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Enterprise AI depends on more than choosing a model: the data an AI system can reach must be useful, governed, and connected to the context needed to answer a question. A July 2025 Futurum brief produced in partnership with IBM argues that organizations can address fragmented data and hard-to-use content through an operational data fabric—without necessarily replacing every existing data system. The report’s featured platform is IBM watsonx.data; its recommendations and product descriptions should be read as partner-sponsored thought leadership, not an independent product comparison.
Why enterprise data can limit AI
Futurum’s July 2025 brief describes a recurring mismatch: enterprises hold valuable information across disconnected systems, but AI applications may not be able to access it consistently or interpret it with enough context. The report points to incomplete, inconsistent, or biased data; fragmented data stacks; and unstructured material such as documents, emails, presentations, and videos that is difficult to make usable for AI.
This matters because an answer can only reflect the information available to the system producing it. If relevant records are inaccessible, inconsistent, or separated from the documents that explain them, an AI application may return an incomplete answer or one that lacks a clear basis. Data readiness is therefore not a substitute for model and application choices; it is a foundation those choices depend on.
The report also cautions that fine-tuning and retrieval-augmented generation (RAG) do not, by themselves, resolve the broader challenge of connecting unstructured sources with established structured-data access. Its argument is that organizations need an operational way to bring those sources into governed, usable reach.
What an AI-ready data foundation should include
Futurum’s proposal is an operational data fabric: a way to unify access across data formats and locations while retaining existing data-management assets where appropriate. In the report’s framing, this is an operating approach for ingesting, governing, and retrieving data across on-premises and cloud environments—not a requirement to move everything into one new repository.
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Consistent access across formats and locations
Access should work across diverse systems and content types, with consistent interfaces rather than a separate access path for every source. The report highlights APIs and SQL access for both human users and agentic systems, alongside support for open data-access standards. It names Apache Iceberg and Databricks Delta Sharing as examples; these are examples in the brief, not a comparative endorsement.
Metadata that supplies context
A semantic metadata layer can help expose relationships and meaning across structured and unstructured information. Without that context, an AI system may retrieve content that contains relevant words but does not answer the actual question or connect properly to structured records. Metadata should help users and applications understand what data represents and how it relates to other sources.
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Governance, lineage, and source permissions
Governance must travel with access. The report emphasizes lineage—the ability to trace where information came from and how it contributed to an answer—and preserving the permissions of the original repository when AI retrieves unstructured content. A system that can retrieve information but ignores its source permissions can expose content to users who were not authorized to see it in the original location.
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The brief’s desired foundation combines quality, accessibility, optionality, open standards, flexible deployment, and secure, governed collaboration. That points to evaluating how well an approach fits existing environments and workloads, rather than treating a single architecture or product as universally suitable.
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How to apply the approach without starting over
The report does not prescribe a universal implementation sequence, but its recommendations suggest a practical way to frame the work: identify what AI needs to access, close the gaps in quality and governance, and then establish reusable access across sources.
- Map the sources behind the use case. List the structured data, documents, and other content an AI application needs, where each source resides, and who is allowed to access it.
- Identify readiness problems. Look for missing or inconsistent data, unclear ownership or meaning, disconnected stores, and content that cannot be retrieved in a controlled way.
- Define shared access and context. Determine which APIs, SQL interfaces, metadata, and open standards can provide consistent access across the relevant environments and formats.
- Set governance controls before broad retrieval. Establish how lineage will be recorded and how source permissions will be preserved when information is retrieved for AI use.
- Fit the architecture to the workload. Assess deployment needs, query engines, interoperability, and cost/performance tradeoffs against the actual use case. The brief supplies these evaluation considerations, but does not compare products or establish which option performs best.
What Futurum’s survey adds—and what it does not
A separate Futurum Group release dated September 23, 2025, reports survey findings from 839 senior data and analytics practitioners. These figures offer context about respondents’ priorities; they are not statistics from the July report and do not demonstrate that a specific data architecture causes better AI outcomes.
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| Survey finding | What Futurum reported |
|---|---|
| Single most important objective | 29% selected “Building AI capabilities.” |
| Biggest challenge in the current data stack | 20% identified “Data quality, trust, and governance.” |
| Investment priorities | 41% prioritized data platforms; 40% prioritized data quality/observability. |
| Lakehouse architecture | 77% were implementing, piloting, or strongly considering a lakehouse built on open table formats and flexible query engines. |
| Data and analytics work | 73% reported a shift toward more business-facing activities. |
| Purchasing plans | 55% planned to increase data and analytics purchasing over the next 6–12 months. |
As Futurum Vice President and Practice Lead Brad Shimmin put it in that September release: “Enterprises are no longer just experimenting with AI; they are re-architecting their entire data strategy around it. However, this aggressive push has exposed a critical vulnerability, namely years of accumulated data debt,” The survey describes its respondents, not every enterprise, and should be interpreted on that basis.
IBM watsonx.data in the report
The brief’s solution spotlight presents IBM watsonx.data as an open data lakehouse with hybrid deployment, open formats including Iceberg, Parquet, and Avro, multiple query engines, and capabilities for vector databases and unstructured data. It also discusses integration, cataloging, lineage, data sharing, and propagation of source permissions.
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Those are descriptions made in a report published in partnership with IBM, not independently tested results or a neutral ranking against competing platforms. IBM’s current watsonx.data resources page continues to list the report and describe the broader rationale of high-quality, accessible, well-governed enterprise data for AI; that product-resource context does not independently validate every capability claim in the brief. Product specifications can change, so confirm current capabilities against IBM’s documentation when evaluating a deployment.
How to evaluate an approach for your environment
The report does not compare competing products. For an actual platform or architecture decision, assess each option against the needs of the use case rather than inferring a winner from the report’s spotlight.
Quick Recap
- Deployment: Does it support the required on-premises, hybrid, or multicloud environment?
- Interoperability: Which open formats and data-sharing approaches can it work with?
- Source coverage: Can it reach both structured records and the unstructured content needed for the task?
- Governance: Can it provide lineage and carry source permissions through retrieval?
- Workload fit: Do its query engines and access methods suit the people and AI systems that will use the data?
- Tradeoffs: What cost and performance implications follow from the architecture for the workloads you actually run?
Sources and further reading
- Read the eight-page July 2025 Futurum brief hosted by IBM.
- Read Futurum Group’s September 23, 2025 survey release.
- See IBM’s watsonx.data resources page.
- See Elsevier’s listing for John Ladley’s Data Governance, 2nd Edition. The publisher describes it as guidance on designing, initiating, executing, and sustaining a governance program, with coverage of data quality and data strategy.
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