The Ultimate Guide to Evaluating a Data Catalog

Download Data Catalog RFI/RFP here

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Data catalogs were created to help people working with data find and understand it better. Before data catalogs, data professionals often worked in the dark. They couldn't easily see what data was available, what it contained, or how good it was. This meant they spent a lot of time just trying to find and figure out data, sometimes even recreating data that already existed. Data catalogs help solve these problems.

At first, data catalogs just helped keep track of data and helped with data discovery. But they quickly became more useful and popular. Today's data catalogs do much more. They're now important for managing data, implementing governance rules, and enable self-service data consumption. They’re automated, AI-powered, and have the potential to really change the data culture in your organization.

The problem is, there are now so many data catalog tools to choose from. It can be hard to know which one is right for your organization. That's what we'll help you figure out in this article.

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What is a Data Catalog?

Gartner, a specialized research business, defines the notion of data catalog as follows:

“A data catalog creates and maintains an inventory of data assets through the discovery, description, and organization of distributed datasets. The data catalog provides context to enable data stewards, data/business analysts, data engineers, data scientists and other data consumers to find and understand relevant datasets for the purpose of extracting business value” Gartner, Augmented Data Catalogs 2019

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Step 1: Define Your Organization's Needs for a Data Catalog

A well-chosen data catalog can streamline data discovery, enhance collaboration, and ensure data governance. But before diving into the evaluation process, it's crucial to understand your organization's specific needs.

The first step to choosing a data catalog is to understand your exact need for a data catalog. As we mentioned already, data catalog vendors have multiplied in the past years, and they cater to different needs. Are you looking for a data governance tool? A pure data discovery tool?

You need to define exactly what you're looking for before going on a data catalog quest. To this end, you should start by identifying your pain points, and then find which data catalog addresses them. The first exercise is thus to identify the top challenges that affect your team's productivity and to map them to data catalog features.

Conduct Surveys and Interviews

Gather insights from various stakeholders within your organization to understand their specific pain points. Here are some sample questions to guide your survey:

• What are your top three challenges that affect productivity?
• How many hours would you save by solving each of the three challenges every week?
• Which data-related activities do you face productivity challenges with today?

Map Organizational Needs to Data Catalog Capabilities

Once you have identified the key challenges, map these needs to the core capabilities of data catalogs:

• Discovery: Master search functionality to easily find data assets using metadata, glossary terms, and classifications.
• Governance: Customizable access policies to manage data provisioning.
• Trust: Data quality rules and scores to ensure reliability.
• Knowledge Management: Comprehensive data dictionaries and profiles for every data asset.

Prioritize Features Based on Needs

Rank the identified features in order of priority. This will help you focus on what matters most to your organization and ensure that the data catalog you choose aligns with your specific requirements.

Step 2: Understand Data Catalog Capabilities

Data Discovery and Inventory

One of the primary capabilities of a data catalog is data discovery and inventory management. This feature allows users to:

• Easily locate data assets: Think of it as Google for your data, enabling complex queries.
• Maintain a dynamic inventory: Automatically updates as new data is added, modified, or removed.

Metadata Management

Metadata management is critical for understanding and utilizing data effectively. A good data catalog will:

• Automate metadata collection: Reducing manual effort and ensuring up-to-date information.
• Provide context: Helping users understand the origin, structure, and usage of data.

Data Classification and Tagging

Effective data classification and tagging streamline data organization and retrieval. Key points include:

• Automatic classification: Using machine learning to categorize data based on attributes like type, source, and sensitivity.
• Custom tags: Allowing users to add specific labels relevant to their projects or business functions.

Data Quality Indicators

Data quality is non-negotiable. A robust data catalog will offer:

• Quality scores and alerts: Indicating data freshness, completeness, and validity.
• Profiling tools: Analyzing data to identify outliers and inconsistencies.

