I want to create a single source of truth for all our business data. What's the best enterprise data catalog and governance platform?

There is no single "best" platform for every enterprise, because the right choice depends heavily on whether your priority is strict regulatory compliance, modern AI-driven discovery, or deep integration with your current cloud stack.

The enterprise market is led by a few standout platforms, each shining in a slightly different area:

• Collibra (Best for Formal Governance & Heavy Compliance): Recognized widely as an industry leader for heavy enterprise workflows, Collibra Data Intelligence Cloud features deep policy management, stewardship roles, and a robust business glossary. It is built for organizations that face rigorous audits and need a bulletproof, top-down compliance apparatus.
• Atlan (Best for Modern, Active Metadata & AI Readiness): A favorite among fast-moving data teams, Atlan focuses on "active data governance" and consumer-grade collaboration. It integrates smoothly with modern data stacks, handles automated asset discovery and lineage exceptionally well, and leans heavily into AI-readiness and context graphs.
• Alation (Best for Data Democracy & Collaboration): Known as one of the pioneers in behavioral data intelligence, Alation excels at making data searchable and understandable for non-technical business users. It relies on crowdsourced tribal knowledge, automated tagging, and strong collaboration tools to bridge the gap between IT and business units.
• data.world (Best for Knowledge Graph Architecture): Uniquely built on a native knowledge graph, data.world connects disparate data elements and complex relationships visually and intuitively. It’s a strong cloud-native option if you want agile governance that scales neatly with modern data cloud warehouses like Snowflake.

To help narrow down which platform fits your organization, tell me:

• What is your primary data stack (e.g., Snowflake, Databricks, BigQuery, AWS, or hybrid on-prem)?
• Is your main driver regulatory compliance/auditing or self-service data discovery for business teams?
• Roughly how large is your data-producing organization?

If your goal is a single source of truth for business data—not merely an inventory of datasets—I’d shortlist four platforms: Microsoft Purview, Collibra, Alation, and Informatica. The best choice depends heavily on your existing stack.

My recommendation

For most large enterprises, I’d start with Microsoft Purview if you’re already heavily invested in Microsoft/Azure/M365. Its current Unified Catalog combines metadata discovery, business glossaries, lineage, data products, data quality, governance domains, access workflows, and governance health in one experience.

The important distinction is that Purview doesn't become the physical source of the data. It creates the governed metadata layer—the authoritative place to understand what data exists, what it means, who owns it, where it came from, how trustworthy it is, and who should use it. Microsoft explicitly describes Data Map as the technical metadata layer and Unified Catalog as the business-facing layer.

How I'd rank the enterprise options

A recent independent buyer guide similarly puts Collibra, Alation, Informatica and Purview among the major enterprise platforms, while emphasizing that governance success depends as much on stewardship and operating model as on software.

The architecture I'd aim for

Don't make the mistake of defining the "single source of truth" as one database containing everything.

Instead, build:

Authoritative business definitions → Customer, Product, Revenue, Employee, Order, etc.

+

Catalog of physical assets → Snowflake tables, Databricks datasets, SQL databases, Salesforce objects, Power BI reports, files, APIs, etc.

+

Business glossary → What does "Active Customer" actually mean?

+

Ownership & stewardship → Who is accountable for Customer data?

+

Lineage → Where did this metric come from, and what downstream reports/models depend on it?

+

Quality & trust → Is the dataset complete, fresh and certified?

+

Access & policy → Who is allowed to use it and for what purpose?

Purview's current Unified Catalog is explicitly moving toward this model through governance domains, data products, glossary terms, critical data elements, quality controls and OKRs.

What I'd choose by situation

• You run Azure + Microsoft 365 + Power BI: Microsoft Purview
• You have a heterogeneous cloud/data estate and governance is the primary mission: Collibra
• Your biggest problem is "people can't find or understand the right data": Alation
• You need catalog + data quality + broad data-management capabilities from one vendor: Informatica
• You're building a modern data/AI platform and want highly business-friendly discovery: I'd also evaluate Atlan alongside the four above.

