Florian Bigelmaier
Analyst Data & Analytics
Florian Bigelmaier is an analyst with a focus on data management and data intelligence.
Take AI from Pilot to Production with Data Intelligence
Independent guidance for catalog, governance, and marketplace decisions. Based on 10,000+ user survey responses per year and 500+ vendor and product analyses
25+ years of independent market research · 10,000+ user survey responses per year · 25 analysts
Data intelligence determines whether AI delivers
Data intelligence platforms determine whether data and AI initiatives deliver or remain stuck in the pilot stage. Data catalogs, data governance, data marketplaces, and context engineering are converging into a single platform class, while the EU AI Act, NIS2, and DORA are force organizations to act. CDOs, governance boards, and platform owners face six- and seven-figure investment decisions that will shape their architecture, operating model, and AI roadmap for the next three to five years.
For more than 25 years, BARC has guided organizations through these decisions as an independent analyst firm. BARC does not provide implementation services, resell software, or accept vendor commissions. Our data intelligence consulting services address common client challenges, platform selection criteria, and the analyst support provided throughout the project.
Production AI use cases depend on data intelligence in 2026
The platform decision determines whether an organization can reliably find and trust its data and deliver it to AI models and agents at runtime. Five data points from current BARC research illustrate the state of the market in 2026:
- 44.55% of companies identify data quality as the biggest obstacle to AI, more than twice the share reported in 2024.
- Data products have entered the mainstream: 93% of self-identified leaders already use data products in production. The focus has moved beyond the concept itself to making data products discoverable, understandable, trustworthy, and governed.
- Governance is the biggest bottleneck for data products: 39% of companies still identify data product governance as a challenge. Effective data products require clear accountability, quality rules, contracts, and controlled access.
- Data fabric improves data access, control, and trust: 68% of respondents report improvements in data accessibility, data control, and data trust. A key enabler is the data fabric’s metadata layer. Metadata makes data discoverable, understandable, traceable, governable, and safe to use.
- Data discoverability and usability remain major barriers to AI: 70% of companies report that less than half of their unstructured data is discoverable and usable for AI. This makes context a core requirement. Metadata, classification, lineage, and governance must make data understandable, assessable, and safe to use.
The market maturity test asks whether a data intelligence platform delivers context, governance, and data products at the speed users and AI require. Three-quarters of platform implementations fail this test. The main causes are use case prioritization, adoption, and business alignment. The technology itself is rarely the problem.
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What users experience today and what they expect after the decision
| What users experience today | What they want to see after the decision |
|---|---|
| "The catalog is populated, but nobody in the business uses it." | An adoption strategy that makes the catalog the go-to resource for business teams |
| "Governance sits in a PDF, but in Snowflake and other tools it never actually applies." | Enforced policies in Snowflake, Databricks, Microsoft Fabric, and similar platforms, automated and auditable |
| "We do not know which AI models run in the company and which data they use." | An integrated model and data inventory with risk classification in line with the EU AI Act |
| "Lineage stops at the edge of one platform, and a gap remains across platforms." | End-to-end lineage across data sources, pipelines, BI reports, and AI models |
| "AI agents hallucinate because they lack verified business context." | A context architecture that delivers knowledge, semantics, lineage, and policies at runtime |
| "Our pricing model does not scale with the number of stewards and users." | A licensing model with fair differentiation between read users, stewards, and connectors |
| "We talk about data products, but our tools do not support their lifecycle." | A marketplace with owner accountability, contracts, SLAs, and lifecycle management |
| "We operate three overlapping tools for catalog, glossary, and governance." | A consolidated platform with clear roles, interfaces, and a shared metadata layer |
| "Stewardship formally sits with the business, but in practice IT handles the work alone." | An operating model with clear roles, incentives, and escalation paths for business stewards |
| "Unstructured data and knowledge assets stay outside the catalog." | A knowledge graph that brings structured and unstructured assets together in context |
Common triggers for a data intelligence engagement with BARC
Data owners and governance leaders contact BARC for recurring reasons. These often overlap.
Trigger 1: AI initiatives lack context. Organizations have started their first GenAI and AI agent initiatives. Models without verified business context, lineage, or governance do not reach production. BARC designs a context engineering architecture, evaluates the appropriate platform category, and develops a roadmap for production AI use cases within 6 to 18 months.
Trigger 2: The data catalog is in place, but adoption is lagging. The first generation of data catalogs has been in use for 3 to 5 years, with a focus on IT-driven metadata. The catalog is populated, business teams do not use it, and pricing negotiations with the vendor are becoming more difficult. BARC assesses 3 options: replacing the platform, expanding it with marketplace and governance capabilities, or resetting the operating model.
