Data Catalog: Best Practices and Tips for Implementation and Maintenance

So you’re going to build a data catalog (or maybe you already have one, but that’s just about it). What should you know to make the best use of it?

Here’s a comprehensive collection of data catalog best practices for implementation and management gathered from Murdio’s data governance team working daily with data catalogs, sometimes in complex enterprise environments.

A data catalog never works just on its own

We’ve already said that a data catalog is just the beginning of your data governance journey. And implementing it is far more than just a software development project.

Without adding processes and policies around it, structuring it in the right way to answer your business needs, or integrating it with your entire data management landscape, it’s not going to do much.

And we’ve seen underused data intelligence platforms. It’s always a shame because data intelligence platforms are quite the investment, frankly wasted if you can’t get maximum benefits out of it.

So, here are some best practices we’ve accumulated over the years working on dozens of Collibra projects involving data catalogs that we find particularly important.

Before we dive into specific best practices for data catalog implementation, management, and maintenance, here’s one overall piece of advice (and we’re not being cheeky here): Hire professionals who know how to work with the tool you’re using to build your catalog. If it’s Collibra, which we recommend, we can help tailor the data catalog to your company’s specific needs and data management software landscape.

Best practices for effective data catalog implementation

Introducing data intelligence tools in large companies usually takes quite a lot of preparation and planning before we can even talk about specific technical aspects of implementation. Here are some things to keep in mind.

Set the business scene first

Implementing a data catalog starts way before you actually set up the software. You first need to audit your data landscape and define business goals and specific use cases.

Do you need to comply with specific data privacy regulations?

Provide self-service analytics across teams to increase productivity?

Or improve the quality of data you feed to AI algorithms for more reliable outcomes?

Business considerations like this come way before technical ones – also because they impact the technical setup and tool integrations. This step also includes:

• Engaging business stakeholders early to define the catalog’s purpose and expected outcomes.
  Identifying key pain points that a data catalog can solve for your company and its employees. (For example, is the data unreliable at the moment, preventing people from effectively using it?)
• Setting measurable goals – for example, to what extent do you expect productivity using data to increase? Defining clear KPIs can help set very clear expectations and analyze performance in the future.

When it comes to KPIs, Collibra itself recommends three types of KPIs as most helpful to measure a data catalog’s success:

• Enablement KPIs, e.g., number of sources ingested and completeness of information
• Adoption KPIs, including unique logins or daily search queries
• Business-value metrics, such as the productivity increase we mentioned above, cost savings, shorter onboarding times, faster time to value for data projects, etc. The list of data catalog benefits is actually quite long.

Establish strong data ownership and governance

Yes, we’re not even close to the actual technical development at this point. When you have clear goals, you also need to know who’s responsible. That’s because when there’s no clear ownership, metadata can quickly become outdated or inconsistent. And this will reduce both its usability and trust in the catalog.

So, define roles and responsibilities, such as:

• Data stewards,
• Data owners,
• Data governance teams

These are the people who will later be responsible for maintaining the quality of the metadata. Avoid a scenario where there’s nobody responsible for curating and validating it.

And absolutely avoid leaving data catalog implementation and later maintenance solely to your IT team while leaving out business goals and data governance overall.

If you’d like to see an example of why this is so important, here’s a case study of a leading international retail chain, whom we helped translate project requirements from the business and architectural teams into actionable technical plans, to only then implement them.

Start with a proof of concept

To establish and validate all of the above, start with a small set of data. This could be data from one department or one data intelligence tool.

This is also when you can actually test data catalog software to see if it meets your needs when it comes to data governance without making a bigger investment upfront.

Make sure the POC has a timeline (preferably a compact one) not to get stuck in an endless testing phase. And then, track the results and analyze insights, for example, the accuracy of data lineage or the frequency of metadata refreshes.

Automate metadata ingestion and enrichment

This might be obvious, but we’ll say it anyway, just in case. Avoid manually populating a data catalog with metadata. Instead, use automated workflows, including Collibra’s automated connectors, AI-powered metadata extraction, and APIs to keep the metadata flowing and up to date.
Avoid launching your data catalog with static metadata that requires continuous, excessive human intervention.

Add context to your metadata

To make the metadata easy to access and understand for everyone, including non-technical users, it needs to be presented in a business context. Here are a few ways to do this:

Setting up a business glossary for the data catalog, with definitions gathered from business stakeholders. The bigger and more diverse the company, the more critical it is to have a unified terminology that everyone can understand in the same way.

Setting up data lineage to trace data back to the source and keep track of changes and transformations.

Creating and gathering documentation, including asset descriptions – and making it a continuous effort to keep your data assets up to date.

A bonus pro tip from one of our Collibra Rangers

Here’s what Bozhena Baranovskaya, Murdio’s top Data Governance Consultant and a certified Collibra Ranger, recommends to help increase and enrich the business context for your metadata with less effort.

“After you set up data lineage and add business context, invest in automated propagation of business definitions based on the technical lineage and asset names. This will help significantly increase the business context with minimum engagement from your data stewards.”

Best practices for data catalog management and maintenance

Already have a data catalog in place? Great! Are you getting from it what you expected in the beginning? (If you didn’t set clear expectations, go through the previous section again.)

Here are some things you should do to make sure the data catalog keeps doing its job.

Keep metadata fresh and relevant

A data catalog’s job is not a set-and-forget thing (apologies for the possible disappointment here). In fact, it’s a job for life (or at least, for as long as a company’s in operation and deals with data.)
This is because, over time, any metadata can become stale or irrelevant, posing the same issues as from before the catalog was even implemented and degrading the hard-earned trust in data.
Apart from automating the metadata ingestion and enrichment we’ve mentioned before, establish regular periodic reviews of your metadata. You can use automated validation rules in Collibra to flag outdated or missing information.