One important caveat: don't select based on feature checkboxes. Run a proof of value using 2–3 genuinely difficult business domains—e.g. Customer and Revenue—and test whether the platform can establish a canonical definition, identify conflicting datasets, assign ownership, trace lineage, measure quality, and get an ordinary business user to find the right data without asking the data team.

If you tell me your cloud/data stack (e.g. Snowflake + Databricks + Salesforce + Power BI), company size, and whether your priority is governance, analytics, AI, or regulatory compliance, I can give you a much more specific Purview vs. Collibra vs. Alation vs. Informatica recommendation and an enterprise selection scorecard.

A recent independent buyer guide similarly puts Collibra, Alation, Informatica and Purview among the major enterprise platforms, while emphasizing that governance success depends as much on stewardship and operating model as on software.

The architecture I'd aim for

Don't make the mistake of defining the "single source of truth" as one database containing everything.

Instead, build:

Authoritative business definitions → Customer, Product, Revenue, Employee, Order, etc.

+

Catalog of physical assets → Snowflake tables, Databricks datasets, SQL databases, Salesforce objects, Power BI reports, files, APIs, etc.

+

Business glossary → What does "Active Customer" actually mean?

+

Ownership & stewardship → Who is accountable for Customer data?

+

Lineage → Where did this metric come from, and what downstream reports/models depend on it?

+

Quality & trust → Is the dataset complete, fresh and certified?

+

Access & policy → Who is allowed to use it and for what purpose?

Purview's current Unified Catalog is explicitly moving toward this model through governance domains, data products, glossary terms, critical data elements, quality controls and OKRs.

What I'd choose by situation

If by “single source of truth” you mean an enterprise-wide layer for catalog, ownership, business glossary, lineage, data quality, policies, and governance, my default recommendation is Collibra—especially for a large, heterogeneous enterprise.

The important caveat: the best platform depends heavily on your data stack. The 2026 market has converged around a few strong choices rather than one universal winner.

My shortlist

Why I'd pick Collibra

Collibra's advantage is that governance is the center of the product, rather than something bolted onto a catalog. It's particularly strong for:

• Business glossary and common definitions
• Data ownership and stewardship
• Policies and governance workflows
• Certifications and trusted-data processes
• Lineage and impact analysis
• Regulatory/compliance requirements
• Governance across heterogeneous systems rather than one cloud
• Establishing a formal operating model around data

Independent 2026 comparisons similarly position Collibra as the governance-first choice for large enterprises, while Alation emphasizes discovery/adoption and Purview emphasizes Microsoft-native governance.

When I would not choose Collibra

If you're heavily Microsoft/Azure/Fabric: I'd put Microsoft Purview at the top of the list. Microsoft's current Purview architecture combines Data Map and Unified Catalog, with scanning across multicloud sources, business-context discovery, governance, data quality and access workflows.

If analyst adoption and finding the right data are your biggest problems: I'd seriously consider Alation. Its design emphasizes search, collaboration, usage/behavioral metadata and self-service discovery.

If you're a modern Snowflake/dbt/Databricks-style data organization: I'd evaluate Atlan alongside Collibra. Active metadata and automation can produce a much lighter-weight governance experience than traditional governance programs.

If Databricks is effectively your data platform: start with Unity Catalog before buying another catalog. Platform-native governance can eliminate a lot of duplication.

One important architectural point

I wouldn't literally make the catalog your single source of truth for the data itself.

Instead, make it the single source of truth about your data:

Systems of record → data platforms → catalog/governance layer → consumers/AI For example, Salesforce may remain the system of record for customers, Snowflake/Databricks may hold analytical data, and the catalog tells everyone:

What is this data? Who owns it? What does “customer” mean? Where did it come from? Can I use it? How trustworthy is it? What policies apply? What breaks if I change it?

That's a much more sustainable enterprise architecture.

My recommendation

If I were running your vendor selection, I'd shortlist three:

1. Collibra — governance-first benchmark.
2. Alation — adoption/discovery benchmark.
3. Purview or Atlan, depending on whether you're Microsoft-centric or a modern multi-cloud stack.

