Creating a Space for Technical and Business Users to Collaborate on Data
The Active Metadata Pioneers series features Atlan customers who have recently completed a thorough evaluation of the Active Metadata Management market. Paying forward what you’ve learned to the next data leader is the true spirit of the Atlan community! So they’re here to share their hard-earned perspective on an evolving market, what makes up their modern data stack, innovative use cases for metadata, and more.
In this installment of the series, we meet Thomas Evans, Director, Data Platform at Mindbody + Classpass, who shares how their data stack is the product of multiple mergers and acquisitions, the role a modern data catalog can play in improving technical team understanding of their stack, and how the Data Platform team intends to improve discoverability, documentation, and system optimization with Atlan.
This interview has been edited for brevity and clarity.
Could you tell us a bit about yourself, your background, and what drew you to Data & Analytics?
For me, that’s a pretty winding course. I came up in engineering and manufacturing, got into project management, and then that led me to the tech sector. So I came into the tech and software sector as a PM and was spending a lot of time getting the right data moving to the right places.
Then, I guess I just had too many of the right questions, and that led to me leading our data warehousing team. That has now grown into our data platform team. We’re responsible for the DBA operations of our production platforms, through any of the data movements around our various systems through to our Enterprise Data Warehouse, and out to our many different use cases downstream.
Would you mind describing Mindbody and how your data team supports the organization?
Mindbody, at large, is a SaaS company. We enable the wellness and fitness and health industry largely through the middle market, even up to some of the larger chains that you’ll see, and down into some of the smaller boutique, higher-end fitness businesses, as well.
All of those businesses are running their business on our platform. We connect these businesses and consumers in our marketplace. So we’ve got the consumer leg, with folks coming into the marketplace in need of services, we’ve got the business management side of things, and Mindbody provides the marketplace to connect and enable them all.
The Mindbody data platform team is supporting and ensuring the safe operations of our data movements across our business.
What does your data stack look like?
Snowflake is our data warehouse, and then we do a lot of our loading with either Fivetran or we have in-house built pipelines. These are Python over SQL that we orchestrate with Databricks.
Then we feed downstream into modeling tools like dbt and Airflow, primarily. This results in modeled data and metrics that feed into our BI tools. Looker is our primary Mindbody platform, and we use Tableau on the ClassPass side, so we’ve got two types of operations there. Then we also do a number of things around data delivery back out to our production systems, or providing a data feed to our customers, that we do through a number of different stacks, but it’s all cohesive around Snowflake.
It’s worth mentioning that we are a growing and somewhat disparate company given various mergers and acquisitions over the years. So our production stacks and operations are quite a bit more broad than that. We have to build the connections to these technologies, and at times migrate them into our current stack.
So, having Atlan to give us that awareness of what’s where, how it’s moving, and what’s important to everybody is critical for us to make sure that as we do these moves, we’re doing it safely and we’re doing it in concert with the right people.
Why search for an Active Metadata Management solution? What was missing?
We’re a 20 plus year old company that made it this far without having a data catalog of really any order, and our data glossary behaviors were localized and rather isolated. So as we’re growing and maturing as a company, we’re taking on new use cases, we’re taking on new users, and relying on tribal knowledge pools has been a dangerous game.
We’re at this inflection point now where we want to change that. And so we are specifically looking for the ability to enable our users with this type of information and knowledge, and we want to move away from the tribal network and knowledge practices that we’ve done for the last two decades.
Why was Atlan a good fit? Did anything stand out during your evaluation process?
Our team’s technical review landed with Atlan at the top of our list due to the available features that were offered. The general usability of the tool, from the look and feel, to the ability to move around the tool intuitively meant Atlan met a lot of our needs.
Atlan also provides a common place for technical and business users to collaborate and exchange value around our data in a way that works for both sides. And that was really important to us. We didn’t want a tool that gravitated too heavily on the engineering side and met that group’s needs, but then the business teams felt overwhelmed and confused. Atlan strikes a very nice balance for both groups.
The financial evaluation was also favorable for Atlan. It’s on par with the commitment needed for other tools, but balancing the financial commitments along with the technical offerings is what put Atlan at the top of the list, for us.
What do you intend on creating with Atlan? Do you have an idea of what use cases you’ll build, and the value you’ll drive?
Our top priorities with Atlan are data discoverability and documentation, glossary management, and then moving into system utilization, monitoring and tuning. So this will allow all users of our data to have knowledge of what is available with supporting information, and the ability to suggest and ask for additional information when needed. Again, all in one place. Not in the tribal network, not lost in Slack.
We’ll also consolidate and streamline a number of disparate glossary resources that will aid in understanding, clarity, and alignment across teams and use cases. So instead of having to ask, “Are you talking about the BI glossary and the terms contained in it, or are you talking about the RevOps glossary and the terms contained in it?” which may or may not have been the same terms, or may or may not have meant the same thing, we’ll now have one enterprise glossary that everyone can gravitate around.
On the utilization and monitoring side, Atlan will enable our teams to identify and protect our most used and valued assets while also providing insights to areas that can be deprecated due to low or no usage. This ensures our data is relevant, accurate, and reliable while our systems are cleaned and optimized on a regular basis.
Photo by Sven Mieke on Unsplash