​Double down on company-wide adoption

You’re probably well aware that it’s not enough to just buy a software tool and set it up. We’ve seen many enterprises with significant investments in tools like Collibra that went underutilized with tons of untapped potential.

We’ve also helped companies overcome that challenge, like in the case of this Swiss private bank that had a Collibra instance in place but did not use its potential. On top of that, they lacked stakeholder alignment with business users, IT teams, and regulatory experts, all needing to be synced to collaborate better.

Our work started with establishing a centralized data catalog with an additional challenge of classifying sensitive critical data elements across over 100 applications. We also integrated Collibra with the bank’s configuration management database to automatically populate metadata and maintain an accurate application inventory.

Apart from the data catalog work and governance workflows automation, our work included targeted training sessions for business and IT teams and comprehensive educational documentation, while making sure all business stakeholders understood how to use Collibra to improve operational efficiency.
Never assume data catalog users (= your employees) will engage with the catalog without any guidance and avoid launching without a clear enablement strategy.

Track and monitor usage

Remember your KPIs? They’ll come in handy at this stage. Measuring the effectiveness of a data catalog is a must-have if you want to see long-term results. And again, this is where many companies underdeliver, leaving the data catalog to just live its own life.

So, instead track usage patterns, search behavior, frequently accessed assets, and metadata updates. Collibra usage analytics is your best friend for this. And maybe more importantly, use the data to regularly refine governance policies.

A data catalog alone is not enough to monitor data quality. For proactive monitoring and real-time tracking of data quality, operational metrics, and pipeline health, integrate the data catalog with data observability tools (which you’ll also find in Collibra).

Don’t stop at data catalog implementation

The above data catalog best practices are really just the basics, but ones you shouldn’t skip. The mistake many companies make is to believe a data catalog is a one-and-done thing, while it’s just the beginning of often much more complicated processes and workflows that use the data catalog at their core, such as data lineage tracking. If anything, it makes the proper implementation of a data catalog all the more critical.

If you’re looking for experts who can suggest the best ways to build and implement a data catalog in your company, based on hundreds of past projects, let us know – we’ll be glad to help!

Frequently Asked Questions

What makes a good data catalog?

In a nutshell, a good data catalog is one that’s actively used, regularly updated, and trusted by stakeholders across your company. It should be a central hub for all data assets, serving as a single source of truth and making it easier for teams to discover, understand, and use data effectively.

Here are some of the must-have aspects of a good data catalog.

• Automated and accurate metadata collection. A data catalog should automatically gather metadata from various sources, including databases, data warehouses, and cloud storage systems. It should also provide rich metadata, such as data lineage, usage statistics, and quality indicators, to help users assess the reliability and relevance of datasets.
• Strong data governance with clear ownership. Each dataset should have responsible stewards who oversee its quality and compliance. It should also support role-based access control, so that it’s easy to enforce data privacy policies while making sure that the right people have access to the right information.
• Compliance with regulatory requirements, such as GDPR, CCPA, HIPAA, and any other relevant regulations concerning data, depending on the industry and location.
• Business-friendly search and discovery tools, such as keyword search, natural language processing (NLP), and filtering options help business users quickly find relevant datasets. The catalog should provide context, such as data descriptions, tags, and user annotations. Interactive previews, data profiling, and usage examples also improve data discoverability and usability.
• Integration with other analytics and BI tools across the organization. For a data catalog to be actually useful, it has to seamlessly integrate with your analytics, business intelligence (BI), and data visualization tools. This way, data teams can access data directly from the catalog without switching between multiple platforms. API support and connectors for tools like Tableau, Power BI, and SQL workbenches also help streamline workflows and improve efficiency.
• Built-in mechanisms for data quality monitoring, user feedback, and automated anomaly detection help keep the catalog accurate and relevant. Machine learning-driven recommendations, such as suggested dataset relationships or frequently used queries, can enhance the user experience. And fostering a culture of data stewardship, where users actively contribute to improving metadata and documentation, is key when you want the catalog to continue to provide value over time.

How do you structure a data catalog?

A well-structured data catalog typically includes:

• Data domains (e.g., Sales, Finance, HR) for logical organization and to help create a structure that aligns with how teams use data in their daily work.
• Metadata categories, including technical metadata, business metadata, and operational metadata to support the different users the catalog serves.
• Governance workflows to manage data certification and approvals, including approval processes to validate data assets, certification tags that indicate a dataset’s status, and role-based permissions to restrict access.
• Tags and business glossaries to enhance searchability and provide business context to non-technical users, with synonyms and alternative names that map different terminology used by different teams.

How to maintain a data catalog?

1. Implement automation for metadata updates, e.g.:- Automate metadata extraction from databases, data lakes, and cloud storage systems to capture schema changes, data lineage, and usage statistics in real time.
   – Use data profiling and quality monitoring tools that continuously scan datasets for inconsistencies, missing values, or anomalies and update the catalog accordingly.
   – Use event-driven updates that trigger metadata refreshes whenever a dataset is modified, preventing outdated or inaccurate information from lingering in the catalog.
2. Assign data owners and stewards for accountability. Create accountability frameworks, such as service-level agreements (SLAs) for data accuracy and timeliness.
3. Establish review cycles and data quality checks. Implement feedback mechanisms so that users can report issues or suggest updates.
4. Provide continuous training and monitor adoption across the company, with hands-on training sessions, documentation, and tutorials. Monitor usage metrics, such as search frequency, dataset views, and user engagement, to identify adoption gaps and refine training strategies.
5. Encourage a data-driven culture by integrating the catalog into existing workflows and emphasizing its role in decision-making, analysis, and reporting.