Trigger 3: Regulation requires action. The EU AI Act, NIS2, DORA, industry-specific requirements, or a specific audit finding make systematic governance unavoidable. Existing solutions cover traditional data governance, but capabilities for AI models, risk classification, and enforceable policies within data platforms are missing. BARC helps define a target architecture for data and AI governance and select the right platform.
Trigger 4: Distributed catalogs require an enterprise-wide view. Many organizations operate multiple catalogs and metadata solutions in parallel. At the same time, they need an enterprise-wide view of data, data products, definitions, lineage, and accountability. Whether a “catalog of catalogs” is the right answer depends on the specific situation. BARC assesses whether existing catalogs should be retained, connected, harmonized, or consolidated. AI increases the need for unified, verified context, and BARC helps organizations develop a context strategy that fits their requirements.
All of these triggers share the same conditions: a decision within 6 to 18 months, a six- or seven-figure investment, and stakeholders ranging from the CDO and data governance board to compliance and risk leaders.
Who we work with on data intelligence projects
In data intelligence selection and architecture projects, BARC typically works with the following stakeholders:
- Data leadership: Chief Data Officer, Chief Data and Analytics Officer, Head of Data Management, Head of Data Governance, Head of Data Strategy.
- Platform and architecture leaders: Enterprise Architect, Business Architect for Data Platforms, Head of Data Architecture, Lead Data Engineer.
- Governance and stewardship leaders: Data Governance Manager, Data Steward Lead, Information Owner, Data Quality Manager, Compliance Manager.
- AI and analytics leaders: Head of AI, Lead AI/ML Engineer, AI Governance Lead, Head of BI and Analytics.
- Executive sponsors: CIO, CDO, CISO, COO, and CFO with a data strategy mandate.
BARC supports midmarket companies and large enterprises across all industries, with a particular focus on financial services, manufacturing, utilities, retail, and the public sector.
How we advise on data intelligence
The catalog question has grown into a topology question. Which architecture can carry context and knowledge engineering, data and AI governance, and data shopping in a single design?
The market is moving in three directions at once: Catalog, governance, and marketplace capabilities are converging into a single platform class, AI is driving automation and new interaction models, and regulation is moving governance out of documents and into enforceable code.
The market is also moving toward context platforms. Data catalogs, knowledge graphs, catalogs with integrated graph technology, and semantic tools increasingly claim to provide context for AI. The right solution cannot be determined solely by whether it provides context. BARC helps identify the relevant use cases, clearly define and scope the target state for context, and build a sustainable context strategy. The term is now used so broadly that, without clear boundaries, it can quickly become another poorly defined platform category.
BARC has evaluated data intelligence platforms since the first edition of the BARC Score Data Intelligence Platforms in 2022. BARC has used the same methodology throughout, based on user surveys, vendor assessments, architecture reviews, and economic viability analyses. BARC does not generate revenue from implementation services or software reselling and does not accept vendor commissions. This independence keeps our recommendations defensible in contract negotiations with vendors.
For six- and seven-figure data intelligence decisions, BARC makes five contributions that distinguish its approach from internal evaluations, Big Four consulting firms, implementation partners, and specialist consulting firms:
- BARC bases its assessments on evidence from more than 1,500 user surveys per year, the ongoing BARC Score Data Intelligence Platforms, and BARC vendor briefings.
- A structured selection methodology with prebuilt requirements catalogs, use-case-driven shortlist logic, and proof-of-concept scripts saves time and reduces selection risk.
- Negotiation benchmarks for pricing and contract models, supported by actual user data on licensing flexibility and scalability, help clients negotiate on equal footing.
- BARC has no implementation, reseller, or vendor interests and bases its recommendations on strategic, functional, and economic fit.
- BARC combines market, technology, customer, and stakeholder perspectives to provide a solid basis for decision-making and assess key risks related to architecture, adoption, governance, dependencies, and economic viability.
Your experts for data intelligence
Timm Grosser
Senior Analyst Data & Analytics
Timm Grosser is a Senior Analyst Data & Analytics with a focus on data management.
Kevin Petrie
VP of Research at BARC US
Shawn Rogers
CEO BARC US
International data & analytics thought leader with nearly three decades of experience spanning analyst, author, and executive roles across BI, AI, and cloud technologies.
Thomas Zeutschler
Senior Analyst Data & AI
As an analyst and technology expert, Thomas supports his clients in adapting modern data & analytics concepts and technologies and in developing data-driven processes and business models.
Your options with BARC
Decision criteria for a sustainable data intelligence platform
A sound decision about a data intelligence platform in 2026 requires clear answers to six questions:
- Use case priority. Which of the three use cases (context and knowledge engineering, data and AI governance, data shopping) is the primary bottleneck over the next 18 months and thus the driver of the selection?
- Metadata reach. Which sources, platforms, BI tools, ML tools, and unstructured repositories must be covered, and at what depth (lineage, profiles, quality)?
