Are These 4 Companies on Your Radar for Data Governance?
An inside look at how Monte Carlo, Atlan, Castor, and dbt Labs (and more!) landed on my personal radar for data governance solutions.
When it comes to data engineering, specifically data governance, it’s critical to stay on top of change, whether that’s a new approach, architecture, technology, etc. In my previous article on the 5 Reasons Why I’m Paying Attention to Data Governance (And You Should Be, Too), I explore how data governance reappeared on my radar and why it’s a topic worth revisiting — I do recommend starting there if you haven’t read it already.
Many times, these changes are led by companies that are embracing the unknowns to drive innovation and make significant contributions to the data community. I sincerely enjoy technology and actively make it a priority to keep a pulse check on emerging topics and companies that are focused on data. Recently, I’ve noticed an increase in the demand for data governance solutions which could be leading you to a tipping point.
So what does it take to get on my radar, and what do these companies have to do with data governance? Well, these four companies that I’m going to walk you through are driving innovative solutions that often relate to a category, or pain point, of data governance. Let’s dive into the companies on my list.
Who’s on My Radar?
There’s a lot to like in the data space. As opposed to profiling everyone, I did want to provide some additional details on just a few that I’ve been impressed with, especially those that focus on the modern data stack and making significant contributions to the data community overall. As you continue reading, look out for “Kelly’s Data Community Contribution Score” — this is my personal metric that I’ve included for each one based on how they rank.
Is there another company I should know about? I’ll likely come back in a future post to highlight more, so make sure to leave a comment with your suggestion.
Data Observability & Data Monitoring
- Focused on determining where data “breaks” and providing recommendations to remedy the problem freeing up valuable data engineering time and enabling a great degree of data “trust” and data “usability”.
Monte Carlo
Data observability is a new category that the team at Monte Carlo has been providing education and awareness on since they started back in 2019. Their focus is to help increase trust in data by eliminating data downtime — check out their super useful cost of data downtime calculator.
For more perspectives and insights about Monte Carlo, listen to Barr Moses, Co-Founder & CEO, on a recent episode of the Hashmap on Tap podcast. Additionally, they’ve made three chapters available for free in a new O’Reilly book, Data Quality Fundamentals, which you can access and download here.
About Monte Carlo:
- HQ: San Francisco, CA
- Founded: 2019
- Employees: ~100
- Status: Private
- Customers Include: ASICS, Fox, Vimeo, Affirm, Shipt, PagerDuty, AutoTrader
- Funding: $101M over 4 rounds with the most recent a $60M Series C in Aug 2021
- Pricing: Not available
- All things Monte Carlo: montecarlodata.com
Kelly’s Data Community Contribution Score: A
Other solutions in this category worth taking for a spin include:
Metadata Management & Data Catalog
- Focused on documenting and usability so that data consumers spend less time finding and understanding data and can focus on business outcomes and collaboration. Look for “data workspaces” here also.
Atlan
You’ll see Atlan talking about the benefits of a “Modern Data Workspace” which has key components such as a Data Catalog, Data Quality/Profiling, Data Exploration, and Data Lineage. Their focus, since being founded in 2018, is democratizing data and growing a highly collaborative data community.
In my previous article, I mentioned that I highly recommend their newsletter, Metadata Weekly. Along with that, check out their DataOps culture code, which is part of Atlan’s documentation. You can also hear Prukalpa, Atlan Co-Founder, on an episode of Hashmap on Tap. I enjoyed the discussion and her thoughts on what it means to think about data as a product instead of a service.
They do provide both a guided demo and free trial available here.
About Atlan:
- HQ: 100% Remote
- Founded: 2018
- Employees: ~100
- Status: Private
- Customers Include: Juniper Networks, Postman, Plaid, Unilever, Monster
- Funding: $19M over 2 rounds with the most recent a $16.5M Series A in May 2021
- Pricing: 3 Tiers: Starter $2,500/mo, Premier $5,000/mo, and Enterprise “custom” price
- All things Atlan: atlan.com
Kelly’s Data Community Contribution Score: A+
Castor
Castor is still at a very early stage (pre-seed), but they first got my attention with their blog which features benchmarks, tips & tricks, and other really useful data-focused information. They not only cover just the data governance categories that we’ve covered here, but they also cover other compelling topics like How to Build Your Data Team, Cloud Data Warehousing: The Past, Present, and Future, Famous Stacks, Modern Data Stack 101 Guide, and a Reverse ETL benchmark. Their focus since founding the company in 2020 is ensuring that data is documented and discoverable by everyone in your team.
