Comprehensive Data Catalog RFP Criteria and Requirements in 2024

Explore essential considerations for picking the right data catalog for your organization. Get insights into how to find a solution that is comprehensive, easy to adopt, and scales with you.
Last updated
April 11, 2024

A data catalog is your central source of truth for your organizational data. It shows the relationships between data sources and is essential for reliably scaling an organization’s data function.

Companies produce more data than ever before, yet up to 73% of all enterprise data goes unused. Finding the right data, when you need it, and understanding said data is no easy task - not to mention the dozens of rules, regulations and security requirements related to data that must be followed.

What is a data catalog? 

Data catalogs are centralized repositories that organize and categorize all types of metadata, including technical and business metadata. A data catalog is used to allow an organization’s users to more easily search and discover data. The most important part of implementing a data catalog is choosing the right data catalog solution. 

A data catalog should be 3 things:

  1. A way to discover & make your data more accessible. Choose a data catalog with robust search features, integrating AI and LLMs for valuable insights. Opt for catalogs compatible with collaboration tools like Slack to enhance discoverability and user adoption, ensuring easy access to essential information.
  1. An easy to use resource for both data and business stakeholders. Data catalog initiatives fail due to poor adoption from business stakeholders and data consumers. A data catalog should prioritize simplicity and user-friendliness for all users, including those who are not data experts. An intuitive interface and straightforward features enable non-technical individuals to effectively contribute to and benefit from data-driven decision-making, fostering increased data literacy across the organization.
  1. Future-proof and built to scale. Ensure future success in data management by prioritizing scalability, adaptability, and compatibility in your data catalog selection. Choose a tool that grows with your organization, integrates seamlessly with other tools, and aligns with your long-term data strategy. Investing in a robust catalog and consolidating tools prepares your data team for future challenges and advancements, establishing a resilient foundation.

How to create a data catalog RFP?

Understanding the various roles a data catalog can play is crucial before you start searching for one that aligns with your requirements. Identifying what you need and assessing different options is a substantial effort. However, adopting a structured approach can simplify this task.

Your RFP should outline your organization and project background, specify what you're looking for in a product, the criteria for making your selection, and the timeline for the vendor selection process. 

Start with a data catalog RFP template. Your RFP should include sections on:

  • Vendor company profile: Essential details about the vendor, such as their history, customer base, and areas of expertise.
  • Product features: A comprehensive description of the product and its functionalities, questioning how they meet your needs.
  • Technical specifications: Information on the product's technical aspects, including security features, scalability, and how it integrates with your current technology infrastructure.
  • Implementation and support: Details regarding the implementation process, ongoing support, training, and maintenance offerings.
  • Pricing: A breakdown of the pricing model, including any additional costs for extra features or support services.

Key criteria for selecting a data catalog tool

Consider how a modern data catalog's features align with your business goals and data strategy. For instance, compatibility with specific tools used by your team should be a criterion if those tools are integral to your operations.

The essential criteria includes:

  1. Powered by AI and LLMs
  2. Data discovery
  3. Data governance
  4. Data monitoring and observability
  5. Scalability
  6. Ease of adoption
  7. Security
  8. Technical support and enablement

Powered by AI and LLM enabled

Modern data catalogs that leverage AI are not just tools; they are strategic assets in the data management strategy. AI transforms data catalogs into intelligent co-pilots for everyone at the organization, streamlining workflows, and ensuring that data becomes a powerful ally in driving business success. As organizations continue to struggle with increasing data complexities, embracing AI-driven data catalogs becomes not just an option but a necessity in staying ahead. 

AI-powered data catalog platforms enable organizations to get better data-driven insights in several ways. 

  • They allow nontechnical users to use natural language search to discover data faster and without help from the data team. This frees up time for the data team and empowers business users in your organization to make more data-driven decisions in a fraction of the time.
  • AI data catalogs make data management processes much more efficient. Using an AI-powered data catalog, you can automate tasks like data tagging, identification, organization and more.
  • Automatically generated data lineage makes it much easier to track and monitor the entire life cycle of data, and time-consuming documentation tasks no longer have to eat up hours of the workweek
  • Improved data quality by automatically identifying inconsistencies, errors, duplicates, and breached thresholds.

