Implementing a data catalog is one of the most important steps you can take to enable trustworthy, self-serve data access across your company. This guide offers a practical, step-by-step approach to help you build a catalog that people actually use. At the end, I’ll cover common challenges teams run into and share how platforms like Secoda can help you skip the heavy lifting.

Whether you are dealing with decentralized data, unclear ownership, or a lack of documentation, a well-implemented catalog can be a game-changer for collaboration, transparency, and faster decision-making.

Step 1: Outline your business and technical requirements

Before building anything, it’s important to get clear on why you’re creating a data catalog in the first place. The best implementations start with alignment between business goals and technical capabilities.

For most teams, a data catalog isn’t just about creating an inventory, but about reducing friction. That could mean fewer repetitive questions, better documentation, easier onboarding, or unlocking true self-serve analytics. The use cases will vary, but your catalog should ultimately help your team find, trust, and use data faster.

Here are some common goals we see from teams starting this journey:

• Enabling self-service access to key data assets across the org
• Standardizing metrics and definitions between teams (especially Finance, Marketing, and Product)
• Improving data quality and surfacing stale or broken tables, like the team at Homebot
• Increasing visibility into who owns what, and how data is being used
• Supporting compliance requirements with access controls and audit trails

From a technical standpoint, you’ll also want to consider:

• Which tools in your data stack need to be integrated (e.g., Snowflake, dbt, Tableau, Airflow)
• Who needs access to what (data engineers vs analysts vs business users)
• How frequently metadata needs to be refreshed and monitored
• What governance controls (e.g., approvals, sensitive tags, or role-based access) should be in place

💡 Secoda tip: If you’re not sure where to start, talk to your internal users. Ask them what slows them down when working with data today. These pain points often reveal your most urgent catalog use cases.

And while it’s tempting to focus only on the technical checklist, don’t forget: adoption is the goal. Your catalog should serve the team, not just inventory the stack.

Step 2: Audit your data stack and sources

Once your goals are clear, the next step is to understand the lay of the land. That means identifying where your data lives, how it flows, and which systems should be included in the catalog.

Start by listing all the tools in your modern data stack which can often include:

• Warehouses like Snowflake, BigQuery, or Redshift
• Transformation tools like dbt or Coalesce
• Orchestration tools like Airflow or Dagster
• BI platforms like Looker, Tableau, or Sigma
• Data lakes, APIs, spreadsheets, or even Confluence docs

It’s important to think beyond just databases. A truly useful catalog should unify metadata across your entire stack, including downstream assets like dashboards, scheduled jobs, and business definitions.

To get this list right, teams often start with one (or a mix) of the following approaches:

• Spreadsheets: Quick and manual, but great for early-stage audits or when you don’t have tooling set up yet.
• Discovery interviews: Talk to key data producers and consumers across teams. Ask what tools they rely on and where friction exists.
• Lineage tools: If you have access to lineage or observability tooling, use it to surface upstream/downstream dependencies automatically.

Not all data sources are equal, either. Prioritize based on:

• How often a data source is used
• How many downstream assets rely on it
• How much tribal knowledge exists about that data
• Whether it contains sensitive or critical business information

🛠️ Secoda tip: With native integrations across the modern data stack, Secoda automatically ingests metadata from your core tools, saving you the manual effort of stitching it all together. Bonus: you can also see usage patterns to help you focus on the highest-impact assets first.

This audit doesn’t have to be perfect. Even a rough map of your tools and sources will help you decide what needs to be included in your catalog MVP, versus what can wait.

Step 3: Plan your architecture

With your sources identified, it is time to think about how your catalog will technically come together. Even if you are not building your own tool from scratch, having a broad understanding of how metadata flows through your system is key.

At a high level, most data catalog architectures include three layers:

• Source layer: This is where your metadata originates, whether that’s in warehouses, data lakes, pipelines, and more. You'll want to connect to each using native integrations, SDKs, or APIs depending on what's available.
  
