The modern data stack has helped democratize the creation, processing, and analysis of data across organizations. However, it has also led to a new set of challenges thanks to the decentralization of the data stack. In this post, we’ll discuss one of the cornerstones of the modern data stack—data catalogs—and why they fall short of overcoming the fragmentation to deliver a fully self-served data discovery experience.
If you are a lead of the data team at a company with 200+ employees, there is a high probability that you have
If that’s the case, you’d definitely find this post highly relatable.
This post is based on our own experience of building DataHub at LinkedIn and the learnings from 100+ interviews with data leads and practitioners at various companies. There may be many reasons why a company adopts a data catalog, but here are the pain points we often come across:
The bottom line is that you want to empower your stakeholders to self-serve the data, and more importantly, the right data. The data team doesn't want to be bogged down by support questions as much as data consumers don't want to depend on the data team to answer their questions. Both of them share a common goal—True Self-service Data Discovery™.
In our research, we saw striking similarities in companies attempting to solve this problem themselves. The story often goes like this:
Voilla! You now have a fully self-service solution and proudly declare victory over all data discovery problems.
Let’s walk through what typically happened after this shiny new data catalog was introduced. It looked great on first impression. A handful of power users were super excited about the catalog and its potentials. They were thrilled about their new-found visibility into the whole data ecosystem and the endless opportunities to explore new data. They were optimistic that this was indeedThe Solution they’ve been looking for.
A few months after launching, you started noticing that the user engagement waned quickly. Customer’s questions in your data team’s Slack channel didn’t seem to go away either. If anything, they became even harder for the team to answer.
So what happened?
Is it really that hard to find the right data even with such advanced search capabilities and all the rich metadata? Yes! Because the answer to “what’s the right data” depends on who you are and what use cases you’re trying to solve. Most data catalogs only present the information from the producer’s point of view but fail to cater the data consumers.
Providing the producer’s point of view through automation and integration of all the technical metadata is definitely a key part of the solution. However, the consumer’s point of view—trusted tables used by my organization, common usage patterns for various business scenarios, impact from upstream changes have on my analyses—is the missing piece that completes the data discovery & understandability puzzle.
Most of the data catalogs don't help users find the data they need; they help users find someone to pester, which often referred to as a “tap on the shoulder”. This is not true self-service.
We believe that there are three types of information/metadata required to make data discovery truly self-serviceable:
It should be fairly clear by now that discovering the right data and understanding what it means is not a mere technical problem. It requires bringing technical, business and behavioral metadata together. Doing this without creating an onerous governance process will boost your organization’s data productivity significantly and bring true data-driven culture to your company.
We will be sharing more thoughts on how to solve the data discovery challenge in a practical and effective way. If you are interested in learning more, follow us on Twitter and LinkedIn, or simply drop me a line at email@example.com.