AI Demands: Data Stewards: step into a role that’s bigger, faster, and riskier than ever before. As organizations scale Generative AI and Large Language Models (LLMs), the quality, traceability, and governance of data has become a non-negotiable foundation for trust, ethics, and performance.

📈 Consider the data landscape:

• Global data is expected to rocket to 175 zettabytes by 2025 (IDC)
• 73% of AI initiatives fail due to poor data quality (MIT Sloan)
• Only 22% of companies have real-time data observability (Forrester)

Design for Data Governance, must deliver value. As AI systems become central to decision-making, customer service, and business strategy, Data Stewards must lead the charge—ensuring the right data fuels the right models, without risk, bias, or misinformation.

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⚙️ The New Mandate: From Gatekeepers to Strategic Enablers

The Role Has Evolved

Data Stewards are no longer just compliance officers or custodians of metadata. They are now strategic enablers of enterprise-scale AI—responsible for validating, curating, and protecting data across a growing web of sources, pipelines, and use cases.

Their responsibilities include:

• Monitoring real-time data ingestion from APIs, sensors, web sources
• Ensuring accuracy, completeness, and trustworthiness of training data
• Tagging, tracing, and remediating biased or harmful data sources
• Enforcing governance in hybrid and cloud-native environments

💡 “Without stewards, AI becomes guesswork at scale.”
– Chief Data Officer, Financial Services Firm

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🛠️ Core Functions: What Data Stewards Must Do Now

1. Real-Time Data Validation

AI models don’t wait—and neither can data governance. Data Stewards must now:

• Apply automated quality checks at the point of ingest
• Use AI-assisted anomaly detection to spot bias or drift
• Enforce data scoring metrics: accuracy, consistency, reliability, and lineage

📊 Stat: 91% of enterprises say real-time data validation is “mission-critical” to AI success (Gartner, 2024).

Data Quality Supportive Monitoring

Objective: Continuously assess and manage critical data quality dimensions.

Dimension	Focus	AI Risk if Ignored
Accuracy	Reflects real-world truth	Hallucinations, false insights
Completeness	No missing fields or gaps	Biased predictions, skewed models
Consistency	Uniformity across systems	Conflicts in AI model decisions
Timeliness	Up-to-date and current	Outdated results, regulatory risk
Lineage	Full trace from source to model	Lack of auditability or accountability

• Automate DQ rules and scoring using Data Quality tools (Informatica, Talend, Great Expectations)
• Set thresholds and alerts for DQ issues
• Enable role-based access to DQ dashboards for transparency

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2. Adopt Data Fabric as a Strategic Framework

Workflow Data Fabric is emerging as the go-to architecture for enterprises juggling hybrid data, distributed systems, and complex AI pipelines.

📌 What It Enables:

• Seamless access across silos
• Active metadata management
• Real-time lineage and impact analysis
• Embedded governance and policy enforcement

For Data Stewards, this means gaining visibility and control over every point in the AI pipeline—from source to inference.

Data Strategy Alignment:

Ensure alignment with enterprise AI, analytics, and governance goals.

• Define data stewardship goals in collaboration with AI, BI, and compliance teams
• Establish data domains, ownership, and accountability (RACI matrix)
• Integrate AI-readiness into enterprise data governance policies
• Identify regulatory frameworks (GDPR, HIPAA, CCPA, AI Act)

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3. Collaborate With Knowledge Managers

In an AI-driven enterprise, Knowledge Managers and Data Stewards must work in sync to ensure what AI “knows” is verified and governed.

Together, they should:

• Define trusted repositories for training and fine-tuning
• Tag enterprise content with provenance and usage rights
• Audit knowledge inputs to prevent misinformation leaks
• Monitor how LLMs use, quote, or transform corporate knowledge

This collaboration helps organizations build AI literacy, protect institutional knowledge, and avoid reputational damage from hallucinated or unauthorized content.

Data Lineage & Impact Analysis

Objective: Trace full data journey to support AI transparency and trust.

• Visualize lineage from raw data → transformations → analytics → AI model
• Identify downstream dependencies for every dataset
• Use active metadata to map data relationships and quality impacts
• Enable root cause analysis during model failure or incident response

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⚠️ What’s at Risk Without Modern Stewardship?

Without real-time standards and Data Fabric oversight:

• Generative AI can spread misinformation or toxic outputs
• AI decisions become non-compliant, biased, or unverifiable
• Legal exposure increases due to data misuse or traceability failures
• Trust erodes—both inside and outside the organization

Real-world examples show the risk:

• A U.S. healthcare firm was sued after a chatbot incorrectly described benefits due to outdated training data, the impacts were profound to those healthcare patients, with dire consequences.
• A major LLM model faced public backlash over biased outputs traced to low-quality data
• A financial institution had to halt its AI rollout after failing a data audit triggered by regulators

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✅ Final Word: Lead with Data, Govern with Confidence

AI Demands: Data Stewards to evolve—not incrementally, but fundamentally.

The volume, velocity, and volatility of today’s data environment means stewards must:

• Use intelligent architecture like Data Fabric
• Collaborate across silos to align knowledge and governance
• Champion data ethics and accountability in every AI initiative

With the right tools, partnerships, and mindset, Data Stewards are no longer reactive—they are essential to AI’s long-term success.

Other AI Demands: Data Stewards Resources

• 6 Data Governance Principles for Reports and Dashboards
• A-Z Data Fabric Glossary
• Artificial Intelligence A-Z Glossary
• Business Process Improvement Glossary
• Comprehensive Guide to Data Stewardship
• DAMA Data Management Guide
• Data Request Best Practices and Life Cycle of Data Requests
• Data Stewards Best Practice ideas from MIT Sloan
• Data Stewards’ Best Practices | Harvard Information Security and Data Privacy
• Designing data governance that delivers value | McKinsey
• Wharton Accountable AI Lab – Wharton AI & Analytics Initiative
• Workflow Data Integration Fabrics