Data Discovery

Even with great data practices, many organizations still struggle to get value out of data. One large contributor to the problem is that organizations create data silos from day one, by not documenting and centralizing data in a place where all users can access information. Data discovery is the practice of taking data and turning it into meaningful insights. Choose a data catalog with strong discoverability capabilities to help everyone in your organization find what they need, quickly and precisely.

Some key features to explore related to discovery:

  • Powerful and intuitive search: Choose a data catalog with advanced search capabilities including features like semantic and LLM-powered search, and a user-friendly modern UI. Prioritize efficient data discovery to enhance productivity and alleviate data request bottlenecks for your team. The catalog's search feature is crucial for indexing all data  effectively.
  • Automated table and column level lineage: Data lineage tracks data evolution from source to destination, revealing connections between sources. Lineage makes it possible for users to understand the connections between different data sources, and answer questions about where data comes from, and what transformations occurred. Opt for a data catalog with lineage capabilities to empower users to find data, understand its end-to-end journey, assess impact from changes, and stay informed on critical errors.
  • Collaboration: Data catalogs should allow you to collaborate in real-time on tables and visualizations to expedite decision-making. A collaborative data catalog enables open discussions, tracks results, and facilitates faster decision-making by integrating with collaboration tools like Slack.
  • Ticket and request management: A lot of information isn't discoverable using metadata or metrics. Sometimes, questions to the data team are much more nuanced and require a person on the data team to answer. Select a data catalog with robust features that facilitate user questions, eliminating the need for ad hoc inquiries through apps like Slack and Jira, and allow users to ask and save questions and answers, preventing repeat requests. 
  • Documentation: Documents allow the data team to store context on your data assets, to assist users with self-serve knowledge and make data accessible to all. Opt for a data catalog with robust data documentation features, integrating text, queries, live charts, and more in an intuitive UI. Enhance documentation efficiency by investing in a catalog enriched with AI tools.

Data Governance

Data governance is the practice and concept of managing data throughout its lifecycle. This includes gathering, understanding, protecting, and leveraging your data. Some data catalogs offer features related to governance that can help make sure your data is being managed properly. Implementing a data catalog or knowledge base with annotations on what the data is and how it should be used results in consistent knowledge of the data.

A data catalog should make it easy to mandate data access and protect PII

Data Access: To assist with strong data governance, choose a data catalog that is able to extract metadata from your sources to understand who in your organization has access to which data assets, internally and externally.

Data catalogs with strong analytics features can show you which users are accessing certain resources, viewing frequently used dataset and provide insight on the most important and sensitive assets at your organization.

Personally Identifiable Information (PII): PII is used to identify data assets that contain personal information that can be used to identify an individual. This sensitive data needs to be tagged and classified so it is treated appropriately. If your organization handles sensitive information and PII, consider a modern data catalog that has automated PII tagging capabilities, so that you’ll never miss identifying sensitive information.

Data Monitoring and Observability

Simplify your data infrastructure by investing in a tool that connects data quality, observability, and discovery for comprehensive visibility into your entire stack. Choosing a data catalog with monitoring built in will help you surface data quality information directly where users are already discovering and exploring data while preventing data asset sprawl. Save money and decrease complexity by consolidating tools.

Key features to look out for include:

  • Metadata management:  visibility into pipeline metadata metrics (cost, query, volume, and popularity) prevents data quality issues and maximizes metadata utilization.
  • Cost management: get visibility into cost drivers in your data stack, allowing for optimized pipeline management and cost assessment. Reliably manage the cost of your infrastructure without surprise compute costs.
  • Observability: Surface key data quality indicators such as freshness, cardinality, uniqueness, and more in the same place where data discovery happens.
  • Detect anomalies: Don’t be caught off guard by data anomalies and breached thresholds. Data catalogs with monitoring built in integrate alerts into your workflow, so you can be notified via app, Slack or email when there is an incident.

We believe that doubling down on cost containment for data teams, and enabling them with monitoring and observability tools is crucial, especially in our current economic climate. It is invaluable to be able to demonstrate the value of data discovery and democratization.


Future-proof your data catalog and choose a platform that will grow with your team, instead of a tool you’ll have to take out of the stone age, or eventually ditch for a newer, shinier model. Invest in a data catalog that is designed to scale with your team, and is continually improving.

