Data Trust Score: After data readiness, you need trust

Two numbers arrive in the same meeting. Finance reports churn at 8%. The customer team reports 11%. Both numbers came out of real systems. Both departments are competent. 

Nobody in the room can resolve the gap. The conversation becomes a conversation about which number to use.

At many companies, a decision gets made on the strength of who sounded more certain.

What failed was trust.

We have written before about data readiness, which is a question of capability. Data trust is a question of belief. When the number appears on the screen, does anyone act on it without checking first?

If they are not, you’re probably in a good place to begin your data trust revamp. We will cover why volume is the wrong ambition, the five things that destroy trust, what low-trust data actually costs, and a simple Data Trust Score you can run on a single decision this week.

Key Takeaways

• Data readiness is about capability. Data trust is about belief. 
• Trust is a property of a decision rather than a property of a company. The same field can be trustworthy for one decision and useless for another.
• Five things destroy trust: missing fields, inconsistent definitions, stale updates, absent ownership, and spreadsheet side systems.
• Low-trust data relocates the decision from evidence to hierarchy. This is how reporting (and decision-making) becomes political.
• The Data Trust Score measures five dimensions: completeness, consistency, timeliness, ownership, and usage. Read the lowest number rather than the total.
• Usage is the only dimension that cannot be faked. It is the organisation’s own verdict on the other four.

What is data trust?

Data trust is the confidence that a specific number is good enough to act on without verifying it somewhere else. It means that multiple teams trust a unified source of truth.

That definition is deliberately behavioural, because trust is visible in what people do with a number rather than in what they say about the data.

Why more data does not create more trust

The instinct when data feels unreliable is to gather more of it. 

Volume works against you here. Every additional source is another place where a contradiction can start. Ten systems holding customer records without an agreed definition of a customer do not produce one strong answer. They produce ten defensible ones.

The pressure to add is also rising quickly. Eurostat reports that 20% of EU enterprises used AI technologies in 2025, up from 13.5% a year earlier, one of the sharpest single-year jumps in the series. More systems are consuming company data faster than most organisations have agreed what that data even means.

There is a second reason volume feels like progress. Senior confidence in data tends to be high, but it is not really earned. In the 2026 State of Data Integrity and AI Readiness study, 87% of data and analytics leaders said their data was ready for AI. Meanwhile, 43% of them named data readiness as their single biggest barrier to aligning AI with business goals. 

The same leaders, in the same survey, held both positions.

The researchers noted something even more useful. When the survey covered practitioners as well as leaders, roughly a third reported high trust in their organisation’s data. When the sample narrowed to senior leaders only, two-thirds trusted the data. 

Confidence in data rises with distance from the people who create it.

This is also why we pointed out that most digital transformations fail within middle management.

Executive confidence is not evidence of trustworthy data. It may be evidence of distance.

What actually destroys data trust

Trust erodes through five specific failures. None of them look dramatic on the day they happen.

1. Missing fields

A field that is optional in the form is optional in reality. The classic HBR study by Nagle, Redman, and Sammon, Only 3% of companies’ data meets basic quality standards, measured newly created records and found that 47% carried at least one critical error. 

A gap at the point of entry becomes a caveat in the report, and a caveat is where trust leaks.

2. Inconsistent definitions

This is the quiet one. Gartner identifies inconsistency across sources as the most challenging data quality problem. It comes directly from data being maintained in silos with overlaps and gaps. 

When sales counts a customer at signature and finance counts one at first payment, both reports are correct. Still, the company can’t give a straight answer to a basic question like how many customers it has. 

3. Stale updates

Freshness is judged against the decision, not against the calendar. A monthly refresh is excellent for a board pack and useless for a service team deciding what to do this afternoon. 

Data becomes untrusted the moment people cannot tell how old it is, because unknown age is treated as old.

4. Absent ownership

When nobody is accountable for a field, errors become weather. Everyone complains, and nobody fixes anything. 

Organisations with neither a data strategy nor a governance programme reported no high trust at all. Ownership is what we mean when we say a digital system needs an operating model rather than a governance binder.

5. Spreadsheet side systems

Somebody exports the report every Monday and maintains their own version. This is usually treated as bad behaviour. It is better read as a measurement. 

That spreadsheet is a written record of exactly which question the official system failed to answer. It tells you more about your data than most audits.

The cost of low-trust data: Parallel reporting and politics

When two numbers conflict and neither can be settled, the argument gets resolved by seniority, by confidence, or by whoever has the most patience in the meeting. 

Evidence stops arbitrating, and hierarchy takes over. 

Every organisation with parallel reporting eventually develops parallel politics. A number that cannot settle a question becomes a tool for winning one.

Three costs follow, and none of them appear on a budget.

• The first is reconciliation work. Capable people spend their weeks explaining why two reports differ instead of acting on either.
• The second is decision latency. Leaders ask for verification, verification takes a week, and the decision arrives after the moment that needed it.
• The third is the most expensive. Organisations stop asking questions they cannot answer defensibly. The ambition of the analysis shrinks to fit the trust available. Nobody notices this happening, because unasked questions leave no trace.

The Data Trust Score: Five dimensions, ten points

The Drexel LeBow research identified the obstacle. The biggest barrier to achieving high-quality data is the inability to measure quality effectively, cited by 29% of leaders. 

Companies know their data is weak and lack any way to say how weak. This makes the problem pretty hard to prioritise.

So here is a deliberately small instrument. Score five dimensions from 0 to 2, for a total out of 10.

Three rules of thumb make the datrust score useful.

Read the lowest number, not the total. A dataset scoring 2, 2, 2, 2, 0 is not an eight out of ten. It is untrusted, because a single broken dimension is enough to justify a shadow spreadsheet. Trust behaves like a chain and fails at the weakest link.

Score a decision, not your company. Enterprise-wide data quality is unmeasurable in any way that changes behaviour. Score the five fields behind one recurring decision. This is the same logic as minimum viable data, applied to belief rather than structure.

Treat usage as the audit. The first four dimensions can be asserted in a meeting. Usage is revealed by what people actually do. If your teams keep private versions, they have already scored your data, and their score is lower than yours. 

That makes usage the most honest number on the page and, not coincidentally, the one most tied to people readiness.

A lowest score of 0 means the data is not ready to automate anything. A score of 1 means it is usable with human verification. A score of 2 across all five means you can act on it, and you can let a system act on it too.

Data trust is the precondition for AI

A person who receives a suspicious number pauses and checks. That instinct is an unpaid quality control layer for companies. 

Automated systems don’t have that instinct. 

They act on the number they are given, at speed, repeatedly. This is why we keep arguing that AI is a cost-saver right until it is not. 

The same applies to any change made to a live system. A new field, a new integration, or a redefined metric can reset a trust score that took years to earn. It is one more reason small changes carry hidden costs.

The cheapest moment to run these five questions is before anything gets built. That is a large part of why serious analysis pays for itself. It surfaces the definitional disagreements while they are still cheap to settle.

Trust is the return on data readiness

Data readiness gets an organisation to the point where good decisions become possible. Data trust is what determines if they ever happen.

At Net Group, we have watched enough reporting meetings to know the tell. The moment someone says “let me check that number and come back to you,” the system in front of them has stopped functioning as a decision tool.

Score five dimensions on one decision that matters. Look at the lowest number. Then fix that one thing, and watch what happens to the next meeting where you need that thing.