Data Catalog Requirements in 2024: A Comprehensive Guide

Updated July 31st, 2023
header image

Share this article

A data catalog should be able to manage diverse data assets, provided end-to-end visibility to users, handle metadata as big data, enable user collaboration and a lot more.

Why? Because having a solid understanding of your organization’s data is crucial for making informed decisions. And, a data catalog serves as a centralized hub that provides users with quick and easy access to relevant data.


Why 40% of data catalog programs fail

Lack of adoption and business engagement is the primary driver of failed catalog programs. Atlan helped HelloFresh reach their 3-year business adoption target in 3 months with adoption-first design and automations, like Atlan AI, to reduce manual enrichment by 50%.

👉 Try Atlan’s product tour


In this comprehensive guide, we’ll explore the essential data catalog requirements. From metadata management to search functionality, we’ll cover everything you need to know to build a robust and user-friendly data catalog.

Let’s dive in!


Table of contents

  1. What should a data catalog contain? Exploring key functionalities of a data catalog
  2. Steps to evaluate the data catalog tools and crafting an effective RFP
  3. Key considerations for selecting the perfect data catalog tool
  4. Rounding it up all together
  5. What is required for a data catalog? Related reads

What should a data catalog contain? Exploring key functionalities of a modern data catalog

A modern metadata management data catalog serves as a central hub for storing and organizing metadata. To ensure its effectiveness, a data catalog must possess key functionalities. It should enable seamless data discovery, efficient metadata governance, and collaborative data management.

In this section, we will delve into the essential requirements for a modern metadata management data catalog. Further exploring the key functionalities that empower organizations to harness the power of their data .

A data catalog in the context of modern metadata management must include the following requirements/functionalities:

  1. Management of diverse data assets
  2. End to end data visibility
  3. Handling metadata as big data
  4. Embedded collaboration
  5. Flexibility and scalability
  6. Integration with other data tools
  7. Data governance and trust
  8. User-friendly interface

Let us understand each of these requirements/functionalities in brief:

1. Management of diverse data assets


2. End-to-end data visibility


  • To establish a single source of truth for all data assets, a data catalog should provide visibility across the entire data lifecycle.
  • This involves aggregating data from different places like data lineage tools, data quality tools, data prep tools, and more. This ensures breaking down silos and providing a holistic view.

3. Handling metadata as big data


  • The data catalog needs to treat metadata as a form of data that can be searched, analyzed, and maintained in the same way as all other data types.
  • This approach could entail parsing through query logs to automatically create column-level lineage.
  • Further, it assigns popularity scores to data assets, or deduces potential owners and experts for each asset.

4. Embedded collaboration


  • A data catalog should facilitate smooth collaboration among diverse data teams. It needs to integrate seamlessly into the team’s daily workflow.
  • It enables actions like access request approvals or issue reporting directly within the platform. This capability promotes efficiency and reduces tool fatigue among data teams.

5. Flexibility and scalability


  • In line with the modern data stack, a data catalog must be quick to set up, easy to scale.
  • Furthermore, it should be flexible enough to accommodate growing data and varying user requirements.
  • It should be cloud-based, eliminating the need for extensive engineering time for setup.

6. Integration with other data tools


  • A data catalog should be interoperable with the tools used by diverse data teams, including SQL, Looker, Jupyter, Python, Tableau, dbt, and R.
  • This interoperability would improve usability and productivity.

7. Data governance and trust


  • While maintaining ease-of-use and scalability, a data catalog should also uphold and enhance data governance, trust, and context.
  • It should aid in defining and enforcing policies for data usage and ensure the consistency, accuracy, and security of the data.

8. User-friendly interface


  • A data catalog should have an intuitive and user-friendly interface to drive adoption among users. The design of the interface and user experience should not be an afterthought.
  • By fulfilling these requirements, a data catalog can successfully facilitate data democratization and governance in a diverse data environment.

Steps to evaluate the data catalog tools and crafting an effective RFP

Once you know the different functions of a data catalog, it is time to look for one that meets your needs. But, creating a Request for Proposal (RFP) for a data catalog and evaluating potential vendors can be a significant task. But, a systematic approach can make it manageable.

Here’s a step-by-step guide on how to proceed:

  • Step 1: Define your business and technical requirements
  • Step 2: Draft the RFP
  • Step 3: Distribute the RFP
  • Step 4: Evaluate the responses
  • Step 5: Request a demo or trial
  • Step 6: Check references
  • Step 7: Finalize the vendor

Let’s dive into each step much more in detail:

Step 1: Define your business and technical requirements


  • Start by outlining your organization’s specific needs.
  • From the aforementioned characteristics of a modern data catalog, which ones align with your business objectives and data strategy?
  • For instance, if your team uses a particular set of tools, ensure compatibility with these tools is a requirement.

