This document should serve as a guideline as the actual strategy to evaluate, select, and adopt data catalog withing the organization should be tailored to align with the specific needs, priorities, and technical landscape of the organization.
Strategic Roadmap for Data Catalog Selection
This roadmap is divided into distinct phases, each with clear objectives and deliverables to ensure a successful data catalog implementation.
Phase 1: Assessment & Planning (Weeks 1-4)
1. Establish a Cross-Functional Team
- Appoint executive sponsor from business leadership
- Include representatives from IT, data teams, business units, and designers (UI/UX)
- Designate a project manager to drive the initiative
Critical Success Factor: Executive sponsorship and visible champions are essential at this stage to drive organizational alignment. Leadership support sends a clear message about the strategic importance of the data catalog initiative and helps secure necessary resources.
2. Analyze Current State
- Data Landscape Assessment: Inventory your data assets, sources, and platforms
- Pain Point Analysis: Identify specific data discovery and management challenges
- Stakeholder Interviews: Gather perspectives from data producers, consumers, and governance teams
- Maturity Assessment: Evaluate your organization’s data management maturity
Critical Success Factor: Comprehensive understanding of the existing data ecosystem ensures the selected solution will address actual organizational needs rather than theoretical problems. This foundation impacts all subsequent decisions.
3. Define Requirements
There are many requirements frameworks that can help determine what a data catalog must, should, and could have. In this example, we use the MoSCoW method (Must have, Should have, Could have, Won’t have). This covers basic needs of a catalog, though additional requirements may depend on your organization’s specific needs. For example, organizations in specialized domains like energy should consider domain-specific requirements. Example is SeSaMe which demonstrates how industry-specific terminology, asset types, and relationships can be modeled within a specialized catalog (wind energy domain). This aligns with the Data Catalog Guide approach discussed earlier.
MoSCoW requirements specification for Data Catalog
| Type | Name | Description | Priority | Functional/Non-Functional | Responsible |
|---|---|---|---|---|---|
| User/System | Semantic Searchability | Finding metadata (text and files) must be easy. The catalog must offer a broad, easy-to-use search functionality that allows users to quickly find data sets, documents, reports, and data catalogs with various search criteria and contents of datasets, data sources and actors | High | Functional | |
| System | Advanced Filtering Capabilities | Catalog must provide comprehensive filtering capabilities across metadata areas. User should be able to filter based on multiple criteria. Filtering will search (should be integrated to final user interface) and allow to apply filters and then search within filtered results. System should maintain search relevance during filtering | High | Functional | |
| System | Connections | Populating the catalog with metadata. It is important that the catalog ensures metadata from different sources systems can be ingested and integrated efficiently. System should support connection protocols | High | Non-Functional | - |
| User | Business Glossary | Catalog platform that has support for keeping a business glossary to categorize and describe data and create a context for data catalog. Provide meaning and context to the metadata in the catalog | High | Functional | |
| System/Business | DCAT Extensible Metadata | The collection of the catalog should provide DCAT (Data Catalog Vocabulary) extensible metadata profile. This enables Application Profile for data portal in Europe and an Extension recommendation for public institutions and companies to publish their data and research. Machine-readable descriptions of public data are essential in a common, structured manner and in a machine-readable format | High | Non-Functional | |
| User/System | Custom Metadata Fields | It must be possible to add custom metadata fields | High | Functional | |
| User/Business | Data Ownership | An additional field specifying the ownership of each dataset is the catalog. There could be several scenarios resource owners can be users or departments or external producers. The catalog must allow defining of ownership groups like consumer and data producer | High | Functional | |
| User/Business | Traceability | There is metadata fields where the owner/origin of data/dataset is tracked. The origin is the source of the data and the ability of the system with the link to producer/source system | High | Functional | |
| User/Business | Data Categories | There is a metadata field that enables categorization of the datasets. | Medium | Functional | |
| User | User Interaction | When users to a dataset page, the system should provide links with business glossary or information help for ex. Allow users to report incomplete, missing or badly categorized metadata, also to comment and subscribe to changes in data product | Low | Functional | |
| User/Business | Data Language | Illustrate where the data originates from, and how it is transformed or changed along the way to where it is being used | Medium | Functional | - |
| User | Multiple Language Support | The data catalog should support multiple languages for the business glossary. Encourage and highlight an expanded multilingual perspective | Low | Functional | - |
| User | Metadata Visualization | Catalog should display visualization (charts, graphs) | Medium | Functional | - |
| System/Business | Authentication and Authorization | Catalog must introduce functionality to define and apply rules to manage access to the data catalog. It should have granular permissions beyond a basic group or individual level of access, ideally with visual and detailed permissions matrix | High | Functional | - |
| System | Data Classification and Performance Requirements | Catalog must maintain acceptable response times for search and retrieval of metadata. Performance must be acceptable even with a large number of datasets | Medium | Non-Functional | - |
| System | Event Notifications | The catalog should emit events when metadata changes occur such a dataset or data groups are created, updated, or deleted. It should support mechanisms like a streaming API to enable external systems to respond to changes in near real-time | Medium | Non-Functional | - |
For more comprehensive evaluations, a weighted scoring approach could be implemented to prioritize requirements based on business value, technical feasibility, and implementation effort.