Collaboration Features

Collaboration is vital for data-driven decision-making. Look for data catalogs that offer:

• User annotations and reviews: Allowing team members to share insights and feedback.
• Group projects: Facilitating teamwork on data-related tasks.

Governance and Security Capabilities

Data governance and security are paramount. Essential features include:

• Access controls: Customizable policies to manage who can view or edit data.
• Compliance tracking: Ensuring adherence to regulatory requirements.

Step 3: Creating a Customized Evaluation Criteria for Data Catalog

Creating a customized evaluation criteria for a data catalog is crucial to ensure you select a tool that meets your specific needs. When evaluating a data catalog, it’s essential to align the criteria with your organizational goals and challenges.

Here, you will find a Request for Information (RFI) template that should help you compare data cataloging solutions against your specific needs.

One effective way to evaluate data catalogs is by creating a rubric. This helps in scoring each tool based on predefined metrics. Here’s how you can do it:

• List Key Features: Include features like data discovery, metadata management, data quality indicators, and collaboration tools.
• Assign Weights: Prioritize features based on their importance to your organization.
• Score Each Tool: Evaluate each data catalog against these criteria and score them accordingly.

Non-Functional Aspects of Data Catalogs: The Hidden Game-Changers

When evaluating a data catalog, it's easy to get caught up in features. But the non-functional aspects can make or break your data catalog experience. These aspects should play an important role in your evaluation. To evaluate non-functional aspects of a data catalog, ask yourself these questions:

• Will this data catalog play nice with our existing tech stack: Does it have pre-built connectors? Robust APIs? Don't just take the vendor's word for it. Ask for a proof of concept to see how the data catalog plays with your specific tools.
• Can our non-technical stakeholders actually use this thing: Is the navigation intuitive? can users find what they need in under 5 clicks? Is the Search easy and intuitive? Can you tailor the interface to different types of users? Make sure the tool you choose is easy to use, or it will quickly become shelfware.
• Will it crumble under the weight of our ever-growing data: Can the catalog handle terabytes? Petabytes? Are the search results fast? Does it allow for many concurrent users? Ask vendors for performance benchmarks with data volumes similar to yours. Better yet, run your own tests during the trial period.
• Are we signing up for a financial nightmare: What is the licensing model? What are the main cost drivers? Are there any hidden costs linked to connectors or advanced features? How does the price change as you grow? The cheapest option isn't always the most cost-effective. Factor in time saved, increased data usage, and potential revenue gains from better data decisions.

Step 4: Understanding The Data Catalog Ecosystem

Once you have clearly defined what you're looking for in a data catalog, it's time to find your perfect match. This is no easy task, as there are a plethora of options to choose from. We've attempted to untangle the data catalog ecosystem to help you find the perfect fit. There are four generations of data catalog tools:

• 1st generation: basic software, similar to Excel, that syncs with your data warehouse.
• 2nd generation: software designed to help the data steward in maintaining data documentation (metadata), lineage, and treatments.
• 3rd generation: software designed to deliver business value to end-users automatically hours after the deployment. It then guides users to document in a collaborative painless way.
• 4th generation: Decentralized and intelligent platforms that integrate directly into the user’s workflow with advanced AI capabilities, offering a more personalized and automated approach to data documentation and discovery.

Data catalog landscape

Below, you will find a data catalog landscape, which can hopefully help you choose a metadata management tool adapted to your needs.

• This is a brief attempt at classifying the tools on the market. If anything seems wrong, or if you don't see your data catalog and want to have it placed, feel free to reach out.

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If you want to know more about vendors, their offerings, and the data catalog ecosystem , you will find our data catalog benchmark here.

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Step 5: take Demos from Selected Vendors

You have now selected a few catalogs that seem to match your pre-defined criteria and answer your business needs. It's time for the next step: take a demo.

If you sit as a passive viewer during the demo, you're unlikely to get much value out of it. You should be participating actively and leave with a clear idea of how the data catalog software will help address your specific needs.