Step 2: Draft the RFP


  • An RFP should include a background of your organization and project.
  • It should include the specific requirements you’re looking for, the criteria you’ll use for selection, and the timeline for the vendor selection process.

Here are some categories you can include in your RFP:

  • Vendor company profile
    • Basic information about the vendor, including company history, client base, and expertise.
  • Product features
    • Detailed description of the product and its features, asking how they align with your requirements.
    • This might include managing diverse data assets, providing end-to-end visibility, handling metadata as big data, and embedded collaboration.
  • Technical requirements
    • Details about the technical aspects of the product, like security features, scalability, and compatibility with your existing tech stack.
  • Implementation and support
    • Information about the implementation process, post-implementation support, training, and maintenance services.
  • Pricing
    • Detailed pricing structure, including any potential additional costs for extra features or support.

Step 3: Distribute the RFP


  • Send the RFP to a list of vendors you’ve identified as potentially fitting your needs. Ensure you give them a reasonable timeline to respond.

Step 4: Evaluate the responses


  • Once you receive responses, evaluate them based on your selection criteria.
  • This might include the product’s fit to your requirements, the vendor’s expertise and reputation, the quality of their customer support, and the pricing.

Step 5: Request a demo or trial


Ask the vendors who scored highly during the evaluation phase for a product demo or trial. This allows your team to explore the product hands-on and understand its usability and functionality better.

Step 6: Check references


Contact some of the vendor’s previous clients to get their feedback. Ask about their experience with the product and the vendor’s customer service.

Step 7: Finalize the vendor


Based on the demo, trial, and reference check, select the vendor that best fits your needs and budget. Make sure to have a clear contract stating all terms and conditions related to product usage, support, and pricing.

By following this process, you should be able to identify a data catalog tool that fits your needs. Remember, it’s crucial to involve key stakeholders, including data users, throughout this process to ensure the tool meets everyone’s needs.


Key considerations for selecting the perfect data catalog tool

When narrowing down a data catalog tool, it’s important to look beyond just the features and the price. When it comes to data catalogs, making an informed decision involves considering various factors beyond the essential requirements.

To ensure that you choose a tool that truly aligns with your organization’s needs, there are additional considerations to keep in mind.

Here are some points to ensure that you choose a tool that truly meets your requirements:

  1. Integration capability
  2. User experience
  3. Scalability
  4. Security snd compliance
  5. Vendor reputation and stability
  6. Community and support
  7. Total cost of ownership
  8. Allingment with business goals

Let’s dive into each of these considerations much more in detail:

1. Integration capability


  • The chosen solution should integrate seamlessly with your existing data infrastructure and tools.
  • It should support the data sources, BI tools, and data processing frameworks that you currently use or plan to use in the future.

2. User experience


  • The solution should be user-friendly, intuitive, and adaptable to the varying needs and skill levels of your diverse data users.
  • It’s a good idea to include representatives from all user groups (data engineers, data scientists, analysts, business users) in the evaluation process to ensure usability across roles.

3. Scalability


  • The chosen solution should be able to scale as your data grows and your needs change.
  • This includes both technical scalability (can it handle increasing data volume, variety, and velocity?) and functional scalability (does it support more advanced features that you might need in the future?).

4. Security and compliance


  • The tool should support robust data security measures and comply with relevant data privacy and governance regulations.
  • This is particularly important if you deal with sensitive or regulated data.

5. Vendor reputation and stability


  • Evaluate the vendor’s track record, financial stability, and commitment to ongoing product development.
  • You don’t want to invest in a tool only for the vendor to go out of business or discontinue the product in a couple of years.

6. Community and support


  • Consider the quality of support provided by the vendor, both during the implementation phase and post-implementation.
  • Additionally, a strong user community can be a valuable resource for getting help and sharing best practices.

7. Total cost of ownership


  • In addition to the purchase price, consider other costs such as implementation, training, ongoing maintenance.
  • Further you should also consider potential future costs for upgrades or additional modules.

8. Alignment with business goals


  • Lastly, always keep in mind the broader business goals that the data catalog is intended to support. The best tool is the one that best enables you to achieve those goals.

Remember, choosing a data catalog is not just about buying a product; it’s about forming a partnership with a vendor. So, assess not just the tool itself but also the vendor’s ability to support you in achieving your data goals.


Rounding it up all together

In today’s data-driven landscape, a robust data catalog has become indispensable for organizations to effectively manage their diverse data assets. A data catalog should be equipped to manage diverse data assets, provide end-to-end data visibility, handle metadata as big data, facilitate embedded collaboration.

Following a step-by-step guide to evaluate data catalog tools and craft an effective Request for Proposal (RFP) is essential. It enables organizations to select the right vendor for their specific needs.

By understanding the requirements and following this comprehensive guide, organizations can unlock the true potential of their data and propel their metadata management efforts to new heights.



Share this article

[Website env: production]