Then run a 4–6 week proof of value using your real data, not a vendor demo. Test roughly 100–500 representative assets and score:

• Metadata ingestion coverage
• Column-level lineage accuracy
• Business glossary usability
• Ownership/stewardship workflows
• Data-quality integration
• Search/discovery experience
• API/event integration
• BI/warehouse/dbt/ETL coverage
• Policy/access integration
• AI/agent context
• Time required for ongoing stewardship
• 3-year TCO

The biggest mistake is choosing based on the catalog UI. Your connector coverage, lineage accuracy, governance operating model, and ability to keep metadata current will matter much more.

If you tell me your data stack (e.g. Snowflake + Databricks + AWS/Azure/GCP + dbt + Power BI/Tableau + Salesforce) and approximate company size, I can narrow this to a top 2–3 recommendation and give you a weighted vendor scorecard/RFP.

I wouldn't literally make the catalog your single source of truth for the data itself.

Instead, make it the single source of truth about your data:

Systems of record → data platforms → catalog/governance layer → consumers/AI For example, Salesforce may remain the system of record for customers, Snowflake/Databricks may hold analytical data, and the catalog tells everyone:

What is this data? Who owns it? What does “customer” mean? Where did it come from? Can I use it? How trustworthy is it? What policies apply? What breaks if I change it?

That's a much more sustainable enterprise architecture.

My recommendation

If I were running your vendor selection, I'd shortlist three:

1. Collibra — governance-first benchmark.
2. Alation — adoption/discovery benchmark.
3. Purview or Atlan, depending on whether you're Microsoft-centric or a modern multi-cloud stack.

Then run a 4–6 week proof of value using your real data, not a vendor demo. Test roughly 100–500 representative assets and score:

• Metadata ingestion coverage
• Column-level lineage accuracy
• Business glossary usability
• Ownership/stewardship workflows
• Data-quality integration
• Search/discovery experience
• API/event integration
• BI/warehouse/dbt/ETL coverage
• Policy/access integration
• AI/agent context
• Time required for ongoing stewardship
• 3-year TCO

The biggest mistake is choosing based on the catalog UI. Your connector coverage, lineage accuracy, governance operating model, and ability to keep metadata current will matter much more.

There is no single "best" platform for everyone, because the right choice depends heavily on your tech stack, organization size , and whether your primary pain point is data discovery, regulatory compliance , or AI readiness.

The enterprise data catalog and governance landscape breaks down into clear category leaders recognized across recent industry evaluations like the Gartner Magic Quadrant for Data and Analytics Governance Platforms:

1. The Heavyweight Enterprise Standard

• Collibra (Collibra Data Intelligence Cloud): The gold standard for large, heavily regulated enterprises with a dedicated Chief Data Officer or formal data stewardship teams. It features the most robust workflow customization, policy management, and business glossary capabilities on the market, and integrates deeply with modern engines like Databricks.
• Best for: Complex, cross-functional organizations where strict regulatory compliance, policy enforcement, and ownership mapping matter more than a quick plug-and-play setup.

2. The Agile & AI-Native Choice

• Atlan (Atlan Data and Analytics Governance): Built with a modern, consumer-grade user experience that treats metadata as active and collaborative. Atlan has emerged as a top-tier leader by focusing heavily on AI readiness, deep automation, and seamless integration across modern data stacks (Snowflake, dbt, AWS, etc.).
• Best for: Fast-moving, cloud-first data teams that want high user adoption and automated, AI-driven asset discovery without navigating a clunky legacy interface.

3. The Analytics-First & Crowdsourced Approach

• Alation (Alation Intelligence Operating System): Known historically for pioneering user-friendly data catalogs, Alation excels at crowdsourced documentation—letting the actual people using the data tag, rate, and comment on assets. They have evolved into a broader intelligence and AI governance layer.
• Best for: Organizations focused heavily on data democratization, analytics adoption, and helping business analysts quickly find and trust BI dashboards and raw data.

