From CPO Outlook survey responses to workshop mapping exercises, data challenges emerge as the persistent barrier beneath every other transformation struggle

Something interesting happened when we aggregated findings from CPO Outlook 2025 in Stockholm and EBG | Xperience events across three Nordic cities. Different questions, different formats, different participants – yet the same fundamental issue surfaced repeatedly. Data quality isn’t just one challenge among many. It appears to be the challenge beneath every other transformation barrier procurement faces.

How Data Quality Shows Up in Different Contexts

Start with the direct question from CPO Outlook. When asked about primary data challenges, 74% cited low-quality or missing data, and 67% struggled with disconnected systems. These aren’t independent problems – they’re interrelated aspects of the same dysfunction.

But the data quality issue becomes more revealing when you look at how it appears in other contexts:

In AI adoption barriers: EBG | Xperience Copenhagen found that 56% identify data quality and system fragmentation as the primary obstacle to AI implementation. Organizations want to deploy AI but recognize they lack the data foundation AI requires.

In technology wish lists: The most frequent requests from CPO Outlook involve data integration – “clean data from various sources,” “better integrated systems with high quality data,” “free text into standard clean data.” Procurement professionals aren’t asking for new capabilities. They’re asking to make existing data usable.

In risk management challenges: Visibility beyond Tier 1 suppliers remains elusive – 59% in CPO Outlook and 75% in EBG | Xperience Gothenburg report lacking this visibility. This gap stems partly from data problems – difficulty connecting internal performance data with external risk intelligence, inconsistent supplier identifiers across systems.

In digital maturity: With 78% at “developing” maturity (some digitalization but limited integration), organizations have implemented systems but struggle to make them work together. The integration challenge is fundamentally a data challenge.

The pattern suggests data quality operates as a foundational constraint. Organizations can’t advance digital maturity, deploy AI effectively, or manage extended supply chain risks without first addressing how data flows through their procurement ecosystem.

Why Data Problems Persist Despite Recognition

If 74% recognize data quality as critical, why does the problem persist? The survey data reveals several contributing factors:

Unclear ownership (28%): Nearly three in ten cite unclear data ownership as a challenge. Procurement needs good supplier data but doesn’t control the ERP. IT manages systems but doesn’t understand procurement’s data requirements. Everyone’s responsible means no one’s accountable.

Lack of governance (34%): One-third struggle with lack of data governance. Without governance frameworks, data quality improvement has no structure, no enforcement mechanisms, and no sustainability.

System fragmentation (48%): Organizations accumulated systems over years, each addressing specific needs. The sourcing platform, contract repository, ERP, spend analytics tool, and risk monitoring service all maintain their own data models. Nobody deliberately designed this fragmentation.

Resource overload (62%): The most cited operational bottleneck is having too many tasks and insufficient time for strategy. Data quality improvement requires sustained effort. When teams are overwhelmed with reactive work, data governance becomes the task that gets deferred.

How Data Problems Block AI Adoption Specifically

The EBG | Xperience Copenhagen finding that 56% cite data quality as their primary AI barrier deserves closer examination. The survey data reveals why data quality becomes particularly critical for AI:

Current AI adoption status: 60% are exploring AI but haven’t widely implemented solutions. Another 48% only use AI features embedded in existing systems. This means most organizations remain in early stages despite significant AI discussion.

The barrier hierarchy: Data quality and system fragmentation (56%) far exceeds other barriers. ERP integration challenges (33%) and lack of internal expertise (30%) follow, but data quality dominates.

The structural challenge: AI requires integrated data from multiple sources – transactional data, contract data, supplier information, risk signals, market intelligence. Organizations with disconnected systems (67% from CPO Outlook) and poor data quality (74%) lack the foundation AI needs.

The data reveals a sequence problem. Organizations want to deploy AI to gain competitive advantage, but AI effectiveness depends on data quality most organizations haven’t yet achieved.

The System Fragmentation Dimension

The 67% facing disconnected systems in CPO Outlook and 48% citing system fragmentation as an operational bottleneck highlights that data quality isn’t just about accuracy within individual systems. It’s about how data flows between systems that were never designed to work together.

The technology wish list responses reference multiple system components:

• “Integration of source of contract and CRC and TPRM system”
• “Building an end to end automated process from Spend Analysis to Contract Lifecycle Management”
• “Bridge S2P from sourcing to contracting to ordering, delivery and payment”
• “Connecting P2P closer with risk agreements”

These responses indicate procurement ecosystems typically include sourcing platforms, contract repositories, ERPs, spend analytics tools, risk monitoring systems, and supplier management platforms. Each system made sense when purchased. Together, they create the integration challenges that show up as both disconnected systems (67%) and system fragmentation bottlenecks (48%).

