Data Catalog Software: Why It’s Essential and How to Pick One for Your Business in 2024

Updated December 15th, 2023

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Data catalog software has changed quite a bit over the years and continues to evolve at a rapid pace today. What defines a best-in-class vs. outdated data catalog? It can be difficult to decide which tool is the right fit for your business.

To help you make a more informed decision, here’s everything you need to know about selecting a versatile, inclusive, and transformative data catalog .

Data catalog software is a vital technology for the modern business

As you begin your journey to select a data catalog, you may be tempted to ask the question, “Do we really need data catalog software? Can we just get by with what’s already in place?”

Let us assure you that if you are dedicated to building a data-driven organization, having a data catalog in place is fundamental to extracting full value from the data you have at hand, as well as the data you will handle in the future.

By combining metadata with data management, governance, and search capabilities, effective data catalog software helps businesses organize data, discover the right data assets at the right time, and determine if a given asset is right for the desired use cases.

Basically, a data catalog serves as a single source of truth that allows data users to quickly access and share relevant insights that drive smarter business decisions.

Must-have features in data catalog software

All data catalogs are not created equal. Since the dawn of the data catalog in the 1990s, there have been several generations and iterations of this technology, with each version evolving to align with the business needs of the current era. There are a few key features that define modern data catalog software and distinguish it from the tools that were used 10 or even just five years ago.

Data catalog must-have features include programmable bots, embedded collaboration, end-to-end visibility, and an open API layer:

Programmable bots: Because there is no magic machine learning algorithm capable of solving every type of data management challenge, programmable bots provide a framework that allows teams to create custom algorithms to drive the intelligence they need.
Embedded collaboration: This functionality empowers all types of data users with different skill sets and tech stacks to interact with, share, and discuss data assets across their existing workflows (think Figma and GitHub, but for data).
End-to-end visibility: Today’s data users need a single repository that provides instant, comprehensive visibility (who owns a data set, where it originates from, how they can use it, etc.) without having to toggle between data catalog, quality, lineage, and governance tools.
Open by default: Integrating large amounts of metadata with the rest of the modern data stack to power “futuristic” use cases, such as CI/CD pipelines, requires an openly accessible API layer that allows data teams to innovate.

Open-source data catalog software vs. Data catalog software as a service (SaaS): Benefits and drawbacks

Part of identifying which data catalog software is the right fit for your business means determining if you want to take on an open-source data catalog or purchase a data catalog solution. It comes down to answering the build vs. buy question that can emerge when evaluating enterprise software. In most cases, organizations end up paying significantly more to build and maintain their own data catalog than they would by subscribing to a SaaS tool.

Open-source data catalog software benefits

The primary benefit of open-source data catalog software is customizability. Over the past decade, some of the biggest tech companies, such as LinkedIn and Netflix, have built their own data catalogs to address their organizations’ particular needs, later releasing the software as open-source that other businesses can build on top of.

Open-source data catalog software drawbacks

While open-source data catalogs do have great potential, it is very difficult to find open-source software that can fit seamlessly into the workflows of the modern data team. Specific challenges include:

Lack of ready-to-use capabilities and features
Limited documentation and information
Minimal or zero support

Open-source data catalogs are intended to be used by experienced data engineers and require significant time and resources to build the desired functionality (not to mention keep up with the required maintenance). According to one estimate from Sprinkle Data, this ends up costing two to three times more than paying for a data catalog that includes support, maintenance, and more.

Data catalog software as a service benefits

The logical alternative to building your own data catalog based on open source software is to purchase software as a service (SaaS) data catalog that offers a lower overhead with more valuable output. Here are some of the other benefits of adopting a data catalog SaaS solution:

Best-of-breed features (see: “must-have features” above) that can be used immediately
A more intuitive user experience for diverse data teams and less technical users
“Service” element provides an irreplaceable human touch (onboarding, training, workshops for new features)

In addition to having a lower total cost of ownership than a build-it-yourself data catalog, SaaS platforms are constantly being updated with new features and functionality that allow your business to stay lean and competitive with your data operations.

