Why operational intelligence has become a board-level issue
Businesses today collect more operational data than ever before, yet the executives who make decisions about cost, performance and risk rarely have access to it in a form that helps them act. Juanjo Mestre, Jacobo Umbert and Luis Escámez – co-founders of Dcycle – explore why having this operational intelligence has now become a board-level issue.
We’ve faced this issue before; 20 years ago when financial data was left in the same disorganised and siloed state. But solutions were developed to make reporting easier, but now we’ve come full circle again. Different functions across the business still have limited visibility over each other’s systems.
For example, finance knows what things cost, operations knows how they work and sustainability teams understand environmental impact. These three perspectives describe the same business from different angles, but they are stored in different systems, analysed on different timelines and rarely connected to each other.
Businesses usually only discover operational problems only after they appear in financial performance. But by then, the opportunity to act has passed.
The convergence of the CFO and COO
Every business generates two different types of data, and most keep them permanently separated: financial and operational. Financial refers to invoices, purchase orders, supplier spend, payroll, operating costs and capital expenditure. This data lives in ERP systems and accounting software, owned by finance teams and structured around cost.
Operational covers energy consumption, carbon emissions across Scopes 1, 2, and 3, water use, waste volumes, employee travel, fleet activity and facility performance. This data sits across operations, procurement and sustainability teams, collected in separate tools and structured primarily for compliance rather than business decisions.
Leaving these datasets in siloes means leadership teams rely on delayed indicators. The operational data may provide a warning to an error taking place, yet the effects aren’t felt until they show up in financial reporting, which usually only happens on a quarterly basis. In which case, the operational conditions that caused it may have persisted for months.
This lack of data visibility and control comes at a time of some of the most unsettled market conditions, as margins get squeezed, there’s havoc across customer demand and supply chain stability, costs become harder to control, and forecasts don’t land like they used to.
The crux of the issue is that the intelligence needed to make critical decisions during this level of volatility is buried deep in siloed systems, across procurement records, supplier performance data, logistics workflows and sustainability metrics.
There’s a clear shift beyond the traditional retrospective reporting as CFOs are now expected to interrogate and influence the day-to-day decisions that drive operational, business outcomes. This includes identifying why supplier costs are rising, locating site-level energy consumption patterns and exposing operational bottlenecks that are eating into margin.
So the lines between finance and operations are now less defined. And there’s a joint barrier to overcome. Without clear operational insight, teams are forced to make decisions on only half of the picture.
A problem shared is not a problem halved
Core financial information is typically stored within centralised systems thanks to decades of process development. Operational data, on the other hand, is a few years behind, and still sits across various departments like procurement, sustainability teams and external supply chains.
This is a deep-rooted issue, and over time, different processes and reporting requirements create different definitions for the same business concepts. Lead-time, for example, may be measured from order confirmation by one team and from goods receipt by another, while sustainability data may be categorised by supplier group in one report and by transport route in another.
Within individual teams, there isn’t a problem. But when the same processes cross over into different functions, the discrepancies can trigger bigger challenges.
Take scaling AI initiatives, for example. AI can analyse data, identify patterns and generate insights, but what it cannot necessarily do is determine which of several conflicting business definitions is the correct one. If two datasets use different inputs, the AI model may generate two answers that are both technically accurate and completely inconsistent with one another.
Unlocking greater operational intelligence with AI
The more forward-thinking CFOs and COOs are taking the next step, and using AI to populate and manage centralised systems of record that house all operational data from across the entire organisation. AI tools can build on data that operational and sustainability teams already have access to, turning this vault of critical intelligence into a source of intelligence for executive decision-making rather than only for compliance reporting.
For one business, simply connecting procurement, energy and logistics data, information that had been sitting in separate systems, surfaced over €2 million in cost-saving opportunities that finance had no way of seeing.
Conclusion
By arming themselves with robust data infrastructure today, leaders are building on their long-term resilience with genuine transparency, and the means to support all decision-making with credible, accurate operational intelligence. Sustainable returns, whether financial or otherwise, depend heavily on the reliability of the data behind all critical decisions.
