Integration is often seen as the solution to data inconsistency. Connect the systems, align the platforms, and the data should become consistent. In reality, it rarely works that way.
Many organisations invest heavily in integration, only to find that their master data – the customer records, product catalogues, supplier information, and reference data that underpin core business processes – remains inconsistent, duplicated, or unreliable. The issue is not the lack of connectivity. It is how data is created, managed, and maintained across the organisation.
The scale of the problem is striking. A 2024 McKinsey survey on Master Data Management found that 82% of businesses spend one or more days per week resolving master data issues[1], and research has shown that 78% of enterprises struggle to maintain data consistency across disparate systems – with poor data quality and redundant efforts costing an estimated $12.5 million per organisation annually[2]. For most large organisations, that figure represents a substantial drag on performance – and the integration projects intended to solve it often make the problem worse.
Why integration alone does not solve inconsistency
Integration ensures that systems can exchange data. It does not ensure that the data being exchanged is correct.
When data originates from multiple sources, each system may have its own structure, validation rules, and standards. Customer records, product data, and supplier information may be created and updated in different ways across different platforms. One system may treat addresses as a single field; another may break them into components. One may enforce a unique customer identifier; another may not. One may validate postcodes; another may accept any string of characters.
When this data is integrated, the inconsistencies are simply shared more widely – and at greater speed. Rather than solving the problem, integration can amplify it. Errors that were previously isolated to one system propagate across the enterprise. Conflicting versions of the same record begin to appear in multiple places. And the integration platform itself becomes a vehicle for spreading inconsistency rather than resolving it.
How inconsistency develops over time
Master data inconsistency is rarely the result of a single issue. It develops gradually – the cumulative effect of many small decisions, exceptions, and workarounds across multiple teams and systems.
Duplicate records are created when systems do not enforce strict validation rules at the point of entry. Data is captured differently by different teams – with varying conventions for capitalisation, abbreviation, or field structure. Updates occur in one system but are not reflected accurately in another. Manual corrections introduce new variations. Acquisitions and system migrations bring in records from environments with entirely different standards.
Research has found that large organisations now manage an average of 18–25 core business applications, with each system maintaining its own version of critical master data[2]. Over time, these small inconsistencies accumulate. Even in integrated environments, organisations can end up with multiple versions of the same data, each slightly different and each used in different contexts – and no clear answer to the question of which version is correct.
Why governance is often overlooked
One of the key reasons inconsistency persists is the lack of clear data governance. Integration projects tend to focus on technical connectivity – getting the systems to talk to each other – rather than on the ownership, control, and lifecycle of the data being exchanged.
Without defined rules for how data should be created, updated, and maintained, inconsistencies are inevitable. Without clear ownership of master data domains, no one is accountable for resolving conflicts when they arise. Without standardised validation and reference data, every system continues to do things its own way – integrated or not.
The risks are growing. Gartner has predicted that 80% of data governance initiatives will fail by 2027 without a crisis catalyst[3] – a stark warning about the gap between the importance of governance and the difficulty of implementing it. Effective governance establishes a single source of truth, clear ownership of data, and consistent standards that apply across systems regardless of how the integration layer is built. Without it, integration alone cannot deliver consistency.
Dajon Data Management works with organisations to address exactly this gap – combining technical data preparation with the governance frameworks needed to maintain quality over time. By focusing on how data is created, validated, and maintained across its lifecycle, Dajon helps ensure that integration delivers consistent, reliable information rather than spreading inconsistency at scale.
The impact on business performance
Inconsistent master data has direct commercial consequences that ripple through the organisation. Reporting becomes unreliable, making it difficult to make informed decisions. Operational processes break down when systems rely on conflicting information about the same customer, product, or supplier. Customer experience suffers when data is inaccurate or incomplete – the wrong address, the wrong contact details, the wrong product specification.
The hidden cost is even higher. Research has found that 85% of business analysts spend an average of 12.5 hours per week reconciling data inconsistencies across systems[2] – effectively losing a day and a half of productive analytical time each week to resolving issues that better data management would have prevented. For Fortune 1000 companies, this is estimated to cost approximately $8.2 million annually in missed opportunities and lost productivity.
These issues reduce efficiency, increase risk, and undermine the value of the very transformation programmes that integration was supposed to enable. AI initiatives in particular are vulnerable: Models trained on inconsistent master data produce unreliable outputs, regardless of how sophisticated the underlying technology may be.
The shift to structured data management
To achieve genuine consistency, organisations need to move beyond integration and focus on data management. This means defining data standards, implementing validation rules, establishing clear ownership of master data domains, and ensuring that data is governed across its full lifecycle – from creation through update to archive.
The benefits of getting this right are substantial. Organisations implementing comprehensive MDM solutions have achieved a 55% reduction in data reconciliation efforts, 42% faster time-to-market for new products, and 33% lower data management costs[2]. McKinsey research has found that organisations with mature MDM practices report up to 40% improvements in data quality metrics and substantially enhanced decision-making capabilities.
When data is managed as a strategic asset rather than a by-product of systems, consistency becomes achievable – and integration finally delivers on its promise.
Building a reliable data foundation
Integration is an important step in modernising systems. But it is not the solution to data quality challenges. Consistency comes from structure, governance, and control – the discipline of treating master data as a managed asset rather than something that just happens.
Organisations that recognise this and invest in managing their data effectively will be better positioned to operate efficiently, make informed decisions, and support long-term transformation. Those that continue to rely on integration alone will find themselves spreading inconsistency faster than they can fix it.
With the right approach, integration can become what it was always meant to be: A reliable foundation for consistent, trustworthy data across the enterprise. The connections between systems matter. But the quality of the data flowing through them matters more.
References
- Master Data Management Tools OvalEdge[↩]
- Enterprise Master Data Management: Trends and Solutions European Journal of Computer Science and Information Technology[↩][↩][↩][↩]
- Data Integration Adoption Rates in Enterprises Integrate.io[↩]