Can You Trust Your Data Enough to Make Critical Decisions?
Every executive wants their organisation to be data-driven. Boards expect decisions to be backed by evidence. Customers expect businesses to understand their needs. Regulators expect accurate reporting. And now, AI tools rely on organisational data to produce recommendations, predictions, and automated actions.
But before using data to shape your next major investment, transformation project or customer strategy, there is a fundamental question to ask:
Can you trust your data enough to make a critical decision?
For many organisations, the honest answer is not entirely.
The data may exist, but it is often spread across different systems, duplicated, incomplete, inconsistent or out of date. One department may be working from a CRM report, another from the finance system, and another from a spreadsheet saved months ago.
Everyone has data, but not everyone has the same version of the truth.
The value of data is not measured by how much you collect. It is measured by how confidently you can act on it.
Why Trusted Data Matters More Than Ever
Having more data does not automatically mean making better decisions.
Trusted data is accurate, complete, consistent, current, secure and suitable for the decisions being made. It is information that executives, employees and technology systems can use with confidence.
The business consequences of getting this wrong can be substantial. IBM's 2026 analysis of the cost of poor data quality reports that more than a quarter of organisations estimate they lose over US$5 million annually because of poor-quality data. It also found that 45% of business leaders see data accuracy or bias as a leading barrier to scaling AI initiatives.
Poor data quality is not simply an issue for the IT team to resolve. It is a business risk that can affect financial planning, operational performance, customer relationships, regulatory compliance and confidence at the executive table.
If leaders spend the first half of a meeting debating whether a report is correct, the organisation does not have a reporting problem. It has a trust problem.
The Business Cost of Poor Data Quality
Poor-quality data rarely fails in one dramatic moment. More often, its effects appear gradually across the organisation.
Financial Losses & Missed Opportunities
Incorrect or incomplete data can distort revenue forecasts, investment decisions, customer targeting, pricing and resource planning.
A campaign may target duplicate or outdated contacts. A forecast may rely on incomplete sales information. A customer opportunity may be missed because relevant information sits in another system.
The individual errors may appear small, but their cumulative impact can be significant.
Operational Inefficiency
When employees cannot trust the data available to them, they create workarounds.
They reconcile spreadsheets, manually check records, request confirmation from other teams and rebuild reports. Time that could be spent analysing information and acting on insights is instead spent determining whether the information is reliable.
IBM's overview of data quality challenges identifies duplicate data, incomplete information, inconsistent records and data silos as common problems that can compromise decision-making and business workflows.
Compliance & Risk Exposure
Privacy obligations, audits and regulatory reporting all depend on organisations knowing what data they hold, where it came from, who can access it and whether it is accurate.
Without clear ownership, governance and data lineage, responding to an audit or information requests becomes harder. It also becomes more difficult to demonstrate that sensitive information is being handled appropriately.
Poor Customer Experiences
Disconnected customer data creates disconnected customer experiences.
A customer may receive the same communication twice, be asked to repeat information or receive an offer that does not reflect their circumstances. Over time, these moments reduce trust and make the organisation feel harder to deal with.
Trusted customer data allows teams to deliver more relevant, consistent and connected experiences across marketing, sales, service and operations.
Trusted Data is the Foundation of AI Readiness
The rapid adoption of AI has made data quality even more important.
In McKinsey's 2024 State of AI survey, 65% of respondents said their organisations were regularly using generative AI in at least one business function. The research also identified inaccuracy as one of the most commonly reported risks associated with generative AI.
AI does not automatically fix fragmented, outdated or poorly governed information. It can expose those weaknesses much faster and at a much larger scale.
If customer records are incomplete, AI-generated recommendations may be less relevant. If business definitions differ between departments, AI tools may interpret performance inconsistently. If sensitive data is not governed properly, new AI use cases may introduce additional compliance and security concerns.
This is why AI readiness starts with data readiness.
Microsoft's guidance on technology and data strategy for AI identifies preparing the data estate as one of the fundamentals required to move AI initiatives from proof of concept into production. Microsoft's AI Readiness Assessment similarly includes data foundations, AI governance and security among its 7 pillars of organisational preparedness.
AI can only be as dependable as the data, controls and business context behind it.
How Can Organisations Build Trusted Data?
Improving trust does not require an organisation to fix everything at once. It does require a clear enterprise data strategy and agreement on where to begin.
Start with the decisions that matter most
Identify the reports, processes and data sets supporting your most important business decisions.
Which information influences financial forecasting, regulatory reporting, customer retention, investment planning or operational performance? Those areas should be prioritised first.
Establish clear ownership
Critical data should have a named business owner, not just a system administrator.
Ownership means being accountable for how the data is defined, maintained, protected and used. Without clear accountability, quality issues tend to remain unresolved or move between departments.
Define practical governance standards
Data governance should help people use information responsibly and confidently. It should not become a layer of paperwork that slows the business down.
Useful governance establishes common definitions, ownership, access controls, quality expectations and escalation processes.
Microsoft’s guidance on AI governance describes processes, controls and accountability structures as essential to governing data privacy, security and the responsible use of AI. Read Microsoft’s AI governance guidance. [microsoft.com]
Connect disconnected systems
CRM, finance, customer service and operational platforms often hold overlapping information.
A considered data integration strategy can reduce duplication, improve visibility and give teams a more consistent view of customers, operations and performance.
Monitor data quality continuously
Data quality is not a one-off clean-up exercise.
Organisations should monitor important data for missing fields, duplicate records, unusual values and inconsistencies. Wherever practical, automated validation and monitoring can identify problems before they affect reporting, customer interactions or AI outputs.
Modernise the underlying technology
Legacy applications and manual processes can make good data governance difficult to sustain.
Application modernisation, integrated enterprise platforms and improved business processes can create a more reliable foundation for business intelligence, automation, customer data platforms and AI-enabled services.
Turning Trusted Data into a Business Advantage
The organisations best placed to succeed in the AI era will not necessarily be those with the most data.
They will be the ones that know which data matters, understand where it came from and can use it with confidence.
Trusted data helps leaders make decisions faster. It allows teams to spend less time reconciling reports and more time improving outcomes. It supports stronger customer experiences, more reliable business intelligence and safer adoption of AI.
For organisations working through digital transformation, the solution is rarely another disconnected tool. It is usually a combination of clearer governance, better integration, modernised applications, improved processes and practical data quality management.
This is where an experienced technology partner can help. Solentive works with organisations to connect systems, modernise applications and build reliable data foundations that support better business decisions and sustainable transformation.
Because before your organisation asks what AI can do with its data, it should first ask:
Do we trust that data enough to act on the answer?
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