Data catalog software as a service drawbacks

If you aren’t careful about selecting the right software, there are certain factors that could hinder your data processes and have you wondering if you should switch to a different solution. For example, if you adopt a SaaS data catalog that isn’t “open by default” as mentioned previously, your catalog will exist in isolation without the option to create custom plugins for data asset integrations or collaboration across different tools.

Another area to consider is cost efficiency: With some SaaS data catalogs, there is a fixed pricing model in place where you end up paying for things you don’t use. You can even get locked into a contract which makes it very difficult to switch to a plan that is better suited to your needs. For this reason, you’d be well-advised to select a platform with flexible, pay-as-you-go pricing that can easily scale up or down as needed.

For more insight into the “build vs. buy” decision, check out this story about Delhivery and how they experimented with both options before ultimately making a decision that gives them an optimal user experience so they can extract full value from their data.

How to choose the right data catalog software in 2024

Here are five steps you can use to select a data catalog that will keep your company’s data users satisfied for years to come.

Step #1: Define organizational needs

The foundation of the data catalog selection process is to have a crystal clear understanding of what an ideal product will do for your company:

Start by identifying the top three challenges that are causing data initiatives to fail at your organization.
Next, map organizational needs or challenges with the core functionalities of data catalog offerings.
Then evaluate non-functional considerations related to data catalogs and how relevant they are to the needs of your organization.

Step #2: Create specific evaluation criteria

Having determined the critical needs a data catalog must fulfill, document a list of objective criteria, including levels of priority and how that might map to the capabilities of a given solution.

The core capabilities to look for in modern data catalog software are:

Discovery: Provides a clear and comprehensive view of all data assets within the organization.
Knowledge: Gives insight into all context, information, and business know-how around data assets.
Trust: Includes information on data quality and coverage along with usage of data assets with the organization.
Collaboration: Features an intuitive UI for diverse team members to effectively work together on data assets.
Governance: Allows users to manage access rights to data assets to ensure legal and regulatory compliance.
Security: Ensures secure and compliant usage of data assets within the organization.

Step #3: Evaluate the current data catalog market

Conduct comprehensive research to understand all of the options available. Confirm any candidates that move on to the final consideration steps have all the essential features we have discussed so far.

Step #4: Book demos from select vendors

Once you have selected a few data catalogs that seem like they would be a good fit for your organization’s needs, reach out to those vendors and schedule demos.

Best practices for this stage include:

Share a finalized evaluation criteria document with vendors in advance.
Ensure stakeholders from different teams attend the product demos.
Conduct a data architecture compatibility check.

Step #5: Execute hands-on proofs of concept (POCs)

After the demos, narrow down your list to a select few vendors and set up proofs of concept for a hands-on look at how the product would function within your business. Evaluate how the vendors respond to feedback and if they are willing to acknowledge the benefits of other solutions on the market.

For an even closer look at each of these five steps, dive into The Ultimate Guide to Evaluating a Data Catalog.

Data catalog software and the future of the modern data stack

As you can tell, we’re recommending you ask a lot from your data catalog software. It may even be more than you previously thought data catalogs were capable of. Truthfully, most data catalogs are stuck in the past and are simply not capable of bringing full context, trust, and governance to data.

After talking to hundreds of data leaders to understand what they really want to see in a modern data catalog, we helped establish the term Data Catalog 3.0 to describe this new generation of collaborative, metadata-driven technology.

In addition to everything mentioned above, this new breed of data catalog leverages the concept of active metadata management. While traditional, passive metadata is just technical metadata (schemas, data types, models, etc.), active metadata includes everything that happens to the data and is done to the data, including operational, business, and social metadata, among other types.

We firmly believe that third-generation data catalogs will be the key to future-proofing organizations’ DataOps and getting the most out of the modern data stack. There are even a steadily growing number of RFPs that specifically request Data Catalog 3.0.

Using data catalog software to innovate rapidly, at scale

Data catalog software is no longer optional for the enterprises of today; now, it’s table stakes for organizations to be able to innovate at a swift pace and keep up with the competition. If your business doesn’t have a data catalog in place, or if you do but still spend too much time looking for data, questioning the quality of data, or struggling to collaborate across diverse data assets, it’s time to reconsider what a modern data catalog should look like and how it can help you achieve maximum data success.

Learn more about the value of a third-generation data catalog.

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