Companies continue pouring money into AI, analytics, and automation—but for many, the returns aren’t just underwhelming; they’re practically nonexistent. That’s the central message in a new blueprint from Info-Tech Research Group, which argues that enterprises aren’t suffering from a lack of innovation, talent, or technology. They’re suffering from something far more fundamental: flawed or nonexistent data operating models.
In a business climate where AI hype has reached fever pitch and new automation tools launch weekly, the research cuts through the noise with a point that’s deceptively simple: you can’t scale advanced data initiatives on top of shaky foundations. And, as Info-Tech puts it, most foundations are wobbling.
The report—Establish the Target Operating Model Needed to Execute Your Data Strategy—lays out how organizations can actually operationalize their data ambitions instead of letting them die in strategy decks and half-executed pilots. The firm’s findings confirm what many IT and data leaders quietly admit: strategy is the easy part. Execution is where everything breaks.
The Strategy-to-Execution Gap: A Growing Industry Pain Point
Info-Tech’s research lands at a moment when organizations are aggressively modernizing their data environments, deploying generative AI, and scaling analytics footprints. Yet even as technologies advance, success rates have not. Many companies can articulate a data strategy—but few can deliver one.
According to the report:
Organizations frequently jump straight from vision to execution without clarifying ownership, governance, or the collaboration model required to make cross-functional data work actually work.
In practice, this leads to operational chaos:
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Data quality whiplash from team to team
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Over-budget or stalled initiatives
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Analytics pipelines built on sand
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Conflicting definitions of “truth” across departments
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Expensive tools being used in expensive ways to produce lackluster results
The research calls this an “operating model failure,” not a technical one. In other words: the problem isn’t your data platform; it’s the way teams interact around it.
Nysa Zaran, research director at Info-Tech, puts it bluntly:
“Organizations often believe their data strategy is sound, but most fail at the operating model level, where ambiguity around ownership, decision rights, and partnership undermines progress.”
That may not be comfortable to hear, but it’s accurate. The industry has long treated operating models as an afterthought—something implied rather than explicitly designed. Info-Tech’s stance is that this approach is no longer tenable, especially as AI initiatives demand more coordination and discipline than previous analytics waves.
Why Operating Models Break: The Five Structural Failures
Though each organization’s data environment is unique, the failures that plague them are shockingly similar. Info-Tech identifies five recurring breakdowns that sideline even the most promising data investments:
1. Operating Models Aren’t Grounded in Strategy
Leaders often craft an elegant data strategy without specifying how the work will get done. Without explicit roles, ownership boundaries, and governance mechanisms, execution devolves into tribalism and improvisation.
2. Overengineering the Wrong Layer
Teams love building orchestration and infrastructure—but underinvest in the data services layer, where business value actually emerges. The result: fancy pipelines delivering…not much.
3. Foggy Capability Ownership
Is data quality owned by IT? By the business? By a governance team? By everyone? By no one?
The report says unclear ownership remains one of the most pervasive blockers to progress.
4. Avoidance of Real Negotiation
Teams dodge difficult conversations about accountability, expectations, and risk-sharing. Without negotiation, alignment becomes mythical, and buy-in evaporates under stress.
5. Tech Purchases Made Without Model Implications
Organizations buy shiny tools—catalogs, platforms, automation suites—without understanding how these investments change (or should change) the operating model. This leads to mismatched capabilities, idle features, and duplicated tools.
The result of these failures is predictable: 94% of business leaders believe they “should be getting more value from their data.”
The report argues they’re not wrong—but they’re blaming the wrong things.
The Three Dynamics Every Data Operating Model Must Balance
Info-Tech highlights that leaders often misdiagnose operating model issues as technology limitations. But the real challenge is balancing three core dynamics across the entire data ecosystem:
1. Proximity to the Problem
Teams must be close enough to business challenges to design relevant, realistic solutions.
2. Control Over Meaning and Access
Decision rights around definitions, governance, permissions, and quality must be clear—and respected.
3. Cost-Appropriate Scalability
Systems and processes must scale without burning through budgets.
Failing to balance these forces leads to the classic pitfalls: inconsistent standards, endless bottlenecks, broken pipelines, and spiraling costs. Balancing them, however, creates the conditions where AI and analytics can thrive.
Inside Info-Tech’s Four-Phase Framework: A Step-by-Step for Fixing the Problem
The blueprint provides a structured, replicable approach for designing operating models that actually work—not just in PowerPoint, but in real organizations with real constraints.
Below is an expanded look at each phase, along with context from broader industry trends.