Tristan Mayer, Co-Founder, and CEO of @Castor-App joined me on episode #89 of Hashmap on Tap to discuss why Castor was started, the unique way they approach data discovery and cataloging, and where they are going in the future.
Check out Castor’s quick and easy guided trial here.
About Castor:
- HQ: Paris, France
- Founded: 2020
- Employees: ~15
- Status: Private
- Customers Include: Not disclosed
- Funding: $2M pre-seed
- Pricing: 3 Tiers: Free $0/mo, Premium $699/mo, and Enterprise “custom” price
- All things Castor: castordoc.com
Kelly’s Data Community Contribution Score: A+
Other solutions in this category include:
Data Discovery & Data Lineage
- Focused on search and identification of key datasets potentially with the application of models to assist (discovery) and ensuring that data from acquisition to consumption is mapped and understood (lineage).
dbt Labs
We started working with dbt Labs in 2018 (the founding company was Fishtown Analytics, now known as dbt Labs), and we were immediately attracted to their open-source model. Their developer tier is free forever, and more importantly, they have a strong focus on building and contributing to a thriving data community. They aren’t a secret anymore since they are used by over 5,000 organizations, and they boast the world’s largest analytics engineering community with over 23,000 members. Pretty incredible right?
You can see why I’m profiling dbt here since they have provided a great way to track data lineage and provide documentation alongside each data transformation in your cloud data platform and not to mention, the data quality bump you get from using dbt (schema tests, out-of-range value flags, data freshness checks, etc.). They’ve been doing that since the early days and continue to enhance and improve that capability.
Below is an example of a dashboard status tile that is driven by the dbt Metadata API. Every time dbt runs a project, it generates metadata on accuracy, recency, configuration, and structure of the views and tables in the warehouse:
You won’t go wrong subscribing to the Analytics Engineering Roundup which Tristan Handy, CEO at dbt Labs, posts each week. He also has an interesting bi-weekly podcast that he hosts with Julia Schottenstein in which they talk with data practitioners that are inventing the future of analytics engineering — check it out on Spotify.
I also recommend listening to Tristan’s perspectives that he shared with me in episode #86 of Hashmap on Tap. We explored how dbt Labs started, where they are today, and how they built such a large community of data practitioners.
About dbt Labs:
- HQ: Philadelphia, PA
- Founded: 2016
- Employees: ~120
- Status: Private
- Customers Include: Canva, GitLab, Hubspot, JetBlue, Kickstarter
- Funding: $192.4M over 3 rounds, their most recent round raised $150M of Series A funding in June 2021, and a recent article in Forbes stated that dbt Labs is in discussions to raise a D round at a $6B valuation
- Pricing: 3 Tiers — Developer $0/mo, Team $50 dev/mo, and Enterprise “custom” price
- All things dbt: getdbt.com
Kelly’s Data Community Contribution Score: A+
One Final Question
I hope these perspectives, category breakdowns, and suggestions were helpful as you look to advance your data programs and initiatives. With these in mind, I’d like to circle back to the starting question and ask you:
Which of these “governance” solution categories has already been fully solved and implemented successfully within your organization?
- Metadata Management & Data Catalog
- Data Observability & Data Monitoring
- Data Discovery & Data Lineage
- Data Quality & Data Usability
- None of the Above
- All of the Above
If you’d like, leave a comment with your answer, any feedback, or other companies that stand out to you — I look forward to hearing what you have to say!
Additional Resources
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Kelly leads the Go To Market team (sales, marketing, and alliances) at Hashmap, an NTT DATA Company, and is also the host of Hashmap on Tap, Hashmap’s weekly podcast. He’s been in the data game for over 30 years and prior to Hashmap spent time at both Hortonworks and Oracle. You can connect with Kelly on LinkedIn and follow him on Twitter.
