What is Data Catalog?
A Data Catalog is an organised inventory of an organisation's data assets, enriched with metadata such as descriptions, ownership, quality scores, and usage statistics. It enables data consumers to discover, understand, and trust available data without relying on tribal knowledge.
What is a Data Catalog?
A Data Catalog is a centralised, searchable inventory of all the data assets within an organisation. It functions like a library catalogue for data — listing what datasets exist, where they are stored, what they contain, who owns them, how fresh they are, and how they have been used. Instead of asking colleagues or searching through databases manually, data consumers can browse or search the catalogue to find the data they need.
At its core, a Data Catalog solves a deceptively simple problem: people in your organisation cannot use data they cannot find. As companies grow and accumulate data across dozens of systems — CRMs, ERPs, data warehouses, cloud storage, SaaS applications — the challenge of simply knowing what data exists and whether it is trustworthy becomes significant.
How a Data Catalog Works
A Data Catalog typically provides the following capabilities:
1. Automated discovery and ingestion
The catalogue connects to data sources across the organisation and automatically scans them to identify tables, columns, files, and other data assets. This discovery process runs continuously or on a schedule, ensuring the catalogue stays up to date as new data is created.
2. Metadata management
For each data asset, the catalogue stores both technical metadata (data types, schemas, storage locations) and business metadata (descriptions, definitions, ownership, tags, and classifications). Business metadata is often added by data stewards or domain experts and is what makes the catalogue useful to non-technical users.
3. Search and discovery
Users can search for data using natural language queries, browse by category or domain, or explore related datasets through recommendations. Modern catalogues use AI to suggest relevant datasets based on a user's role, past queries, and current project.
4. Data profiling and quality indicators
The catalogue automatically profiles datasets to show statistics like row counts, null percentages, value distributions, and freshness. This helps users assess whether a dataset is suitable for their purpose before they invest time working with it.
5. Collaboration and documentation
Users can annotate datasets with notes, reviews, and usage examples. This builds institutional knowledge about data assets and reduces dependence on individual experts who happen to know where specific data lives.
6. Access management
The catalogue integrates with access control systems to show users which datasets they have permission to use and provides workflows for requesting access to restricted data.
Why Data Catalogs Matter
Without a Data Catalog, organisations face several recurring problems:
- Duplicate efforts: Different teams independently build datasets that already exist elsewhere in the organisation, wasting engineering time and creating inconsistencies.
- Tribal knowledge dependency: Only a few senior employees know where critical data lives and what it means. When they leave, that knowledge leaves with them.
- Low data trust: Business users are unsure whether a dataset is current, complete, or accurate, so they either avoid using data or make decisions based on unverified information.
- Slow onboarding: New data team members spend weeks or months learning the data landscape through conversations and trial and error rather than through a structured catalogue.
Data Catalogs in the Southeast Asian Context
For companies operating across Southeast Asia, Data Catalogs address region-specific challenges:
- Multi-market complexity: A company operating in six ASEAN markets may have customer data in different formats, languages, and systems across each country. A catalogue makes it possible to find and understand data from any market from a single interface.
- Growing data teams: As data teams expand in the region, a catalogue prevents the knowledge gaps that occur when new analysts and engineers join without institutional context.
- Regulatory navigation: With different data protection regulations across ASEAN, a catalogue that classifies data by sensitivity level helps teams quickly identify which datasets contain personal information subject to specific regulatory requirements.
- Partner ecosystems: Southeast Asian businesses frequently work with distributors, marketplace platforms, and logistics providers that share data. A catalogue tracks these external data sources alongside internal ones.
Leading Data Catalog Platforms
Several platforms serve different needs and budgets:
- Atlan: A modern, collaborative data workspace popular with mid-market companies. Strong user experience and integration with modern data tools.
- Collibra: An enterprise-grade data governance platform with comprehensive cataloguing. Common in regulated industries like financial services.
- Alation: Known for intelligent search and machine learning-driven recommendations. Strong adoption among large enterprises.
- DataHub (open-source): Originally developed by LinkedIn, DataHub provides metadata management and cataloguing without licensing costs.
