How can organizations turn fragmented data into informed decision-making in complex systems?

Organizations today operate within increasingly complex value chains made up of interconnected and often interdependent suppliers, partners, technologies and processes. In fact, the term “chain” is somewhat misleading. It implies a linear and sequential process, whereas in reality, today’s values chains are complex, interconnected and interdependent networks or ecosystems, where decisions made in one area are felt across the whole.

Despite significant investment in data collection, organizations often lack a joined-up view of how their operations function in reality and how decisions in one part of the chain impact their business more broadly. Data is often incomplete, irrelevant, inaccurate or siloed across multiple systems built for different purposes. This creates bottlenecks that disrupt the flow of useful information and insight across the organization.

Without a connected view, it becomes difficult to answer fundamental questions: Where are the real constraints? What impact will this decision have elsewhere? And how do we balance cost, risk and performance? Instead, decisions are reactive and made to optimize parts of the system in isolation rather than improving the performance of the system as a whole.

David Lo Jacono works with organizations to move from fragmented data to system-level insights and decision-making. 

The work starts with mapping how data, models and decisions interact across the system and where information is lost, distorted or delayed. 

From there, solutions combine three elements: 

• Sensor data and measurement systems to show what’s happening
• Engineering and physical models to explain why it’s happening
• AI and optimization techniques to determine what to do next. 

This approach delivers a more accurate representation of complex systems, capturing both physical behavior and operational realities, accounting for uncertainty and identifying the true performance drivers. 

The final step is ensuring insights translate into action by embedding solutions into day-to-day operations. This involves connecting models to live data, enabling continuous feedback between what’s observed and predicted and what decisions are made. Over time, this supports a shift from static, one-off studies to dynamic, adaptive systems that support ongoing optimization across the value chain.