Trust in Data: Why AI-Ready Data Platforms Fail Without Trust by Design

Many organisations have no shortage of data yet still struggle to find clear answers. Fragmented legacy platforms, unclear data ownership, and business logic confined to siloed spreadsheets can hinder the ability to answer simple business questions, and limit AI-driven automation and decision support.

Data transformation initiatives often focus on improving access by consolidating and integrating platforms. However, technology-driven transformation can become disconnected from business objectives, often overlooking whether enhanced access inspires trust in enterprise data quality, and how data is interpreted and used. Doubt may not cause immediate resistance, but I’ve seen it emerge in the questions leaders ask when they’re trying to re-establish trust in every decision:

“Why does this tell a different story from what I’m hearing elsewhere?”

“Why does this number differ from the one we saw last quarter?”

“Where does this data come from?”

“Can we trust this enough to make a decision?”

“How long would it take to prove whether this is right?”

These questions are warning lights, signalling that the meaning, origin, and ownership of the data haven’t been clearly defined to support confident decision-making. With advancements in generative and agentic AI, the risk is not seeing the warning signals at all before action is taken that results in revenue leakage, reputational damage and regulatory exposure.

Why does trust erode in complex organisations?

Trust in enterprise data rarely collapses in a single event. It erodes gradually as organisations grow more complex and data estates outpace governance. Data flows across functions and platforms, often owned by different teams with different priorities. As a result, the same data can take on subtly different meanings depending on where it is produced, transformed, or consumed. Even straightforward measures can vary in their meaning and how they’re derived.

Consider how service performance might be measured during a customer service outage at a utilities company:

•      A field service engineering team may measure against the time an engineer takes to complete remedial work.

•      A customer-facing team may measure against when the customer experienced service restoration.

•      A network operating centre may measure against when an incident is updated in the system.

Each of these definitions is valid in isolation. When presented without clarity on which definition applies, or without acknowledging that multiple definitions exist, leaders encounter findings that seem inconsistent. This inconsistency arise snot from incorrect data, but from unclear underlying meaning.

Even basic metrics like ‘Average Revenue Per User’ or ‘Count of Active Customers’ can unravel if measured in the CRM versus the billing system. Without a shared source of truth, identical questions produce conflicting answers, chipping away at trust.

Modern, unified data platforms can accelerate this dynamic. While they make data easier to access and reuse, they also allow ambiguity to spread. Data becomes more available but less reliable without clear ownership, shared definitions, and visible lineage.

Trust isn’t just cultural, it’s designed

Trust in data is often seen as a cultural challenge involving adoption, training, or mindset. When confidence is low,organisations typically focus on change management or data literacy – valuable efforts, but they treat trust as a byproduct of familiarity.

In practice, trust is primarily structural. It is determined by how data, business logic, and metrics are defined, owned, governed, and surfaced across the organisation. Designing for trust in data-driven transformation means:

•      Making meaning explicit: shared definitions create a common reference point. When people understand exactly what a metric represents, they can interpret it consistently and accurately. In formal terms, this aligns with establishing a governed business glossary – a core tenet of data governance frameworks and a prerequisite for the metadata quality expected under principles such as FAIR (Findable, Accessible, Interoperable, Reusable).

•      Defining ownership: when accountability for a dataset, definition, or business rule is clear, there is line of sight for challenge and clarification, reducing parallel sources of truth. This is not just good practice; the UK Government’s guidelines on AI-ready data explicitly position named data ownership and stewardship as foundational to responsible AI deployment.

•      Being transparent: when lineage and assumptions are visible, data becomes easier to reason about. Leaders don’t need perfection; they need confidence that what they are seeing is explainable. The ability to trace how a measure was produced is a powerful trust signal – and a critical requirement for auditability in regulated sectors such as financial services, defence, and energy.

Surfacing assumptions, defining owners, and clearing up ambiguity early are essential for building shared understanding. This way, what gets built mirrors how the organisation truly operates, now and as it changes, and everyone using or producing data speaks the same language when making decisions.

As a business analyst at Aker Systems, one of the most valuable ways I can drive successful data transformation is to take ownership of this ambiguity, which often lives at the boundary of functions and roles. It means challenging assumptions, exposing disagreement early, and turning implicit understanding into explicit, shared definitions. By facilitating alignment on meaning before systems or models are built, we can help prevent trust gaps from forming downstream.