Phase 1: Deconstruct and Assess Capabilities vs. Outcomes
Leaders begin by identifying the “success principles” their operating model must uphold. This includes:
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Mapping existing capabilities (and who owns them)
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Visualizing the current, often fragmented, operating model
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Identifying mismatches between capability quality and desired outcomes
This mirrors a trend emerging across the data world: leaders are finally accepting that centralization vs. decentralization isn’t the debate. Clarity is the debate.
This step forces organizations to confront where capability gaps are systemic vs. the result of local dysfunction.
Phase 2: Build the Roadmap and Engagement Strategy
This phase identifies the “mine, ours, yours” of the operating model:
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Mine: Capabilities or responsibilities owned by a specific team
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Ours: Shared responsibilities requiring ongoing coordination
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Yours: Accountabilities held by partner teams or business units
This may sound simple, but it’s one of the hardest parts of data transformation work.
Teams also identify risks, stakeholder implications, and partnership requirements—elements many companies don’t formally map until problems explode.
Phase 3: Co-Design Operating Model Shifts
Here’s where Info-Tech deviates from traditional top-down methodologies. Instead of leaders dictating the new model, they facilitate structured stakeholder negotiations.
The goals:
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Align on accountability
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Expose hidden risks
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Reconcile conflicting expectations
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Define required people/process/technology shifts
This aligns with an emerging movement in the data world: collaboratively designed governance models, similar to the “federated ownership” concept popularized by data mesh frameworks.
The report emphasizes that co-design builds not just a better model, but stronger cross-team relationships—critical for long-term success.
Phase 4: Communicate and Secure Endorsement
Finally, the operating model, roadmap, and risk register are consolidated into executive-ready materials. This is where leaders:
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Communicate operating model decisions
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Clarify funding requirements
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Show how shifts unlock strategic outcomes
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Explain maturity impacts across AI, analytics, and data ops
This is also the point where many data initiatives collapse—because leaders underestimate how much communication and political capital is required to push through organizational change.
Info-Tech’s approach acknowledges this head-on, giving leaders templates and talking points to make the case effectively.
Industry Context: Why This Blueprint Is Being Released Now
Info-Tech’s blueprint arrives at a crucial moment for the enterprise data world. Several industry dynamics make coherent operating models more important—and more difficult—than ever:
Explosion of Generative AI Use Cases
GenAI has introduced new expectations for speed, quality, and accessibility of data. Organizations are scrambling to upgrade pipelines, governance models, security frameworks, and metadata systems. Without a defined operating model, these changes generate chaos, not acceleration.
Pressure to Rationalize Tool Stacks
Tech leaders are being pushed to consolidate BI and data tools, automate workflows, integrate catalogs, and reduce redundant platforms. These decisions require clear ownership and decision rights—something most organizations lack.
Shift Toward Federated Models (e.g., Data Mesh, Data Products)
Data products, domain ownership, and decentralized governance approaches are gaining traction. But without a strong underlying operating model, these frameworks produce more fragmentation, not less.
Demand for Measurable Data ROI
Boards and executives are increasingly skeptical of data programs that can’t show measurable financial impact. A strong operating model is one of the few ways to guarantee consistent, value-oriented delivery.
How This Blueprint Stacks Up Against Competing Frameworks
Compared to data mesh, DAMA-DMBOK, or Gartner’s operating model guidance, Info-Tech’s approach is notably more pragmatic and less ideological:
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It avoids prescribing centralization or decentralization and instead focuses on clarity of ownership.
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It includes negotiation frameworks, which is rare in technical methodologies but crucial in real organizations.
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It emphasizes capability-to-outcome mapping, helping leaders avoid building capabilities that look mature but deliver little value.
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It is technology-agnostic, which is attractive to organizations with complex or legacy-heavy stacks.
While not as conceptual as data mesh or as exhaustive as DAMA-DMBOK, this blueprint may be more accessible for mid-market organizations or those early in their data maturity journey.
The Bottom Line: Technology Alone Won’t Save Struggling Data Programs
Info-Tech’s warning is clear: the technology arms race around AI and analytics will continue, and organizations without strong operating models will continue overspending and under-delivering.
Or, as Zaran summarizes:
“Data strategies only succeed when operating models enable proximity, clarity, and cost discipline.”
If you’ve ever tried to scale an AI initiative only to find two teams arguing over who owns the data, or if your governance council exists only on paper, this blueprint might be worth a close look.
The message isn’t new—but the urgency is. As enterprises move into more advanced and expensive stages of AI adoption, weak operating models will translate into even bigger failures, faster, and at higher price tags.
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