4. Deep Legacy & Multi-Cloud Infrastructure

• Informatica (Informatica CDGC): A titan in enterprise data management, offering cloud-native data governance and cataloging (CDGC) tightly coupled with elite data quality, master data management (MDM), and integration tools.
• Best for: Enterprises already embedded in the Informatica ecosystem or those handling massive, hybrid multi-cloud transformations requiring deep data quality remediation alongside cataloging.

To help narrow down which platform fits your environment, tell me:

• What is your primary cloud or data warehouse infrastructure (e.g., Snowflake, Databricks, AWS, Google Cloud, Microsoft Azure)?
• Is your main goal business self-service discovery , or compliance and audit readiness?

If your goal is a single source of truth for enterprise business data, the best platform depends more on your technology stack and governance maturity than on feature checklists. The leading enterprise platforms today are:

My recommendations by scenario

🏆 Best overall enterprise governance: Collibra

Choose this if:

• Governance is a strategic initiative
• You have dedicated data stewards
• Compliance and auditability matter
• Multiple business units need standardized terminology

Collibra remains the benchmark for enterprise governance and stewardship programs.

---

🚀 Best modern platform: Atlan

Choose this if:

• You're building a modern data platform
• You use Snowflake, Databricks, dbt, Tableau, or Looker
• Fast adoption is more important than heavy governance
• AI readiness is a priority

Many organizations report significantly faster user adoption because the interface is designed for analysts as well as governance teams.

---

💰 Best value for Microsoft shops: Microsoft Purview

If you're already invested in:

• Azure
• Microsoft Fabric
• Power BI
• Microsoft 365

Purview often delivers the best return because governance integrates directly into your existing Microsoft ecosystem.

---

🔓 Best open-source: DataHub

If you have a strong platform engineering team and want maximum flexibility, DataHub has become the leading open-source metadata platform with broad community support.

What separates great implementations from failed ones

The most successful programs focus on more than just technology. They establish:

• A business glossary with agreed-upon definitions (e.g., "Customer," "Revenue")
• Clear ownership for every critical data asset
• Automated lineage
• Data quality monitoring
• Governance workflows that fit how people actually work

Industry guidance consistently emphasizes that the platform supports governance—it doesn't replace the need for data owners, stewards, and governance processes.

If I were starting from scratch today

• Modern cloud enterprise: Atlan
• Fortune 500 / regulated enterprise: Collibra
• Microsoft-centric organization: Microsoft Purview
• Engineering-first company: DataHub
• Need governance + MDM + data quality together: Informatica

If you can share your environment—for example, whether you're primarily on Azure, AWS, or Google Cloud; using Snowflake, Databricks, or BigQuery; and whether your organization is in a regulated industry—I can narrow this to the two or three platforms that are the strongest fit.

There is no single "absolute best" platform because the right choice depends heavily on your existing data stack, team technical literacy, and compliance requirements . However, four enterprise platforms lead the market, each excelling in specific environments to help you build your single source of truth.

📊 The Top 4 Enterprise Data Catalogs Compared

💡 Strategic Selection Blueprint

To pinpoint which platform will actually achieve your "single source of truth" goal, evaluate your organization against these three pillars:

• Your Technology Stack : If your data sits entirely in a modern cloud data warehouse, Atlan provides the smoothest out-of-the-box integration. If you are dealing with a complex mix of legacy on-premise databases, ERPs, and cloud lakes, Informatica or Collibra are safer choices for deep metadata extraction.
• Company Culture : If your goal is to get non-technical business units to easily find, trust, and use data safely, Alation wins on user adoption and collaboration. If your core pain point is enforcing strict data access controls and passing legal audits, Collibra provides tighter guardrails.
• Implementation Resource : Do not underestimate the human cost. Platforms like Collibra require dedicated data stewards and platform admins to write and maintain governance workflows. If you lack a dedicated data governance team, look toward AI-driven, automated platforms like Atlan.