Connecting Data Challenges to Other Transformation Barriers

The data quality patterns connect to other survey findings in revealing ways:

Resource overload (62%) and data chaos (74%): These two challenges reinforce each other. Poor data quality creates manual work – reconciling inconsistent records, hunting for information, validating questionable data. This manual work consumes resources, leaving insufficient time to address the data quality problems causing the manual work.

Digital tools proficiency (64%) as the top future competency need, alongside system fragmentation (48%) as a current bottleneck: These two findings sit in interesting proximity. Organizations are simultaneously dealing with disconnected systems today while recognizing that digital tools proficiency will be essential going forward. Whether the emphasis on digital skills reflects anticipation of new tools, or the need to better navigate existing fragmented systems, the data doesn’t tell us – but the pairing is worth noting.

Limited sub-tier visibility (59% in CPO Outlook, 75% in Gothenburg during with this was the theme) and disconnected systems (67%): Extended supply chain visibility requires connecting internal procurement data with external intelligence sources. Organizations struggling to integrate their own internal systems will find it even more difficult to incorporate external data streams.

Digital maturity plateau (78% at “developing”) and data challenges (74%): Organizations have moved beyond basic digitalization but can’t reach advanced maturity. The concentration at “developing” level suggests a common barrier preventing further progress. Data quality and integration challenges provide a plausible explanation.

The Workshop Mappings Add Context

Beyond the quantitative survey data, the workshop mapping exercises revealed additional patterns:

CPO Outlook Skills Mapping: Data & Analytics capabilities appeared in the “over-invested zone” – high readiness but questioned relevance. Organizations have built analytics teams and bought tools, yet 74% struggle with data quality. This suggests organizations may have capability without effectiveness.

Stockholm’s “Skilled but Stuck” observation: In the mapping of competence readiness versus empowerment level, multiple organizations positioned in zones where teams possess necessary skills but lack empowerment to act. Data quality problems contribute to this dynamic – when data is unreliable, organizations hesitate to empower teams to act on it.

Copenhagen’s value-complexity AI mapping: When participants positioned different AI applications by implementation complexity versus value, data-dependent applications showed wide positioning variance. Some organizations saw these as achievable, others as highly complex. The difference likely reflects underlying data maturity.

What the Patterns Suggest

Synthesizing across the CPO Outlook and EBG | Xperience findings, several observations emerge:

Data quality appears foundational to other transformation goals: Whether organizations want to deploy AI (56% cite data as primary barrier), improve risk visibility (59-75% lack sub-tier visibility depending on the survey), or advance digital maturity (78% stuck at developing), data quality emerges as the persistent constraint.

The problem is structural, not just technical: With 34% lacking governance, 28% facing unclear ownership, and 67% dealing with disconnected systems, data quality problems stem from organizational structure and accountability gaps as much as from technical challenges.

The resource trap is real: With 62% experiencing resource overload and 74% struggling with data quality, organizations face a difficult dynamic. Poor data creates manual work that consumes resources. Resource constraints prevent investment in data quality improvement.

Questions the Data Raises

Rather than prescribing solutions, the survey patterns raise questions worth considering:

• If 74% struggle with data quality, why isn’t data governance the dominant procurement transformation priority?
• What would it take to move from 78% at “developing” maturity to “advanced” when data and integration challenges persist?
• Can organizations successfully deploy AI (60% exploring) before addressing the data quality issues 56% cite as the primary barrier?
• How do organizations break the cycle where poor data creates resource overload that prevents data quality improvement?
• What explains the gap between having data capabilities (workshops show “over-invested”) and struggling with data quality (74% cite this challenge)?

The Nordic procurement community participating in CPO Outlook and EBG | Xperience events continues exploring these questions together. Not because anyone has definitive answers. Because collective learning from shared challenges helps everyone make better decisions about where to focus limited resources.

Continue Networking with EBG

The data quality patterns explored here emerged from aggregating findings across CPO Outlook 2025 in Stockholm and EBG | Xperience events in Stockholm, Gothenburg, and Copenhagen. These aren’t isolated survey results. They’re consistent themes that appear when procurement professionals compare experiences across different contexts.

This is how professional communities enable progress – not through prescriptive consulting that claims to know your answers, but through structured conversations where procurement professionals discover patterns in shared challenges and learn from each other’s experiments.

CPO Outlook 2026 (in Stockholm) and EBG | Xperience 2026 (in Helsinki, Stockholm and Malmö) will continue these explorations, bringing together Nordic procurement professionals to compare what’s actually working and what remains frustratingly difficult.

Register your interest for 2026 events today as registration is open!