Some key items to consider:

  • Pricing: Legacy catalogs generally have a large upfront cost in addition to ongoing maintenance expenses. Legacy catalogs often employ a-la-carte pricing, potentially exceeding modern solutions' costs with less functionality. They typically aren’t as flexible, considering they were developed at a time when company data needs were very different than today.  A modern data catalog should be customizable to organizational needs, providing scalability at a reasonable price, distinguishing it from point-solution catalogs.
  • Consolidation: Explore modern data catalogs as strategic partners in managing the ever-expanding array of tools. Choose a catalog that evolves with consistent improvements and new features to streamline data management, consolidating tools and preventing data sprawl. Modern data catalogs offer more than being just a data catalog. At Secoda, we’ve grown our data catalog to be able to connect data quality, observability, and discovery. A data catalog that provides visibility into the health of your entire stack, and prevents data asset sprawl, can work overtime so your team can focus on your main priorities.
  • Integrations: Take advantage of modern data catalogs that offer native integrations with the tools in your data infrastructure. Choosing a data catalog that is frequently creating new integrations and is receptive to customer feedback and requests will pay off if your stack ever changes, or if you need a more custom solution. Some data catalogs offer options for customers to build their own custom integrations, so that no matter what kind of tool you’d like to integrate into your data catalog, you’ll be able to. Secoda’s platform natively integrates with 40+ data tools, and we add new integrations every week.

Ease of Adoption

Initiatives involving data catalogs often fall short because they're not widely embraced by business stakeholders and those who use the data. For a data catalog to be successful, it must be easy to use and welcoming for everyone, not just those with data expertise. 

Data catalogs designed with data democratization in mind will make it easier for users to navigate through the catalog, reduce learning curve for onboarding, and will make it easy for users, technical or otherwise, to integrate a data catalog into their day-to-day.

Think about a data catalog as the homepage for your company’s data, rather than simply a catalog. 


A secure data catalog is vital for safeguarding sensitive information, ensuring compliance with data protection regulations, and protecting intellectual property. Choose a solution that effortlessly connects your data, provision users, manage access requests, enforce role based access controls (RBAC), and is committed to maintaining the highest standards of data protection compliance.

  • Deployment Methods: Depending on your needs, a data catalog should have multiple deployment methods. You can deploy your data catalog in a self-hosted environment, behind your own VPN, and in your own VPC if you choose, and deploy via Terraform or Docker. Multi-tenant HIPAA or EU, Single-Tenant, and Private Cloud options can also be available depending on your needs.
  • Role Based Access Controls (RBAC): RBAC refers to the idea of assigning permissions to users based on their role within an organization. RBAC not only keeps your data secured, but helps users have the best experience possible by only granting access to the information they need.
  • SOC II Compliance: Data catalogs can be SOC 2 compliant, meaning the way the product process and store client data is secure and protected, based on standards set by the AICPA.
  • SAML, SSO & MFA: Sign in with the services your team already uses, including Google and Microsoft SSO, Okta, MFA and SAML.
  • SSH Tunnelling: Securely move data from your private databases to your data catalog with SSH tunneling.
  • Encryption: The data managed with your data catalog should be fully encrypted in transit and at rest, so that the vendor does not see the data they are moving.
  • HIPAA: If you work in a HIPAA compliant environment, choose a data catalog that can remain compliant.
  • GDPR: Choose a solution that recognizes the importance of adhering to regulations in advancing information security and privacy for citizens of the EU.

Technical Support and Enablement

When selecting a data catalog, it's crucial to assess the experiences of other customers. Examine the reputation of existing users and explore case studies and success stories related to the product. 

Ultimately, choose a partner rather than just a vendor—one that is receptive to feedback, transparent about product limitations, and values open communication with customers.

About Secoda

Secoda is the fastest way to explore, understand, and use data.

Companies like Chipotle, Cardinal Health, Kaufland, and Remitly use Secoda to get visibility into the health of their entire stack, reduce costs, and help their data teams run more efficiently.

Powered by AI, Secoda creates a single source of truth for an organization’s data by connecting to all data sources, models, pipelines, databases, warehouses, and visualization tools.

Secoda consolidates multiple tools into a single data management platform to simplify your data catalog, lineage, governance, monitoring, and observability processes.

Regardless of technical ability, it is the easiest way for any data or business stakeholder to turn their insights into action.

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