Turning Industrial Data into Profit and Sustainability: From Spaghetti Systems to Intelligent Decisions

At Sustainability in Service 2025 – Power of 50, Mounir Boemond with AVEVA, presented a perspective showing that these two agendas are not separate. When industrial companies treat sustainability as a data and operations problem, not just a reporting requirement, it becomes a powerful lever for cost reduction, new revenue, and stronger customer relationships.

Where operational data, aftermarket services, and sustainability targets meet, the opportunity exists. The challenge is that most organisations are still trying to unlock this value from a spaghetti of unstructured data flows, manual workarounds, and siloed tools.

From Engineering Tools to Data-Centric Operations

The evolution of industrial software over the last two decades illustrates a broader shift from engineering design tools or point solutions for asset management to data.

In industrial environments, every design, asset, and process generates information. SCADA systems, DCS, sensors, weather feeds, condition monitoring, financial systems, and maintenance records all contribute to a vast data universe. Technology alone no longer presents a competitive advantage, but the ability to:

• Capture data reliably from multiple systems;  
• Structure and contextualise it;
• Turn it into decision-ready information that operators, service teams, and management can trust.

This transformation requires more than software deployment, demanding a shift in mindset from system-by-system optimisation to an integrated approach to industrial information.

Reframing Sustainability as a Business Opportunity

Sustainability, when addressed strategically, is fully compatible with profit and growth. Industrial companies are beginning to recognise that:

• Emissions data and ESG metrics can reveal inefficiencies in operations;  
• Regulatory compliance can be the trigger for broader digitalisation;  
• Reducing energy consumption and waste often improves margins;  
• Helping customers decarbonise deepens commercial relationships.

Some organisations are embedding sustainability so deeply that it becomes a structural driver for their product and service roadmap, commercial strategy, and customer engagement. New roles focused on sustainability-led growth are a direct reflection of this. If sustainability is central to customers’ strategies, it should be central to commercial conversations as well.

Notably, this is not about enforcing sustainability from the top down, but about enabling it through tools, data, and business models that demonstrate clear economic value.

The Real Problem: Spaghetti Data and Low Trust

Many industrial companies today are generating huge volumes of operational data but using very little of it effectively. Common symptoms include:

• Manual data consolidation in spreadsheets;  
• Conflicting numbers from different systems; 
• Time-consuming, error-prone reporting processes;  
• Stakeholders who do not trust the data they receive.

For sustainability reporting, this is a serious barrier. If the people responsible for external reporting do not trust the underlying data, they are forced into continual manual validation and updates. The same is true for service teams who need to make decisions about maintenance, spare parts, or performance contracts.

In many organisations, this spaghetti of data flows prevents them from using AI or advanced analytics in a profitable way. Without a coherent data model and governance layer, AI simply amplifies noise rather than insight.

Building a Digital Backbone: From Raw Data to Decision-Ready Information

The emerging best practice is to move from fragmented data flows to an integrated digital backbone or industrial information platform, based on four core principles:

1. Connect to all relevant data sources: SCADA, DCS, fleet management, historian data, ERP, maintenance systems, and external data (e.g., weather, market prices) need to be accessible in a unified environment.

2. Apply a framework and context: Raw data must be structured, tagged, and standardised. A framework is needed to define what the data means, how it relates to assets, and how it should be interpreted for different use cases (compliance, asset health, energy, services, etc.).

3. Serve defined consumers and use cases: Instead of asking what dashboards to build first, leading organisations start by determining who needs to consume this data, and for what decisions. This might be sustainability reporting teams, field service managers, central control rooms, or customers under service contracts.

4. Enable trust, transparency, and automation: Once data pipelines and models are stable, reporting and analysis can be largely automated. This builds trust, as people know they are working from a single version of the truth, derived directly from operational systems.

Timeframes vary by organisation, but with the right internal commitment and cross-functional alignment, the initial structuring and connection of data can be achieved in a matter of weeks, rather than years. The main constraint is often not technology, but the availability of the right stakeholders and clarity around objectives.

Compliance as a Catalyst for Operational Efficiency

Regulatory drivers are often the catalyst for data transformation. Organisations that start by digitalising their sustainability reporting quickly discover that the same data can unlock substantial operational benefits.

For example, projects initially driven by ESG and emissions reporting requirements can unlock far greater operational value when approached strategically.

What begins as a compliance exercise, by automating data collection and standardising reporting, often creates a reliable, structured data foundation that enables deeper visibility into asset performance, supports the identification of patterns, and lays the groundwork for more predictive and prescriptive maintenance. It not only improves reporting but also facilitates tangible gains in uptime, cost efficiency, and operational reliability.

Treating ESG and emissions reporting as a narrow regulatory obligation risks missing a significant opportunity. When integrated into a broader data and digital strategy, these requirements can help justify and accelerate investments that ultimately transform operations and service performance.

