Refining India December 2025 Issue

ADVERTORIAL

Unlocking AI potential: role of a modern data ecosystem

Scott Kahre dataPARC

A s industrial AI vendors continue to promote their products as the key to unlocking better performance, efficiency, and reliability, smart manufacturers are learning that these tools are only as powerful as the data behind them. For a variety of industries, including oil refining and petrochemical manufacturing, leaders are realising that industrial AI tools need access to accurate, real-time process data to deliver meaningful results. A well-planned, AI-ready data architecture allows intelligent systems to anticipate production issues before they occur, optimise plant performance, and provide actionable recommendations that drive efficiency. In this article, we explore what it means to build a modern data ecosystem that can support AI at scale, and how you can accelerate your factory’s digital maturity by avoiding common pitfalls and embracing proven data practices. Where most manufacturers find themselves Many manufacturers attempt to leap from raw, unorganised information to advanced AI models overnight, only to find themselves frustrated when those projects fail to deliver. The truth is that AI does not start with algorithms; it starts with data. Disorganised data leads to inconsistent results and a lack of trust among users. Engineers, therefore, may hesitate to act on AI-driven recommendations if they suspect the underlying data is incomplete or outdated. Alternatively, when data is clean, contextualised, and centralised, the impact is transformative. This strong data foundation gives operators and engineers the tools they need to accurately diagnose process failures, improve decision-making, and run the operation more efficiently. Simply put, data is the foundation of AI. Without reliable, structured, and accessible data, even the most advanced models will underperform. Evaluating your digital maturity stage Every organisation sits somewhere within the

five stages of digital maturity (see Figure 1 ). Some companies are still early in their transformation, with very little data and/or very little time to analyse it. The data they have may come from manually extracting reports from SCADA systems or from a variety of spreadsheets. Others have already begun integrating their data into a single interface, but they find it difficult to use that data in day-to-day decision-making. Understanding where you are in your digital journey is critical. The early stages should focus on organising and contextualising your existing data as your operators learn to trust the results. Over time, with the right tools, you eventually develop a data- driven culture that spreads to every facet of the plant. Why AI efforts often fail The reality is that many AI initiatives fall short of expectations. The reasons are rarely about the technology itself, but are instead about the ecosystem surrounding it. Common pitfalls include: • Lack of data readiness : Poor quality, missing, or inconsistent data. • Minimal subject matter expert (SME) involvement : Without operational context, models miss the mark. • Inflexible or siloed systems : Preventing data sharing across departments. • No clear strategy for applying results : Insights stay theoretical instead of operational. • Lack of cross-functional collaboration : IT, engineering, and operations do not align on goals.

The ve stages of digital maturity

STAGE

No time, no data

Some data, still little time

Reective decision making

Data-driven culture

Data-driven is in the DNA

Figure 1 Five stages of digital maturity