  
• Metadata processing layer: This is your backend. It typically includes:
  
  
  • A relational database (like Postgres or MySQL) to store metadata in a consistent structure
    
  • A search engine (like Elasticsearch) to support full-text search across assets
    
  • A graph database (like Neo4j) to model relationships between data entities for lineage and impact analysis
    
    
• Presentation layer: The frontend that makes metadata accessible to users via search, lineage maps, glossaries, and embedded tools like Slack or dashboards

At a basic level, your catalog should be able to:

• Ingest metadata from various tools and sources
• Store it in a way that enables fast search and discovery
• Visualize relationships between assets, like lineage and ownership
• Manage access based on roles, sensitivity, or domains
• Surface this information to users in the tools they already use

Behind the scenes, these capabilities are usually powered by a few key components:

• A metadata store to capture information from all your sources
• A search engine to enable fast querying of tables, columns, and definitions
• A graph database to model how data assets connect and flow across systems
• An access control layer to enforce permissions and governance policies

If you are building your own catalog, you will likely need to assemble and maintain each of these components independently. That includes syncing metadata from each tool, managing dependencies, and ensuring the entire system remains performant and secure over time.

💡 Secoda tip: With Secoda, you get all of this out of the box. Metadata ingestion, full-text search, lineage mapping, and role-based access controls are already built in. That means your team can focus on using the catalog, not maintaining the backend infrastructure behind it.

Your architecture will shape how quickly you can scale, how easily users adopt the tool, and how future-proof your catalog becomes. Whether you are building or buying, getting this right early on will save you time and complexity later.

Step 4: Ingest metadata from your source systems

Once your architecture is mapped out, the next step is bringing data into the catalog. Metadata ingestion is the process of collecting context from your tools. This includes information like table names, column types, data lineage, freshness, owners, and usage metrics.

How you ingest metadata depends on your stack and the capabilities of your tools. Some platforms support pull-based ingestion, where you extract metadata on a schedule. Others use push-based methods, where metadata is sent to your catalog as changes occur. 

Most teams use a hybrid model, depending on what their tools support. Pulling works well for systems like warehouses or BI tools, while pushing may be better for pipelines and transformation jobs.

Handling different source types

Each type of data source requires a different approach to metadata extraction:

• Relational databases & warehouses (like Snowflake, Redshift, BigQuery): These expose metadata via internal schemas like information_schema. You can query for tables, columns, primary keys, permissions, and more.
  
• Data lakes (like S3, Delta Lake): These require schema inference from file formats like Parquet, JSON, or CSV. Libraries like PySpark or Apache Arrow can help extract structure from these files.
  
• APIs & SaaS tools (like Segment, Mixpanel, SurveyMonkey): These require handling less predictable schemas and tracking API contract changes. A good catalog will monitor for schema drift and alert you to changes.
  
• Cloud-native catalogs (like AWS Glue, GCP Data Catalog): These can act as intermediate sources of metadata, but often lack business context. You'll want to ingest their metadata and enrich it with ownership, usage, and documentation layers in your catalog.

Metadata ingestion isn’t just about technical schemas. It’s also about surfacing who uses what, when, and why, and making that data discoverable where teams already work.

Choosing the right stack (if building yourself)

If you’re taking a build-your-own approach, you’ll need to select technologies that map to the components above. Some common pairings include:

• Storage: PostgreSQL, MySQL, or DynamoDB
  
• Search: Elasticsearch
  
• Lineage modeling: Neo4j or JanusGraph
  
• Orchestration: Airflow or Prefect
  
• Frontend: React or other JS frameworks

Questions to ask for each integration

As you connect each data source, ask yourself:

• What metadata is available? (schemas, columns, joins, usage)
• How fresh is it, and how often should it sync?
• Does the tool provide native connectors or APIs?
• What load does metadata extraction put on the system?

Be mindful of performance. Querying metadata from large databases too frequently can strain production systems or trigger rate limits. It’s best to stagger syncs and run metadata jobs during low-traffic windows.

Another key consideration is schema standardization. Metadata often comes in different shapes depending on the source. By normalizing this metadata across tools, you can build a catalog that feels consistent which makes it easier to search, document, and govern data regardless of where it came from.

💡 Secoda tip: Secoda supports both push- and pull-based ingestion depending on the integration, and handles schema normalization automatically. Our native connectors sync metadata with minimal overhead and give teams confidence that their catalog stays up to date, without manual work or custom code.