Smart Services: Turning Data into Customer Value

For OEMs and service organisations, the potential is even greater. Service models increasingly depend on real-time or near-real-time insight into installed equipment at customer sites. Leading approaches combine:

• Asset performance data from equipment;  
• Environmental data such as weather or load conditions;  
• Historical maintenance and failure information.

With this intelligence, service providers can shift from reactive maintenance to proactive, and eventually predictive, services. This can take the form of:

• Condition-based maintenance contracts;  
• Performance guarantees tied to energy consumption or uptime;  
• Remote monitoring and optimisation services;  
• Advisory services helping customers meet their own sustainability targets.

A significant hurdle is often data access. Customers can be reluctant to share their operational data. Modern architectures address this by allowing data to remain under the customer’s control while sharing only the necessary information and insights with the OEM or service provider. This builds trust and opens the door to collaborative models where both sides benefit from better performance and lower environmental impact.

Industrial Intelligence: Humans Still Make the Decisions

While AI and advanced analytics are increasingly embedded into industrial tools, human decision-makers are central.

Industrial intelligence is not about replacing human judgement. It is about surfacing relevant, contextualised information that allows experts to make better and faster decisions. In practice, this means:

• Combining multiple data streams to anticipate production shortfalls and adjust generation portfolios accordingly;  
• Enabling control rooms to orchestrate complex networks of assets based on real-time conditions, market signals, and risk tolerance;  
• Equipping service teams with forward-looking insights so they can schedule interventions before failures occur.

In energy generation, this can translate into more precisely planning when to start gas-fired plants or dispatch long-duration energy storage to compensate for low wind forecasts. In manufacturing, it can be applied to production planning, asset utilisation, and maintenance scheduling.

Multi-source data, structured and contextualised, enables operational efficiency. The same building blocks can be applied to both internal operations and external service offerings.

Remote Operations and Hybrid Service Models

The pandemic accelerated remote operations in ways that many industrial organisations had previously resisted. What began as a necessity has now become a strategic advantage.

Centralised control centres and hybrid service models enable companies to:

• Consolidate data from multiple sites (mines, plants, or assets) into a single monitoring and decision hub;  
• Coordinate local teams on-site with remote experts;  
• Deliver services to assets located in another country or continent.

Distance is no longer a structural barrier when the data infrastructure and processes are in place. For service-based businesses, this model directly supports emissions reduction as well. If better remote diagnostics and planning reduce the number of site visits or enable more efficient routing and scheduling, the outcome is both lower cost and lower environmental impact.

Measuring and Monetising Sustainability Outcomes

The most compelling examples of digital and sustainability integration are those where clear financial returns can be quantified. When companies:

• Digitise their emissions and ESG reporting;  
• Apply analytics to energy use and asset performance;  
• Enable predictive or prescriptive maintenance strategies.

The tangible benefits often include:

• Reduced energy consumption year-on-year;  
• Lower unplanned downtime and maintenance costs;  
• Optimised spare parts and resource usage;  
• Improved asset life and reliability.

These savings, when properly measured and attributed, justify the investment in data platforms and tools. In some industrial cases, recurring annual energy savings alone are material enough to redefine the business case for digital transformation.

How Long Will It Take?

The two most often asked questions are:

1. How long does it take to move from a spaghetti data landscape to a structured one?  
2. How long before the organisation starts seeing real benefits?

There is no universal answer, but some patterns are emerging.

If an organisation can secure internal commitment, identify all critical data sources, and agree on clear target outcomes, the technical work to design a framework and connect key systems can be relatively fast, in the order of several weeks for a first usable version. The main bottleneck is usually people and governance, not connectivity.

The timeline for tangible cost reductions or revenue enhancement depends on the scope, asset base, and existing maturity. However, once trustworthy data and automated reporting are in place, many organisations begin to see:

• Faster, more accurate regulatory and ESG reporting;  
• Early operational improvements in maintenance and energy use;  
• Better visibility across fleets or networks of assets.

From there, more advanced use cases such as predictive maintenance, performance-based contracts, and new service offerings become much more achievable.

The critical enabler is organisational alignment. Securing internal stakeholder support often determines the pace of change more than any specific technology choice.

Industrial Data as the Foundation for Sustainable Growth

For industrial manufacturers and service organisations, sustainability, digitalisation, and aftermarket transformation are reshaping competitive dynamics.

The companies that will lead in this new environment are those that:

• Treat sustainability as a driver of value creation, not just a reporting obligation;  
• Invest in an integrated information backbone that turns fragmented data into trusted insight;  
• Use compliance demands as a catalyst to digitalise and optimise operations;  
• Build service models that leverage operational data to improve both performance and emissions for their customers;  
• Recognise that AI and analytics are only as powerful as the data structures and human decisions behind them.

Most organisations already possess vast amounts of operational data. The next step is not necessarily more sensors, but more intelligence by structuring, contextualising, and using existing data to make better decisions.

Equipment uptime, aftermarket services, and sustainability create an opportunity together. Those who succeed in aligning these domains through robust data strategies will not only meet regulatory expectations but also reduce costs, unlock new revenues, and strengthen long-term customer relationships.