There is an uncomfortable truth hanging over boardrooms, research institutes and public bodies today. We are surrounded by more data than at any point in human history, yet we understand far less about its reliability. Real world data, the vast stream of information drawn from hospitals, GP surgeries, pharmacies, insurers, mobile apps, supply chains and everyday transactions, now sits at the heart of major decisions across business and government. The trouble is that much of this data is taken at face value. It should not be.
Leaders often speak with confidence about the insights generated from huge datasets. The size creates a false sense of certainty. The volume suggests rigour. But anyone who has worked at the point where data is actually captured knows the truth. Quality varies wildly. Definitions change between departments. Key fields are left blank. Devices malfunction. Human exhaustion leads to errors that quietly embed themselves deep in the system. And organisations often pretend these imperfections do not exist.
Poor quality data does far more than distort a dashboard. It leads to misguided strategies. It shapes flawed investments. In health, it warps our understanding of outcomes. If adverse events are under recorded, a medicine may appear safer than it truly is. If demographic information is inconsistent or misclassified, insurers and policymakers may make decisions that disadvantage entire communities. The familiar phrase “garbage in, garbage out” remains accurate not because it is old fashioned but because it is persistently ignored.
Then there is the issue of provenance. It is a word that sounds academic, yet its implications are very real. Provenance asks a simple question. Where did this data originate, and how exactly did it reach the point where someone is now drawing conclusions from it? In the world of art, provenance determines whether a painting is a masterpiece or a forgery. In data, it determines whether an insight is meaningful or misleading.
Too often, provenance is treated as an afterthought. Teams download files, reshape formats, merge sources and pass spreadsheets around without documenting the decisions and assumptions that shape them. By the time a chart appears in a presentation, the lineage is so murky that no one can fully explain the steps that produced it. In this fog, bias flourishes. Data that was collected for one purpose quietly becomes the basis for a decision in a completely different context. Without provenance, accountability erodes. Without accountability, trust collapses.
This brings us to transparency, a word that is used frequently yet practiced rarely. True transparency is not a glossy statement in an annual report. It is the willingness to acknowledge uncertainty. It is the discipline of stating what the data shows and what it does not. It is the honesty of revealing the limitations that might weaken a conclusion.
Many organisations fear that transparency will make them appear less competent. In reality, it does the opposite. Transparency builds credibility. It reassures regulators. It strengthens relationships with customers and patients. It enables teams to work from a shared understanding rather than from assumptions or wishful thinking.
Transparency also acts as a corrective force. When teams know they must explain their sources, they handle provenance with greater care. When they know their reasoning will be scrutinised, they invest more in quality. When they know their models must be interpretable, they stop treating data science as a black box that only specialists are allowed to question.
The potential of real world data is immense. It can uncover health inequalities. It can identify pressure points in supply chains before they become crises. It can help companies understand changing behaviour with unprecedented clarity. It can genuinely improve lives. But potential is not enough. Without quality, provenance and transparency, real world data risks becoming a source of confusion rather than insight.
The organisations that recognise this will lead the next chapter of innovation. Those that continue to treat data as abundant but unquestionable will find that more information does not always lead to better judgement.
