A data catalog is a centralized inventory that organizes, describes, and tracks all data assets across an organization. It provides metadata management, data lineage, search capabilities, and governance controls so teams can find, understand, and trust their data before using it for analysis or AI applications.
Before AI can reason over business data, organizations must first understand what data they have and where it lives. Metadata and data catalog platforms deliver four core capabilities:
Without this foundation, AI systems inherit confusion, inconsistency, and compliance risk.
What it is: A modern data workspace that combines cataloging, governance, and team collaboration in one interface.
Best for: Mid-market and enterprise data teams that want usability without sacrificing governance.
Key differentiator: The "human layer" approach treats data as a collaborative asset, not just a technical one.
What it is: An enterprise-grade platform built for governance, policy management, and compliance automation.
Best for: Large enterprises in regulated industries like banking, insurance, and telecommunications.
Key differentiator: Deep compliance workflows that map directly to regulatory requirements like GDPR, CCPA, and Basel III.
What it is: A data catalog with strong business glossary and stewardship capabilities.
Best for: Large and upper mid-market organizations that need both technical metadata and business context.
Key differentiator: Search-first design that helps business users find and understand data without SQL knowledge.
What it is: An open-source metadata platform originally built at LinkedIn for internal use.
Best for: Engineering-heavy organizations comfortable with self-hosting and customization.
Key differentiator: Extensible architecture with a growing ecosystem of integrations and plugins.
What it is: An open-source data discovery platform created at Lyft.
Best for: Early-stage data teams and organizations adopting open-source-first strategies.
Key differentiator: Lightweight and fast to deploy, though governance features remain basic.
What it is: A catalog offering from Informatica, integrated with their ETL and MDM products.
Best for: Large enterprises already invested in the Informatica ecosystem.
Key differentiator: Tight integration with Informatica's data integration and quality tools.
What it is: A metadata and lineage platform that connects directly to Talend's ETL pipelines.
Best for: Organizations using Talend for data movement and transformation.
Key differentiator: Automatic lineage capture from Talend jobs without manual documentation.
What it is: IBM's governance and metadata solution, often paired with master data management.
Best for: IBM-heavy enterprises in regulated industries.
Key differentiator: Combines metadata, master data, and governance in a single platform.
What it is: A modern catalog that uses AI to generate documentation and enable natural language search.
Best for: Mid-market data teams that want strong UX without enterprise complexity.
Key differentiator: Chat-based interface that lets users ask questions about their data in plain English.
What it is: A lightweight catalog with AI-powered auto-documentation and search.
Best for: Startups and mid-market companies that need speed over depth.
Key differentiator: Fast setup and analyst-friendly interface with built-in AI assistance.
What it is: An open-source metadata and governance framework for Hadoop ecosystems.
Best for: Organizations running big data workloads on Hadoop, HBase, or Hive.
Key differentiator: Strong policy and classification features, though the UI lags behind modern alternatives.
What it is: Newer entrants offering automated lineage, collaboration, and governance for cloud-native stacks.
Best for: Modern data platforms built on Snowflake, Databricks, or BigQuery.
Key differentiator: Purpose-built for cloud-native architectures with strong lineage automation.
Five factors to consider:
Data catalogs answer one question: What data exists?
Decision intelligence platforms answer a different question: What does this data mean for my business decision?
Catalogs provide the foundation. Ontology mapping, knowledge graphs, and reasoning engines sit one layer above, connecting data definitions to business context. Organizations that want AI to reason over their data, not just retrieve it, need both layers working together.
DecisionX builds on top of data catalogs, BI tools, and spreadsheets. It does not replace metadata platforms. It consumes their outputs and adds a reasoning layer.
What catalogs do: Define structure, ownership, and lineage.
What GREEN adds: Connects those definitions to business questions and explains why metrics changed.
Your data catalog tells you:
GREEN tells you:
The catalog documents what exists. GREEN explains what happened and what to do about it.
GREEN is built for teams that are already invested in catalogs and BI but still spend hours chasing answers across tools. If your analysts can find the data but struggle to explain the "why" behind performance changes, that gap is what GREEN closes.
During beta, 240 users across mid-market and enterprise companies tested the platform. The common pattern: teams had the data infrastructure in place but lacked a reasoning layer that connected metrics to decisions.
Data catalogs are infrastructure. They tell you what data exists, who owns it, and where it came from. Every modern data team needs one.
But catalogs stop at documentation. They don't explain why numbers changed or what to do next. That requires a reasoning layer built on top.
If your team can find data but struggles to explain performance changes, the gap isn't your catalog. It's what sits above it.
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Shaoli Paul, Product Marketing Manager, DecisionX
Shaoli Paul is a content and product marketing specialist with 4.5+ years of experience in B2B AI SaaS and fintech, working at the intersection of SEO, product messaging, and demand generation. She currently serves as Product Marketing Manager at DecisionX, leading the content and SEO strategy for its decision intelligence platform. Previously, she built global content strategies at Simetrik, Chargebee, and HighRadius, driving strong growth in organic visibility and lead conversion. Shaoli’s work focuses on making complex technology understandable, actionable, and human.
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