This approach avoids heavy-handed control and excessive governance. The goal is to ensure understanding keeps pace with increased access, not to slow progress. Trust is designed and established when meaning, ownership, and transparency are prioritised rather than treated as afterthoughts. When trust is built into the operating model, we enable speed and certainty – confidence does not need to be re-established in every meeting.

AI raises the stakes; it doesn’t fix the foundations

AI now sits at the heart of modern data transformation. Whether powering analytics, automation, or decision support, its promise is unmistakable: quicker insights, less manual work, and better results at scale.

However, AI does not rewrite the fundamentals of trust. When trust in data is weak, AI amplifies the issue rather than resolving it.

•      Ambiguous definitions, unclear lineage, and inconsistent assumptions become more difficult to identify and address when models lead or automate decision support. Decisions may be made faster, but with less shared understanding of their basis.

•      When data underpins agentic automation, ambiguity has real operational impact. Processes move faster, but with reduced visibility into why specific outcomes occur, making issues harder to diagnose and accountability harder to establish. Worse still, work can be done incorrectly without any visibility until long after undesirable outcomes manifest.

Conversely, when trust is designed into the data foundations, AI can serve as a force multiplier. Clear definitions, visible assumptions, and traceable lineage give decision-makers the context they need to interpret and challenge outputs appropriately. The same foundations allow automation and agents to act predictably and responsibly, with outcomes that can be explained, monitored, and corrected when needed.

AI readiness in this sense, is not primarily a technical milestone, but a trust milestone.

Trust before scale

Data transformation is usually framed around platforms and pipelines, but in complex, regulated organisations, success hinges on whether people and systems can trust the data they’re asked to use.

Designing for trust means making meaning, ownership, and transparency the foundation – not just afterthoughts or box-ticking exercises. With that foundation, trust endures as data is reused, automated, and scaled. Without it, organisations are forced to renegotiate trust with every decision, losing both speed and certainty.

At Aker Systems, we design, build and operate AI-ready data infrastructure for organisations in government, defence, energy, and financial services –environments where trust is non-negotiable. If the challenge of designing trust into your data transformation resonates, we’d welcome a conversation.

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Frequently Asked Questions

What does “trust in data” mean in the context of enterprise transformation?

Trust in data means that the people and systems consuming data have confidence in its meaning, origin, accuracy, and currency. It is not simply a feeling – it is a structural outcome of clear definitions, accountable ownership, and transparent lineage. When trust is present, decision-makers act with speed and certainty rather than reverting to manual checks or parallel analysis.

Why do data-driven transformation programmes fail?

Most data transformation programmes fail not because of technology shortcomings, but because they prioritise data access and platform consolidation over shared understanding. When business definitions are ambiguous, ownership is unclear, or lineage is invisible, the resulting platform may deliver more data without delivering more confidence. Trust gaps emerge at the point of decision-making and can undermine the entire investment.

How do you design trust into a data operating model?

Designing trust into an operating model involves three structural pillars: making meaning explicit through governed business glossaries and shared definitions; defining ownership so that every dataset, metric, and business rule has a named accountable steward; and being transparent about lineage and assumptions so that outputs are traceable and explainable. These elements should be embedded from the outset of transformation, not added retrospectively.

Is trust in data the same as data quality?

Data quality is a component of trust, but not the whole picture. Data can be technically accurate yet still untrusted if users don’t know what it represents, who owns it, or how it was derived. Trust encompasses quality, meaning, ownership, lineage, and timeliness. Two teams can look at the same high-quality dataset and draw conflicting conclusions if the underlying definitions differ – a trust problem, not a quality problem.

What are the risks of deploying AI on untrusted data?

When AI models are trained on or make decisions from data that lacks clear definitions,ownership, and lineage, ambiguity becomes automated. In analytics, this means insights may be misinterpreted. In agentic automation, processes may execute incorrectly without human visibility until after consequences emerge –including revenue leakage, regulatory non-compliance, and reputational damage. The UK Government’s AI-ready data guidelines explicitly position governance and trust as prerequisites for responsible AI deployment.

How does Aker Systems help organisations build trust into their data platforms?

Aker Systems designs, builds, and operates AI-ready data infrastructure for organisations in government, defence, energy, and financial services. Our approach embeds trust by design: we work with clients to surface ambiguity, establish shared definitions, and implement governed data ownership and lineage before, during,and after platform delivery. Operating in classified and highly regulated environments, we understand that trust is the foundation, not the afterthought,of successful data transformation.

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