- Apache Atlas (open-source): A governance and metadata framework commonly used in Hadoop-based environments.
- Cloud-native options: Google Cloud Data Catalog, AWS Glue Data Catalog, and Azure Purview provide cataloguing within their respective cloud ecosystems.
Getting Started with a Data Catalog
A practical implementation roadmap:
- Inventory your data sources. List all databases, data warehouses, cloud storage, SaaS applications, and file systems that contain business data.
- Start with high-value datasets. Catalogue the datasets that your leadership and analytics teams use most frequently before expanding to less critical sources.
- Assign data owners. Every dataset in the catalogue should have a designated owner responsible for its documentation and quality.
- Automate where possible. Use automated discovery and profiling to reduce manual effort and keep the catalogue current.
- Encourage adoption. Make the catalogue the default starting point for any data project. Measure adoption through search queries, page views, and user feedback.
A Data Catalog directly impacts your organisation's ability to make data-driven decisions quickly and confidently. For CEOs, it means that teams spend less time searching for data and more time analysing it, leading to faster insights and better decisions. For CTOs, it reduces duplicate data engineering work, improves data governance, and accelerates the onboarding of new team members.
The cost of not having a catalogue grows with your organisation. Every time an analyst spends hours searching for a dataset, every time two teams build the same pipeline independently, and every time a decision is delayed because no one can verify a number — these are the hidden costs of poor data discoverability.
In Southeast Asia's competitive markets, where speed and agility are critical advantages, the ability to quickly find, trust, and use data across multiple markets and business units is a meaningful operational edge. Companies investing in AI and advanced analytics especially benefit because these initiatives are only as effective as the data that feeds them, and a catalogue ensures that the best available data is discoverable and accessible to every project that needs it.
- A Data Catalog is only valuable if people use it. Prioritise user experience and integrate the catalogue into existing workflows rather than creating a separate tool that teams must remember to check.
- Automated discovery reduces the burden of keeping the catalogue current, but business context — descriptions, definitions, and ownership — must be added by people who understand the data.
- Start with your most-used datasets and expand coverage incrementally. Trying to catalogue everything at once leads to low-quality metadata and user frustration.
- Assign clear data ownership for every catalogued asset. A dataset without an owner will quickly become outdated and untrustworthy.
- Integrate your catalogue with data lineage and quality monitoring tools to provide a complete picture of each dataset, not just its location and description.
- Evaluate whether a cloud-native catalogue from your existing provider is sufficient or whether a dedicated platform offers meaningfully better capabilities for your needs.
Frequently Asked Questions
How is a Data Catalog different from a data dictionary?
A data dictionary is a static document or database that defines table structures, column names, data types, and business definitions. A Data Catalog is a dynamic, searchable platform that goes beyond definitions to include data lineage, quality metrics, usage statistics, ownership, access controls, and collaboration features. Think of a data dictionary as a reference document and a Data Catalog as a living, interactive platform. Most modern Data Catalogs incorporate data dictionary functionality as one component among many.
What is the ROI of implementing a Data Catalog?
Studies from Forrester and Gartner suggest that organisations with effective Data Catalogs see 20 to 40 percent reduction in time spent searching for and preparing data. For a data team of ten people, this can translate to two to four additional productive team members worth of output annually without hiring. Additional ROI comes from reduced duplicate work, faster regulatory compliance responses, and improved data quality. The catalogue typically pays for itself within 12 to 18 months through these efficiency gains.
More Questions
Yes. Most modern Data Catalog platforms support hybrid environments and can connect to cloud databases, on-premises systems, SaaS applications, and file storage simultaneously. This is particularly important in Southeast Asia where many organisations run a mix of legacy on-premises systems and newer cloud infrastructure. When evaluating catalogue platforms, verify that they offer connectors for all your key data sources, including any region-specific or industry-specific systems you rely on.
Need help implementing Data Catalog?
Pertama Partners helps businesses across Southeast Asia adopt AI strategically. Let's discuss how data catalog fits into your AI roadmap.