🛠️ Execution Plan: Moving Toward a Single Source of Truth

Achieving a single source of truth is 20% software and 80% process. Avoid buying a tool before setting up the architectural foundation.

Phase 1: Audit (Weeks 1-4)      Phase 2: Framework (Weeks 5-8)    Phase 3: Integration (Weeks 9+)
Map critical sources & silos -> Define ownership & glossaries  -> Connect catalog & automate lineage

1. Phase 1: Audit and Map : Before buying a tool, document your core data environments. Identify which database holds the true "golden record" for core entities like Customers, Revenue, and Products.
2. Phase 2: Establish Stewardship : Define your data governance policies. Assign data owners to specific domains (e.g., the Finance team owns the definition of "Net Revenue") so that contradictory definitions don't corrupt your catalog.
3. Phase 3: Programmatic Integration : Roll out your chosen catalog tool. Use its automated lineage features to track data from the ingestion pipeline, through your transformation layer (like dbt), and straight to the BI dashboards.

To help narrow down the absolute best platform for your specific business, tell me a bit more about your situation:

• What does your current data stack look like? (e.g., Snowflake, SQL Server, AWS, Salesforce?)
• Who will be the primary users of this platform? (Data engineers, business analysts, or compliance officers?)
• What is the primary driver for this project? (Data discovery/analytics or regulatory compliance/privacy?)

There is no single "absolute best" platform because the right choice depends heavily on your existing data stack, team technical literacy, and compliance requirements . However, four enterprise platforms lead the market, each excelling in specific environments to help you build your single source of truth.

📊 The Top 4 Enterprise Data Catalogs Compared

💡 Strategic Selection Blueprint

To pinpoint which platform will actually achieve your "single source of truth" goal, evaluate your organization against these three pillars:

• Your Technology Stack : If your data sits entirely in a modern cloud data warehouse, Atlan provides the smoothest out-of-the-box integration. If you are dealing with a complex mix of legacy on-premise databases, ERPs, and cloud lakes, Informatica or Collibra are safer choices for deep metadata extraction.
• Company Culture : If your goal is to get non-technical business units to easily find, trust, and use data safely, Alation wins on user adoption and collaboration. If your core pain point is enforcing strict data access controls and passing legal audits, Collibra provides tighter guardrails.
• Implementation Resource : Do not underestimate the human cost. Platforms like Collibra require dedicated data stewards and platform admins to write and maintain governance workflows. If you lack a dedicated data governance team, look toward AI-driven, automated platforms like Atlan.

🛠️ Execution Plan: Moving Toward a Single Source of Truth

Achieving a single source of truth is 20% software and 80% process. Avoid buying a tool before setting up the architectural foundation.

Phase 1: Audit (Weeks 1-4)      Phase 2: Framework (Weeks 5-8)    Phase 3: Integration (Weeks 9+)
Map critical sources & silos -> Define ownership & glossaries  -> Connect catalog & automate lineage

1. Phase 1: Audit and Map : Before buying a tool, document your core data environments. Identify which database holds the true "golden record" for core entities like Customers, Revenue, and Products.
2. Phase 2: Establish Stewardship : Define your data governance policies. Assign data owners to specific domains (e.g., the Finance team owns the definition of "Net Revenue") so that contradictory definitions don't corrupt your catalog.
3. Phase 3: Programmatic Integration : Roll out your chosen catalog tool. Use its automated lineage features to track data from the ingestion pipeline, through your transformation layer (like dbt), and straight to the BI dashboards.

To help narrow down the absolute best platform for your specific business, tell me a bit more about your situation:

• What does your current data stack look like? (e.g., Snowflake, SQL Server, AWS, Salesforce?)
• Who will be the primary users of this platform? (Data engineers, business analysts, or compliance officers?)
• What is the primary driver for this project? (Data discovery/analytics or regulatory compliance/privacy?)

If your goal is a single source of truth for enterprise data, I’d shortlist Collibra, Alation, and Microsoft Purview—but my default recommendation in 2026 is Collibra unless your organization is heavily standardized on Microsoft.

