The organisation begins making decisions outside its governed information environment. When people stop trusting formal asset information, they replace it with local spreadsheets, personal experience and informal sources, creating multiple versions of the truth and making it increasingly difficult for leadership to know what information decisions are actually based upon.
- Technically accurate data can still be ignored when people lack confidence in how it was created, maintained or interpreted.
- Historical data problems create organisational distrust that can persist long after the underlying technical issues have been corrected.
- Spreadsheets and shadow systems emerge when people feel they cannot rely on formal systems to provide the information they need.
- People revert to experience and intuition when they don’t trust the information available to support their decisions.
- Governance, ownership and assurance rebuild confidence by making responsibility for information quality visible and understood.
- Trusted information enables informed decision-making because people are more willing to use information when they understand and have confidence in it.
Real World
I saw this very clearly during an ERP/EAM transformation involving Product Lifecycle Management across five regions, 55 countries and 18 languages. On paper, standardising the information made perfect sense. In practice, even something as apparently straightforward as naming conventions became complicated because different regions had developed their own language, terminology and ways of interpreting the same information. We were also migrating legacy asset and PLM data into a common system, so we could technically clean, map, and validate the data. The harder question was whether the people receiving it would actually trust it enough to use it.
That experience reinforced something I have seen repeatedly since: data quality and data trust are not the same thing. You can improve accuracy, completeness and consistency, but trust is earned through governance, transparency and involvement. One of the most useful things I introduced was a standard Site Impact Analysis that let local stakeholders assess impacts, risks, and opportunities in their own context while still feeding into a common governance structure. Once people could see where information came from, how decisions were made, and how their local knowledge fit into the bigger picture, buy-in became considerably easier. The data hadn’t suddenly become magical; people had developed confidence in the system surrounding it.
Change Management Perspective
From an organisational change perspective, I would resist the temptation to start by fixing the data. I would first seek to understand why people don’t trust it. That means getting close to the people who create, maintain and use the information and understanding where confidence has been lost. Is the problem accuracy, timeliness, ownership, previous system failures, or simply that people cannot see how the information relates to the decisions they make? Rebuilding trust requires more than data cleansing.
It requires clear accountability, visible assurance, feedback loops and evidence that when people raise information-quality issues, something actually happens. My objective is not to convince people to trust the data; it is to create the organisational conditions in which the data becomes worthy of their trust.
Key Takeaway
Data becomes valuable when people trust it enough to change a decision.
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