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Kevin Hu, PhD
Chief SQL Wrangler & CEO at Metaplane | MIT research | Data-informed posts about data
Kevin Hu, PhD is the Co-founder and CEO of Metaplane, a data observability platform that helps companies ensure trust in their data.12 He is a researcher-turned-founder with a mission to improve data quality and reliability for modern data teams.13
Background and Education
Kevin holds multiple degrees from the Massachusetts Institute of Technology (MIT):
- Doctor of Philosophy (Ph.D.) in automated data visualization and semantic type detection (2015-2019)1
- S.M. in Data visualization and machine learning (2013-2015)1
- S.B. in Physics (2011-2013)1
He also participated in Y Combinator's W20 program in 2020.1
Professional Experience
Before founding Metaplane in June 2019, Kevin's experience includes:
- Doctoral Candidate at MIT (2015-2019), focusing on automating data science systems using machine learning1
- Masters Candidate at MIT (2013-2015), working on visualization and mapping subjective experiences1
Metaplane
Under Kevin's leadership, Metaplane has:
- Raised $13.8 million in a Series A funding round in March 20243
- Developed a platform used by companies like Bose, ClickUp, and Klaviyo3
- Focused on providing data observability solutions that detect and prevent data issues before they become significant problems23
Kevin is known for sharing insights about data engineering, data quality, and startup experiences on professional platforms like LinkedIn.14
Highlights
When does summer end for you? Some say the fall equinox, though I'm in the "when kids go back to school" category.
In any case, better late than never, I'm happy to share that Metaplane is again a G2 Leader in the Data Observability category!
We're also #1 for every supporting metric: Best Relationship, Best Usability, Best Results, and Most Implementable.
Thank you to our customers for continually raising the bar. You're the reason we do what we do — Metaplane exists to save you time and preserve trust.
And we have a lot more cooking in the kitchen :) so stay tuned.
#dataengineering #analytics #dataquality
What happens when the root cause of data quality issues isn’t properly fixed?
The frequency and severity of downstream business-facing issues continue to compound as data complexity increases.
Teams spend more and more time managing the symptoms, which provides temporary relief. Sometimes, new technologies are applied on top like bandaids without first stemming the bleeding.
Technology is necessary but insufficient. You need to invest in your people, process, and culture in tandem with getting the right technologies in place:
• How can your teammates help you define data quality metrics and play their part in ensuring high-quality data? • How can your organization implement business processes that provide guardrails for data input by either machines or humans? • How can you properly manage cultural expectations around those metrics and processes, towards a shared responsibility model?
Fix the people, process, and culture — fix the data.
#dataengineering #dataquality #dataculture
