The Data Lead

Transformative Data Leadership: Establishing a Strong Metadata Management Program

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by

Eric Hanson
in , , ,

Data is one of the most valuable strategic assets for organizations, but most companies are not investing fully in properly managing it. Many data leaders I come across are quick to talk about the hot buzzwords like data mesh, data fabric, GenAI, and digital transformation, but neglect to discuss the most important topic affecting the ability for data teams to realize the value of any of these, metadata management. Your LinkedIn news feed won’t be flooded with posts and articles on the topic, because it’s just not as flashy and exciting, but the truth is, the only companies that are able to successfully execute on the more exciting new trends in data are the ones with robust metadata management capabilities already in place. Companies that have spent the time early on investing in building out this foundation are reaping the rewards while their competitors are struggling to understand why they can’t get their investments in AI to pay off.

Data leaders should be shifting their focus and energy into establishing a robust metadata management program that sits at the core and forefront of their data strategies. Successful metadata management ensures data quality, improves discoverability, enhances governance and enables business users and data teams to derive value from enterprise data assets efficiently. In this article I’ll lay out a plan and guidelines for building the metadata management plan and strategy to help you succeed in 2025 and going forward. Use this as an example or template for building your own strategy and plan for your specific needs.

Define Objectives and Outcomes

Clearly define your objectives and desired outcomes.  Below are some examples:

Key Objectives:  

  • Data Discovery: Enable easy access to and understanding of enterprise data.
  • Data Quality: Enforce consistent data definitions and ensure data accuracy.  
  • Governance and Compliance: Ensure compliance with regulations like GDPR, CCPA, and other data privacy laws.  
  • Operational Efficiency: Reduce redundancy and improve productivity for analytics and data engineering teams.  

Desired Outcomes:  

  • Standardized metadata definitions and management processes.  
  • Greater trust in data for decision-making.  
  • Reduced time for data discovery, leading to faster insights.  

Assemble the Metadata Leadership Team

Define the leadership team with defined roles.  Ensure you have executive sponsorship and support.

Key Roles: 

  • Executive Sponsor: Advocate for organizational alignment and funding.  
  • Metadata Steward: Responsible for defining and managing metadata standards.  
  • Data Owners: Subject-matter experts for specific domains.  
  • Data Architects: Technical leads for metadata integration into systems.  
  • Program Manager: Oversees implementation and monitors program KPIs.  

Assess Current State

Evaluate the strengths, weaknesses and gaps in your current metadata management capabilities.

Conduct a Metadata Maturity Assessment:  

  • Identify metadata sources (databases, data lakes, SaaS systems, APIs).  
  • Understand current metadata management tools and practices.  
  • Identify key pain points, such as siloed metadata, lack of standards, or limited metadata automation.  

Define Metadata Management Framework

Define the key pillars of your framework, including metadata types, standards and policies, and processes.

Key Pillars:  

  • Metadata Types:  
    • Technical Metadata: Schema, lineage, and transformations.  
    • Business Metadata: Glossaries, definitions, KPIs, and ownership.  
    • Operational Metadata: Data quality metrics, profiling, and monitoring.  
  • Standards and Policies:  
    • Establish naming conventions and metadata tagging standards.  
    • Create governance policies for metadata lifecycle management.  

Processes:  

  • Metadata collection: Define how metadata will be sourced (manual, automated).  
  • Metadata review: Implement regular reviews by stewards and stakeholders.  
  • Metadata utilization: Embed metadata in workflows for analytics, AI, and compliance.  

Technology Enablement

Choose the tools and technology needed, evaluating different solutions and vendors based on the fit for your organization and the capabilities you want to prioritize.

Select Metadata Management Tools:  

  • Evaluate and implement a metadata management solution such as Collibra, Alation, Informatica, or open-source options such as OpenMetadata.  
  • Ensure the tool integrates with your data catalog, data warehouse, ETL pipelines, and BI tools.  

Capabilities to Prioritize: 

  • Automated metadata harvesting and lineage tracing.  
  • Role-based access control and governance integration.  
  • Collaboration features for documenting business definitions and annotations.  

Develop an Implementation Roadmap 

Break the plan down into manageable deliverables and timelines for execution

Phase 1: Planning and Pilot (Months 1–3)  

  • Finalize metadata framework, policies, and tools.  
  • Identify priority systems for metadata integration (e.g., key data warehouses or analytics platforms).  
  • Conduct a pilot program with a specific business domain or department.  

Phase 2: Scaling Metadata Management (Months 4–9)  

  • Roll out metadata management across other domains and systems.  
  • Train business and technical users to contribute to and utilize metadata.  
  • Implement metadata dashboards for monitoring lineage, quality, and compliance.  

Phase 3: Continuous Improvement (Month 10–Ongoing)  

  • Establish feedback loops for metadata usage and quality.  
  • Regularly update standards, policies, and metadata inventories.  
  • Evolve metadata management capabilities to support new use cases like AI/ML model tracking or real-time metadata.  

Change Management and Adoption

How will you handle change management and drive adoption within your organization to ensure success

Key Initiatives:  

  • Stakeholder Engagement: Regular communication with business and IT leaders about the program’s value.  
  • User Training: Conduct workshops and provide documentation on metadata tools and processes.  
  • Incentives: Encourage adoption by demonstrating time saved and value added in analytics and compliance.  

Metrics for Success

How will success be measured for the program implementation

Program KPIs:  

  • % of metadata automated vs. manual.  
  • Time to discover data assets (baseline vs. after implementation).  
  • % compliance with metadata standards.  
  • Reduction in redundant data assets or errors in analytics.  

Business Impact:  

  • Improved data-driven decision-making.  
  • Faster compliance reporting.  
  • Higher productivity of data engineers and analysts.  

Ensure Sustainability

Make sure you have the right people and processes in place to continue to support and maintain

  • Set up a Metadata Governance Council to maintain oversight.  
  • Periodically reassess technology and processes to align with evolving business needs.  
  • Scale the program to support advanced use cases, such as self-service analytics or AI-driven metadata enrichment.  

Data architectures and frameworks are ever changing and evolving, but a solid metadata management foundation will set your team up for long-term success. Implementing a plan and roadmap similar to the one laid out in this article will pay dividends well into the future. Data is king, but to get the value out of it, you need to have trust and reliability in it.

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