accessing operational sustainability data

Industries will encounter many challenges around ESG reporting, including:

• Data collection methods: Enterprises attempting to monitor operations and collect data manually will find they are on to a losing battle. This takes time, meaning data is old before it can be used, and inevitably human error can skew results.

• Data diversity: Many enterprises operate in brownfield environments, reliant on legacy systems and technologies that present data in diverse formats, with processing often managed in silos. That requires manual aggregation which again is rife with errors and takes time. KPMG figures show that 47% of companies manage their ESG data manually via spreadsheets.

• Incorrect/incomplete data: Legacy systems, not designed with today’s wider analytics and monitoring in mind, often don’t provide the information required for ESG reporting and data-driven operational excellence.

Enterprises can’t visualize their progress or the sustainability improvements they could capitalize on. Transparent reporting with key stakeholders, regulatory compliance and public reputation can all be placed at risk. In a world where making sustainable progress is key – these practices are completely unsustainable. So, what is the answer?

Digitalization holds the key

To improve visibility and gain deeper insights into operational and sustainability performance many industries are turning to digitalization. Statista expects spending on digital transformation to reach $3.9 Trillion in 2027, up from $2.5 Trillion in 2024. Through digitalization of their operations, industrial enterprises can access:

• More comprehensive data, as additional operational data is collected, and data silos are broken down.

• More accurate and relevant data, through automated real-time data collection, reducing the time and errors associated with manual activities.

• Real-time operational insights through continuous monitoring, to implement data-based decision-making and achieve sustainability, efficiency and worker safety goals.

Digital tools enable data-driven sustainability

A recent study conducted by GlobalData and Nokia found that 79% of enterprises achieved more than 10% reduction in emissions after deploying digitalization solutions, with 78% seeing a positive ROI in the first six months of deploying private wireless and digitalization technologies.

To achieve operational data transparency through digitalization, industries must implement four key components:

• Industrial devices: Wireless routers, dongles, user devices, smart PPE, drones, IoT sensors and more, connecting people, machines, vehicles, and any other equipment and systems – even in brownfield environments – to gain broader, automated, real-time access to all operational sustainability data.

• Pervasive critical campus connectivity: High-performance private wireless networks, leveraging 4.9G/LTE or 5G to support critical applications and industrial Wi-Fi 6/6E for non-business-critical applications, ensure all data is captured and transmitted in real-time from thousands of devices and sensors, even across vast industrial sites.

• On-premises industrial edge: Our robust, reliable and secure on-premises MX Industrial Edge enables high-capacity data processing and breaks down data silos. With all operational sustainability data centralized in a single data hub, existing and new Industry 4.0 applications can access what they need, when they need it, in real time, while retaining data governance, sovereignty and security. The advantages of this centralized real-time data hub go beyond sustainability and benefits other use cases such as predictive maintenance, digital twins and AI applications.

• Applications: Enterprises must be able to implement applications that allow operational data to be harmonized and visualized from different systems leveraging different industrial protocols for real-time insights and transparency to achieve their goals faster. With a holistic, single-pane-of-glass view of all critical operational sustainability data, enterprises are empowered to simplify and streamline analysis and reporting and optimize operational processes. Applications like the Nokia Integrated Operations Center, Litmus Edge and Crosser Node provide enterprises with those powerful data capabilities, ranging from data collection and harmonization to data visualization into a single dashboard view – enabling real-time unified monitoring, advanced analytics, predictive insights and intelligent automation of processes and workflows. With Litmus Edge, we provide a dedicated and pre-designed dashboard specifically for advanced monitoring and control of energy systems.

Today a fifth component is emerging: Artificial Intelligence (AI) allows enterprises to leverage large language models (LLM) and machine learning (ML). For AI to live up to its promises of transforming industrial operations, it needs access to vast amounts of precise, real-time data. With an on-premises edge, enterprises have a unified real-time data hub that can be used to deploy and scale real-life AI use cases, enabling comprehensive data visibility, actionable insights, advanced automation and improved situational awareness.

The ways AI can advance data analysis and monitoring for sustainability reporting and progress include:

• Detecting patterns in energy consumption to predict trends in the behavior of assets such as vehicles and other machines and address issues before they impact efficiency and sustainability.

• Identifying wear and tear in machinery to inform predictive maintenance activities and extend asset longevity.

• Using AI-driven video analytics, for example, across a manufacturing line, to identify anomalies faster and reduce the waste and costs associated when quality control issues go undiscovered. 

When implementing AI-powered use cases, it is important to consider the high data processing demands of AI applications, which can significantly affect energy consumption and environmental footprint. Opting for an AI-capable on-premises compute infrastructure may reduce the sustainability impact compared to cloud-based processing. However, it is essential to balance the benefits of AI applications against their environmental impact to ensure a net positive outcome.

The journey to Net Zero is underpinned by data and digitalization

Enterprises that embrace their digitalization journey today will accelerate their sustainability goals and transform processes to enhance operational efficiency, safety, and profitability. The comprehensive, real-time data gained from digitalizing operations will be critical to inform your journey to net zero, providing deep visibility and transparency, supporting ESG reporting, and enabling informed decision-making to improve operations. Additionally, new use cases enabled by digitalization generate value extending beyond simply supporting reporting requirements, empowering you to reduce fuel, waste and energy consumption in your operations.

Download our free chemical manufacturing and port terminal sustainability guides for deeper insights.