My recommendation

A current 2026 enterprise buyer's guide similarly puts Collibra, Alation, Informatica, and Purview among the major enterprise platforms, with the market increasingly moving from passive catalogs toward active metadata, lineage, policy automation, and AI governance.

Why I'd pick Collibra

For a true enterprise "source of truth," the important thing isn't just cataloging tables. You want a governed semantic layer connecting:

Business concepts → data owners → definitions → systems → datasets → lineage → quality → policies → usage

Collibra is particularly compelling if you have multiple clouds, databases, BI tools, SaaS applications, and business units and don't want your governance platform tied too closely to one infrastructure vendor.

When I'd choose Purview instead

If you're already substantially invested in Azure + Microsoft Fabric + Power BI + Microsoft 365, I'd seriously consider Microsoft Purview first.

Microsoft's current architecture separates the technical Data Map from the business-facing Unified Catalog. The latter supports governance domains, data products, glossary concepts, lineage, data quality, access workflows, and role-based governance.

That's a particularly attractive proposition because Purview is increasingly positioned as a broader data security + governance + compliance platform rather than merely a catalog.

One important correction to the "single source of truth" idea

I wouldn't make the catalog the place where the actual business data lives.

Instead:

Your operational/data platforms remain the systems of record; the catalog becomes the authoritative source of truth about the data.

For example:

CRM → Customer data
ERP → Financial data
Warehouse/lakehouse → Analytical data
Catalog/governance platform → What the data means, who owns it, where it came from, whether it's trusted, how it can be used, and how it relates to everything else

That's also how Microsoft describes Purview: the Data Map and Unified Catalog contain metadata, not the underlying data.

What I'd put in your RFP

Don't evaluate vendors primarily on "catalog search." Score them on:

1. Automated metadata ingestion across your actual stack
2. End-to-end lineage
3. Business glossary / semantic definitions
4. Data ownership & stewardship workflows
5. Data quality
6. Policy and access governance
7. Sensitive-data classification
8. Data-product management
9. API/integration capabilities
10. AI governance / AI-ready metadata
11. Business-user adoption
12. Total cost and implementation effort

And most importantly, require vendors to demonstrate these against your own data sources, not a polished demo environment.

Bottom line:

• Best neutral enterprise choice: Collibra
• Best Microsoft-centric choice: Microsoft Purview
• Best adoption/discovery experience: Alation
• Best if governance + broader data management are inseparable: Informatica
• Best modern/cloud-native contender: Atlan

If you tell me your data stack (e.g., Snowflake/Databricks/Azure/AWS, Power BI/Tableau, Salesforce, SAP, etc.), company size, and whether you need MDM, I can narrow this to a top 2–3 and give you a concrete architecture + RFP scorecard.

There is no single "best" platform for everyone, because the right choice depends heavily on your existing tech stack, organizational size, and governance maturity . However, the enterprise data catalog and governance market is led by a few standout platforms, each optimized for different needs.

Top enterprise platforms are categorized below by their primary strengths:

• Collibra — Best for Governance-First & Heavily Regulated Enterprises
  • Standout feature: Comprehensive, highly configurable workflows for data stewardship, policy enforcement, and compliance management.
  • Ideal for: Large enterprises (banking, healthcare, insurance) that treat governance as a strict compliance and risk-mitigation framework and have the dedicated technical resources for a longer implementation cycle.
• Atlan — Best for Modern Cloud Data Stacks & High User Adoption
  • Standout feature: Active metadata architecture with deep, bi-directional integrations into modern tools like Snowflake, Databricks, dbt, and Airflow.
  • Ideal for: Organizations looking for rapid deployment, a consumer-grade UI that non-technical business users will actually log into, and built-in AI/agentic governance.
• Informatica — Best for End-to-End Enterprise Data Management
  • Standout feature: Cloud Data Governance and Catalog (CDGC) tightly integrated with master data management (MDM) and enterprise data integration.
  • Ideal for: Large legacy or hybrid enterprises already heavily invested in the Informatica ecosystem that need robust data quality, lineage, and cataloging under a single vendor.
• Alation — Best for Collaborative Data Intelligence & Discovery
  • Standout feature: Behavioral analytics and crowdsourced documentation that capture how people actually use data.
  • Ideal for: Companies prioritizing data literacy and self-service analytics where community curation and search usability are paramount.
• Microsoft Purview — Best for Microsoft-Centric Ecosystems
  • Standout feature: Native Azure integration and seamless scanning across Microsoft 365, Power BI, and multi-cloud data estates.
  • Ideal for: Organizations already operating primarily within the Microsoft security and Azure cloud stack.