We encourage you to plan for the key topics you want to cover and share the features that matter to you the most to the vendors in advance. This will ensure a much more tailored experience.

We thus propose setting the following agenda beforehand covering the following topics:

‍Cost of ownership

Price is obviously a concern when choosing a catalog software. However, price often involves more than the price declared by the vendor. Total cost of ownership involves how much the software costs to purchase, implement and maintain.

Purchasing: Ensure you have understood what's comprised in every pricing tier. Inquire about potential additional purchase charges, such as extra users.

Implementation: Inquire about implementation costs, as it can make a significant difference. For example, choosing an open source data cataloging solution will save you from purchasing cost, but will lead to important implementation costs.

Maintenance: Make sure you understand clearly what the vendor charges post purchase, such as updates. Even without updates, the software might be expensive to maintain. For example, legacy data catalogs (1st generation) often require a full time engineering team to maintain the tool. Ensure that you factor in these additional costs within the total cost of ownership.

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Vendor support

What relationship will you have with the vendor after completing the purchase? Will you be on your own? If so, does that work for you? This is not a negligible question. A lot of Tesla owners love their car but have encountered frustration due to bad customer service experience and bitterly regret their purchase choice. For this reason, ensure you have understood the following:

1. Training conditions: How is your team going to learn how to use the catalog? Is training included for all users? If not, does it entail additional costs? Make sure you have cleared out the path regarding onboarding matters.
2. Support: Ensure that you've understood different levels of customer service (phone, email, chat) and their costs. Be sure to leave with a sense of the service logistics, such as whether customer service available 24/7 or only during certain hours.

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Data and privacy

Companies can lose serious amount of money and customer trust following data security breaches. Be sure to understand exactly what data the vendor has access to, the kind of security the vendor uses for its databases, and what processes they've got in place to keep your information safe.

We also advise that you attend the demo with stakeholders from different teams. This will allow you to gather the most comprehensive feedback, and thus choose the right tool that suits all kinds of users. Finally, ensure that the data catalog is compatible with your current data infrastructure as well as well as with your vision and roadmap for the next 1-5 years.

We have also pulled together a more detailed checklist of data catalog assessment criteria that you can use during demos here:

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Step 6: Run Proof of Concepts (POCs)

After narrowing down your choices based on the demos, the next step is to run Proof of Concepts (POCs) with your top contenders. A POC allows you to test the data catalog in your own environment and with your own data.

Here's how to make the most of your POC:

• Set clear goals: Define what success looks like for your POC. What specific outcomes are you looking for?
• Use real data: Test the catalog with a subset of your actual data to see how it performs in real-world conditions.
• Involve key stakeholders: Get feedback from the people who will be using the tool daily. Their input is crucial.
• Evaluate non-functional aspects: Test things like performance with your data volume, integration with your existing tools, and user experience across different roles.
• Time it: Set a reasonable timeframe for the POC. This could be anywhere from a few weeks to a couple of months, depending on your needs.
• Create test scenarios: Develop specific use cases that reflect your typical data operations and challenges.

During the POC, pay attention to:

• How well the catalog handles your data volume and complexity
• The accuracy and usefulness of its automated features (like data classification or quality scoring)
• The ease of customization to fit your specific needs
• The quality of support provided by the vendor during the POC

By thoroughly engaging in POCs, you'll get a much clearer picture of which data catalog is the right fit for your organization. This hands-on experience is crucial in making an informed decision that aligns with your needs and goals.

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About us

We write about all the processes involved when leveraging data assets: from the modern data stack to data teams composition, to data governance. Our blog covers the technical and the less technical aspects of creating tangible value from data.

At Castor, we are building a data documentation tool for the Notion, Figma, Slack generation. We designed our catalog software to be easy to use, delightful and friendly.

Want to check it out? Try CastorDoc for free with a 14 day demo.

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