Critical Success Factor: Clear, prioritized requirements ensure that the solution selection process is objective and focused on addressing actual business needs rather than being swayed by vendor marketing or personal preferences.
Phase 2: Solution Evaluation (Weeks 5-10)
1. Understand Available Options
When evaluating data catalog solutions, it’s important to consider various dimensions that affect how well the tool will fit into your organization’s environment. Each dimension represents a spectrum of options with different trade-offs in terms of complexity, cost, and capability. The specific types of data catalogs are described in detail in the Data Catalog Guide section earlier in this guide.
Refer to the Data Catalog Guide table to understand how different catalog types compare across implementation time, technical expertise required, cost, governance capabilities, and other important factors.
Critical Success Factor: Matching the catalog solution to your organizational structure, user needs, and technical environment is crucial for long-term success. A mismatch here can lead to poor adoption regardless of the solution’s technical capabilities.
2. Conduct Solution Assessment
Whether you’re evaluating vendor products or planning custom development, a thorough assessment process is critical to selecting the right approach for your organization’s data catalog implementation.
- Research potential solutions with RFIs (Request for Information)
- Evaluate shortlisted options through demos and technical deep dives
- Connect with reference customers or organizations with similar use cases
- Assess total cost of ownership beyond initial implementation costs, including maintenance, upgrades, and training
When evaluating commercial solutions, be mindful of vendor lock in discussed in Data Catalog Guide. Review the vendor’s commitment to open standards, API completeness, and contractual terms related to data export.
Critical Success Factor: A thorough, objective assessment process helps avoid costly mistakes and ensures the selected solution can grow with your organization’s evolving data needs.
Phase 3: Proof of Concept (Weeks 11-16)
1. Design POC Approach
- Define scope, timeline, and success metrics
- Select representative data assets across different domains
- Include both technical and business users in testing
- Prepare test cases that validate key requirements
Critical Success Factor: A well-designed POC provides real-world validation of how the solution will perform in your environment before committing significant resources to full implementation.
2. Execute POC
- Implement catalog in controlled environment
- Test integration with key data sources
- Validate metadata extraction and enrichment
- Assess search and discovery experience
- Evaluate governance and security controls
For AI-powered catalogs (see AI powered ) metadata discovery in Data Catalog Guide.
Critical Success Factor: Technical integration capabilities are tested during this phase to ensure the catalog can connect with existing systems and automatically extract metadata, which is essential for maintaining an accurate, up-to-date catalog.
3. Analyze Results
- Collect feedback from test users
- Measure against defined success criteria
- Document limitations and workarounds
- Refine requirements based on findings
Critical Success Factor: Objective assessment of POC results helps identify potential issues early and refine the implementation approach before broader rollout.
Phase 4: Implementation & Adoption (Weeks 17-26)
1. Develop Implementation Plan
- Create phased rollout strategy
- Define metadata standards and governance model
- Establish integration architecture
- Develop training and enablement program
- Set up monitoring and success metrics
Critical Success Factor: Governance alignment ensures the data catalog supports and enhances your existing data governance framework, making compliance and stewardship more efficient through automated workflows and audit capabilities (access tracking, change history, usage analytics for example).
2. Pilot Deployment
- Start with high-value, manageable data domain
- Onboard champion users to test and provide feedback
- Iterate based on early adoption insights
- Document best practices and lessons learned
Critical Success Factor: User experience is crucial at this stage—the interface must be intuitive for both business and technical users, integrating seamlessly with tools they already use (like BI platforms or data science notebooks), and delivering quick value for common use cases such as finding trusted data for analytics.
3. Organization-Wide Rollout
- Expand to additional data domains incrementally
- Implement formal training and support programs
- Establish ongoing governance processes
- Develop adoption metrics and KPIs
Critical Success Factor: A robust adoption strategy includes executive sponsorship, clear communication of benefits to different stakeholder groups, metrics to track usage (such as search volumes, user engagement, and time savings), and continuous improvement based on user feedback.
Critical Success Factors
Technical Integration
- Seamless connection with existing data platforms and tools
- Automated metadata extraction where possible
- Comprehensive lineage tracking across systems
Governance Alignment
- Support for your data governance framework and policies
- Clear ownership and stewardship workflows
- Audit capabilities for compliance requirements, including the ability to track who accessed what data, when, and for what purpose, supporting regulatory compliance (GDPR for example)
User Experience
- Intuitive interface for both business and technical users (as discussed in self service vs technical metadata catalogs in Data Catalog Guide)
- Integration with tools teams already use, such as BI dashboards, data science notebooks, or collaboration platforms
- Quick time-to-value for common use cases, like finding trusted data sources for a quarterly report or understanding data lineage for regulatory reporting
Adoption Strategy
- Executive sponsorship and visible champions who actively promote and use the catalog, demonstrating its importance
- Clear communication of benefits to all stakeholders
- Metrics to track usage and business impact
- Continuous improvement based on user feedback
Expected Outcomes
When implemented successfully, a data catalog will:
- Reduce time to find and understand data by 50-70%
- Improve data quality and trust through transparency
- Enable better decision-making through reliable data context
- Foster collaboration across departments and roles
- Create foundation for advanced data governance and analytics
These outcomes directly address the challenges outlined in the What Problems Do Data Catalogs Solve in Data Catalog Guide.