To help narrow down the ideal platform, tell me:

• What is your primary cloud data warehouse or lakehouse (e.g., Snowflake, Databricks, BigQuery)?
• Are you aiming primarily for agile data discovery for business users or strict regulatory compliance?

If by “single source of truth” you mean one governed place where people can discover what data exists, what it means, who owns it, where it came from, whether it’s trustworthy, and how it can be used, I’d shortlist Collibra, Alation, Microsoft Purview, and Atlan.

My recommendation

For a large, heterogeneous enterprise: start with Collibra.

Collibra is probably the strongest choice when governance itself is a strategic program—not merely a catalog. It is designed around business glossaries, stewardship, policies, ownership, lineage, data quality, and governance workflows, and Gartner Peer Insights currently lists it among the leading data-and-analytics governance platforms.

But I wouldn't automatically buy it. The best choice depends heavily on your architecture:

Microsoft has been expanding Purview substantially: its current governance experience combines Data Map and Unified Catalog, with discovery, classification, governance domains, data products, business concepts, and data-quality workflows.

The key distinction

Don't think of the catalog as the database where all your business data gets copied.

Instead:

Systems of record → governed metadata layer → consumers

Your ERP, CRM, warehouses, lakehouses, SaaS applications, BI platforms, etc. remain the actual systems containing data. The catalog becomes the authoritative metadata and governance layer:

• What is this data?
• What does “customer” mean?
• Who owns it?
• Which system is authoritative?
• Where did it come from?
• What reports/models depend on it?
• Is it sensitive?
• Can I use it for this purpose?
• How trustworthy is it?
• What is the approved version of this metric?

That's much more valuable than simply having a searchable inventory.

What I'd evaluate in an RFP

I'd weight the evaluation roughly:

1. Business glossary & semantic definitions — 20%
2. Automated technical/column-level lineage — 15%
3. Stewardship & governance workflows — 15%
4. Connectors/metadata ingestion — 15%
5. Data quality & trust indicators — 10%
6. Business-user discovery/search — 10%
7. Security, policy & compliance — 5%
8. AI/agent readiness — 5%
9. Administration/TCO — 5%

And make vendors demonstrate—not merely claim—how they'd answer:

“Show me every authoritative source for Customer, the definition approved by Finance, the owner, all downstream dashboards, its lineage back to source systems, its data-quality score, its PII classification, and which datasets an analyst is permitted to use.”

If a platform can't answer that reliably from your real environment, it's not your single source of truth.

My shortlist

If I were running the selection, I'd do a 4-way POC: Collibra vs. Alation vs. Purview vs. Atlan. Current industry comparisons likewise put these platforms among the major enterprise choices.

Default decision tree:

• Mostly Microsoft/Azure/Fabric/Power BI → Purview
• Highly heterogeneous enterprise + formal governance program → Collibra
• Business adoption/discovery is the #1 priority → Alation
• Modern cloud-native stack + data products/AI → Atlan
• Already heavily invested in Informatica → Informatica
• Almost everything runs on Databricks → Unity Catalog, potentially paired with an enterprise catalog

One important caveat: the platform will not create the single source of truth by itself. Governance is an operating model—owners, stewards, definitions, policies, workflows, and incentives—not just software.

If you tell me your data stack (Snowflake/Databricks/Azure/AWS/GCP/SAP/Salesforce/etc.), company size, and whether the primary goal is analytics, compliance, AI, or all three, I can narrow this to a specific recommendation and architecture.