- AI integration. What are your requirements for active metadata, MCP connectivity, programmatic APIs, AI asset management, and context delivery at inference time?
- Governance depth. Which regulatory frameworks (EU AI Act, NIS2, DORA, GDPR, and industry-specific requirements) must the platform support, and at what level of detail?
- Operating model. Central governance organization, federated data mesh model, or hybrid? Who is responsible for stewardship and adoption?
- Economics and licensing model. Which pricing model (read users, stewards, connectors, asset volume) fits your expected usage over the next 3 to 5 years?
Answer these six questions with an explicit architecture and operating model statement before creating the vendor shortlist. Without clear answers, the software selection process becomes little more than a feature comparison.
Planning a data intelligence platform decision within the next 12 months?
A 30-minute BARC briefing determines whether a Guidance Package, full software selection, or health check fits your situation. The discussion provides a clear next step, regardless of whether you decide to continue working with BARC.
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Frequently asked questions about data intelligence
A data catalog discovers and describes data assets. A data governance tool manages policies, stewardship, and compliance. A metadata management platform collects, normalizes, and connects metadata from different sources. A data intelligence platform combines these disciplines in a single architecture and adds marketplace capabilities, knowledge and context engineering, and AI asset management. In the BARC Score 2026, we define platforms that address all three core use cases as data intelligence platforms.
The right platform depends on your primary use case, the maturity of your data governance organization, your heterogeneous platform landscape, and your AI roadmap. In the BARC Score 2026, Informatica, Collibra, and Alex Solutions offer the broadest functional coverage. DataHub and Ab Initio set the standard for context engineering. Atlan and Alation stand out for user experience. A well-founded decision requires a structured selection process. Vendor presentations alone are not enough.
Platform-native tools such as Microsoft Purview, Databricks Unity Catalog, and Snowflake Horizon provide strong coverage within their own environments. Their coverage is rarely sufficient for the broader data landscape. In heterogeneous architectures, regulated industries, and AI-centric environments, a standalone data intelligence platform complements hyperscaler tools with cross-platform lineage, enforced governance, and a centralized marketplace experience. During the selection project, BARC assesses where to draw an appropriate boundary between hyperscaler tools and third-party platforms.
Context engineering provides AI models and agents with the right business context at runtime: semantics, lineage, policies, definitions, and examples. Without this context, agents hallucinate or make decisions based on outdated or unapproved data. In 2026, data intelligence platforms provide programmatic access to metadata through MCP, open APIs, and standard protocols. BARC identifies context engineering as the most important new value driver in the market.
Keeping a data catalog in active use requires three measures to work together.
- Start by prioritizing a business-owned use case before selecting a tool, because catalogs tied to a concrete business need are more likely to be adopted.
- Define an operating model with real accountability, incentives, and escalation paths rather than relying only on role documents.
- Then support adoption with measurable targets for business user activation, a consistent stewardship cadence, and marketplace orders.
AI governance covers the model inventory, data provenance and lineage for each model, risk classification under the EU AI Act, audit trails, policy enforcement in training and inference, and the links between data products and AI use cases. Platforms with this coverage treat AI models as first-class catalog objects instead of shadow assets.
No. BARC does not provide implementation or integration services, act as a reseller, or accept vendor commissions. This separation keeps our recommendation defensible during contract negotiations. For implementation, we recommend independent partners based on the ongoing BARC Data & Analytics Service Provider Survey.
A workshop for a strategic situational assessment takes one day. A full software selection including shortlist, demo evaluation, proof-of-concept scripts, and contract recommendation typically runs over 6 to 12 weeks, depending on the scope and the number of vendors to be evaluated.
BARC receives no commissions or shares of licensing revenue from software vendors. Vendor evaluations draw on analyst reviews and more than 10,000 user survey responses each year. Sponsorship of BARC studies is fully disclosed and does not influence the evaluation methodology.
BARC Consulting covers data strategy, data platforms, data management, AI governance, integrated planning & analytics, corporate performance management, and ESG. An overview of all consulting offerings can be found here.
No. BARC structures the selection so that suitable implementation partners are included and evaluated directly in the candidate pool. You receive recommendations for both the platform and the implementation partner.
This is a valid concern when working with strategy consultancies. BARC addresses it in two ways. Our senior analysts have 10 to 25 years of experience with the platforms you are evaluating. They understand implementation challenges from more than 500 projects they have supported. Our deliverables include an operating model outline, skill requirements, pricing negotiation benchmarks, and concrete next steps for the first 90 days after contract signing.
In that case, the Data Intelligence Guidance Package provides a pragmatic starting point. In a one-day workshop, we address the six core questions: use case priority, metadata reach, AI integration, governance depth, operating model, and economics. The result provides a defensible internal basis for the investment in a full selection or health check.
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