The more metadata you can ingest and standardize early, the easier it becomes to automate documentation, surface insights, and build trust in the catalog as a single source of truth.

Step 5: Build a business glossary

A data catalog is only as helpful as the context it provides. One of the most important steps in making your catalog truly useful, especially for non-technical users, is building a business glossary.

A business glossary is a shared library of key terms, metrics, and definitions across your organization. It helps align teams on what terms like "active user" or "churn rate" actually mean, reducing misinterpretation and confusion across departments.

Start by identifying:

• Commonly used terms across departments
• Metrics that appear frequently in reports and dashboards
• Definitions that vary depending on the team or tool

Then work with domain experts and data owners to define each term clearly and concisely. Your glossary should include details like ownership, applicable data sources, and where the term is used, such as in dashboards or specific tables.

To keep it actionable:

• Link glossary terms directly to relevant assets in your catalog
• Add tags for departments, domains, or priority levels
• Review and update glossary entries regularly to avoid drift

💡 Secoda tip: In Secoda’s glossary, you can link definitions to tables, columns, dashboards, and even questions, helping users get full context wherever they are working.

A well-maintained business glossary acts as a translation layer across the company. It helps bridge the gap between data producers and consumers and is one of the fastest ways to build trust in your catalog.

Step 6: Implement governance and access policies

As your catalog starts to take shape, governance becomes critical. Metadata should be both organized and accessible, while still protecting sensitive resources.

This step involves setting up the policies and controls that keep your data secure, compliant, and trustworthy. That includes everything from role-based access to documenting sensitive data and applying approval workflows.

Start by defining:

• Who can view, edit, or manage different parts of the catalog
• Which datasets contain sensitive or restricted information
• How data should be tagged for compliance or regulatory purposes
• What audit logs or approval processes are required for changes

Access policies should reflect how your teams actually work. Some companies assign access by department, while others manage it by domain, project, or data sensitivity. It is also helpful to involve stakeholders from legal, compliance, or security early on to ensure nothing gets overlooked.

💡 Secoda tip: Governance is built into Secoda from day one. You can assign permissions at the workspace, domain, or asset level, tag sensitive data using integrations like Cyera, and track access and changes through audit logs. This helps teams stay compliant without slowing down access or collaboration.

Good governance gives you control without creating bottlenecks. It builds trust in your catalog, especially as adoption expands across the company. The earlier you implement clear access and tagging policies, the easier it becomes to scale responsibly.

Step 7: Launch a proof of concept

Before rolling out your catalog across the company, start with a focused proof of concept. This helps you test your setup, gather feedback, and demonstrate value to stakeholders early.

Choose one domain, team, or data source that is high-impact but manageable in scope. For example, you might start with Marketing dashboards, Finance reporting tables, or your core product analytics schema.

During the proof of concept, focus on:

• How easy it is for users to search and find relevant data
• Whether documentation and lineage are clear and useful
• How much time the catalog saves compared to asking teammates or digging into SQL
• What gaps users still encounter when trying to understand or trust the data

Collect feedback through short interviews, surveys, or usage analytics. Look for signs of friction, like confusion around naming conventions or missing context, and use those insights to iterate before expanding.

💡 Secoda tip: Secoda makes it easy to run a lightweight proof of concept by connecting just a few tools and immediately surfacing searchable metadata, auto-generated documentation, and lineage. Many teams see internal adoption within days just by linking Secoda to a single warehouse or dashboarding tool.

A successful proof of concept builds internal momentum. It shows leadership the value of investing in data governance and helps you secure buy-in for broader rollout.

Step 8: Prepare to scale and automate

Once your catalog is up and running, the next step is making it sustainable. That means putting systems in place to keep metadata fresh, automate repetitive tasks, and scale usage across the organization.

Start by identifying the areas that are the hardest to maintain manually. This usually includes:

• Tagging and classifying new data assets
• Keeping documentation up to date
• Assigning and tracking ownership
• Monitoring data quality and usage
• Alerting teams when schema or lineage changes

Automation plays a critical role here. Without it, most catalogs go stale within months. Automating metadata ingestion, freshness checks, glossary suggestions, and alerts can turn your catalog from a one-time project into a continuously improving product.

