Organizations deal with vast amounts of data across projects, operations and the wider supply chain, yet much of it remains difficult to access, interpret or reuse.  

Information is often stored in inconsistent formats, poorly documented or locked into individual workflows. As skills gaps widen and experienced staff retire, capturing and structuring that corporate memory becomes increasingly important. If not actioned upon, the result is not just inefficiency, but lost insight, repeated work and decisions made without full context.

Based on what we see working in the field, there are four connected steps that will improve organizations’ data: standardize, rationalize, integrate and recycle. 

Standardize: overcoming silos beyond the organization

Data silos do not exist only because people use different tools. They exist because data is created, structured and delivered in ways that make it difficult for other systems, and other teams, to use. This is not a new problem, but it has become far harder to ignore as organizations rely on more digital workflows and external data sources.

Internally, individuals understandably rely on familiar tools such as spreadsheets, PDFs, drawings and notes. Externally, service providers often deliver reports in their own preferred formats, structured differently from one another and labelled inconsistently. Even when the same tools are used, the underlying data may still be incompatible.

This lack of standardization creates friction wherever data needs to move. When information cannot be reliably ingested by other applications, including engineering tools, integrity systems or analytics platforms, it becomes siloed by default. The issue compounds in organizations that operate multiple systems across projects, operations and regions, or that rely heavily on third-party data.

But standardization is not about forcing everyone into a single platform. It is about agreeing on formats, structures and conventions that allow data to move between tools and teams without constant manual rework. When data can be imported, exported and reused consistently, whether it originates in Excel, CAD, GIS or inspection reports, it becomes significantly more valuable.

Digital platforms play an important role here, not by replacing existing tools, but by connecting them. When data is structured in a way that other applications can consume, it stops being trapped in individual files and starts supporting wider workflows across the organization and its partners.

Rationalize: speaking the same language, methodically

Even when data is accessible, it is not automatically useful. One of the biggest challenges with siloed information is inconsistency, including duplication, conflicting values, unclear labels and differing interpretations of what the data actually represents. Left unresolved, these issues undermine trust and slow decision-making. Rationalizing data means addressing these issues in a deliberate, methodical way. Cleansing information, aligning definitions and agreeing how data is labelled and applied is essential for building shared understanding.

This process also needs to be paced carefully. Large, sudden changes rarely succeed. Adoption improves most reliably when organizations focus on a small number of high-value use cases first, prove their benefit, and then expand. That incremental approach helps users build trust in new ways of working, rather than reverting to familiar tools simply because they feel safer.

Visualization can support this process by helping people interpret complex information in context. For example, defect data that lives in spreadsheets may be difficult to analyze, but when viewed spatially, patterns and clusters can emerge more clearly. Those insights prompt better questions and more informed discussions.

However, visualization alone is not enough. Its value depends on how well it fits into existing workflows and how clearly its benefits are communicated. Without that connection, even powerful tools struggle to gain lasting adoption. 

Integrate: bringing tools together without replacing them

Most organizations already rely on a wide range of specialized tools, including engineering software, design platforms and analysis packages, each built for a specific purpose. These tools are not the problem, and attempting to replace them outright is rarely realistic or desirable.

The real challenge is that information often has to pass manually from one tool, one team and one format to another. That handover process is slow, error-prone and heavily dependent on individual knowledge.

Integration addresses this by providing a central context where data from multiple systems can be brought together, viewed consistently and passed on in a structured way. Rather than forcing teams to abandon trusted tools, an integrated platform allows them to continue working as they always have, while making their outputs visible, accessible and reusable by others.

When data is connected through a central hub, teams spend less time searching, reformatting or duplicating work. More importantly, they can collaborate around the same information, with a clearer understanding of how it was generated and how it should be used.

At a broader level, this kind of integration supports a wider industry shift. Many operators are delivering the same or greater output with fewer people, enabled in part by better digital workflows. Integration does not just improve efficiency, it helps organizations adapt to changing workforce structures without losing critical knowledge. 

Recycle: treating data as a lifecycle asset

The final step is to stop thinking about data as something created once and discarded at the end of a phase. Instead, it should be treated as a reusable asset that continues to add value throughout the lifecycle.

Data generated during projects should inform operations. Operational data should support future modifications, decommissioning decisions or repurposing strategies. Lessons learned should feed back into new projects, rather than being locked away in archives that few people revisit.

How organizations structure their teams around projects, operations and decommissioning varies widely by company and region. What matters is not the organizational model, but whether data can be agile and adapt with the work as responsibilities change. When information is recyclable, it remains accessible and meaningful regardless of who owns the next phase.

This lifecycle view supports better front-end decision-making. With richer historical context and more reliable data, teams can identify risks earlier, assess options more effectively and act proactively rather than reactively. Without that continuity, organizations remain trapped in reactive cycles, responding to issues that better data could have helped prevent.