💡 Secoda tip: Automation is built into the core of Secoda. You can create rules that flag undocumented tables, assign owners based on domains, or trigger Slack alerts when key assets are updated. Secoda AI can suggest documentation based on usage and metadata patterns - saving your team hours of upkeep.

As your company grows, your catalog should grow with it. That includes expanding coverage to new teams and tools, introducing training or onboarding for new users, and continuously refining the way metadata is organized and governed.

Scaling does not mean doing more work. With the right automations and structure in place, your catalog becomes a self-sustaining asset that improves over time.

Common challenges when building your own catalog

Even with the right steps in place, building and maintaining a data catalog is rarely straightforward. Many teams start strong, only to run into roadblocks that limit adoption or create more maintenance than expected.

Here are some of the most common challenges we see:

• Keeping metadata fresh: Without automated syncs and alerts, catalogs quickly become outdated, which leads to loss of trust and declining usage
• Low adoption: If the catalog is hard to use or disconnected from daily workflows, most people will default back to Slack or asking around
• Manual upkeep: Tagging, documenting, and updating lineage manually becomes overwhelming as your data volume grows
• Lack of visibility: Without insight into how data is being used, or by whom, it is difficult to prioritize what matters
• Missing context: Even if metadata exists, it often lacks the business definitions or explanations needed for non-technical users to actually use it
• Siloed governance: Access, quality, and documentation are often managed in separate tools, making it hard to enforce policies consistently

These challenges are especially common when teams try to build their own catalog or stitch together open-source tools. While this can work in the short term, it often leads to more complexity, slower adoption, and limited impact.

A modern alternative: Why teams choose Secoda

If you are running into the challenges above, or want to avoid them entirely, you’re not alone. Many teams choose Secoda as a modern, AI-ready alternative to building their own catalog from scratch.

Secoda is more than a catalog. It’s an end-to-end data governance platform designed for modern teams. Documentation, discovery, lineage, observability, and access management are all included in one system that connects directly to your stack and works out of the box.

Here’s what you get out of the box with Secoda:

• Automated metadata ingestion across warehouses, BI tools, transformation layers, and orchestration platforms
  
  
• AI-powered documentation and smart recommendations that help reduce manual work
  
  
• Context-aware search that returns personalized results based on role and metadata
  
  
• Built-in data quality scores at the table and column level, with clear suggestions for improvement
  
  
• End-to-end lineage visualizations and impact analysis to identify downstream dependencies
  
  
• Role-based access controls and sensitive data tagging to manage privacy and compliance
  
  
• Alerts and automations through Slack, Teams, and email to keep everyone informed
  
  
• Adoption and usage insights to help improve documentation and search behavior over time
  
  
• No-code and SQL-based monitoring tools to track freshness, cardinality, and schema changes
  
  

Secoda supports organizations as they scale, whether they are onboarding a few analysts or managing hundreds of users. Everything stays connected, documented, and accessible in one place.

Trying to build your own solution will likely result in missing key features like AI-powered documentation, proactive quality scoring, or automation. These are quickly becoming the baseline. In an environment where data evolves daily, teams need systems that can adapt just as fast.

Final thoughts

Building a catalog can be one of the most impactful decisions a data team makes. When done right, it becomes more than a list of assets. It becomes the system that helps everyone across the organization trust data, move faster, and stay aligned.

But building a catalog on your own often creates challenges. Manual upkeep, inconsistent documentation, and disconnected governance workflows lead to unnecessary overhead. As organizations work toward becoming AI-ready, they need more than a metadata store. They need governance that fits directly into their existing tools and workflows.

In the past, governance was often a reactive process. It happened when compliance required it. That approach no longer works. Being AI-ready means having high-quality, well-documented, and accessible data. That is why governance is built into every part of the Secoda platform. From discovery to access management, everything is connected.

If you are ready to stop stitching together tools and start building a sustainable data foundation, we would love to show you how Secoda can help.

👉 Book a demo to see how teams like yours are scaling governance without the overhead.