Today, the team and I are excited to share what we’ve been working on for the past year. We’re launching our invite-only release of 🧪Dataframe, the simplest Data Discovery and Documentation tool for your data warehouse.
About a month ago, we launched 🐳 Whale, our terminal-centric open-source data discovery tool, dubbed “the stupidly simple data discovery tool”. We were pleasantly surprised by the love and support we received from the data community - we surpassed 350 stars on Github and gathered a tightly-knit group of active contributors and early adopters.
In my career as a data scientist, I’ve built data science and machine learning products for dozens of Global 1000 companies, where my products impacted billions of dollars in revenues and uplifted hundreds of millions of dollars of additional profits. Through these experiences, I saw that through machine learning, data teams everywhere were starting to become recognized as competitive assets for their organizations, rather than back-office units. I experienced first-hand the start of what will be a transformative shift in how organizations work with their data over the next decade.
But through these same experiences, I also found that one of the largest barriers to the adoption of data science and machine learning in organizations is the lack of data context - the who/what/when/where/how/why of data. To verify that my own struggle was not an isolated case, I surveyed 100 data scientists and data engineers - 95% of whom said they have experienced the pain arising from the lack of data context in their organization, and 63% of whom said the current way of searching for and understanding data was from asking co-workers. A tool for Data Discovery and Documentation bridges the gap between data generation and data consumption. More specifically, it enables users to answer questions such as who created this data, what are the columns of this data, when was the data created, where is this data, how can I access this data, and why was this data created. This is where Dataframe comes in.
We believe that great tools for Data Discovery and Documentation will accelerate the data transformation of the world economy and unlock trillions of dollars of value for companies and the consumers that these companies serve. We also believe that the correct patterns for data discovery and data documentation will be as fundamental as the patterns that git and Github established for code workflows. We’ve built our platform following these beliefs, which we hope you’ll find not only valuable, but delightful to use.
This launch is but a small step in our journey to fulfill our mission: to democratize data science for every organization.
Sign up for the waitlist at dataframe.ai, and join us on our quest to unlock #DataProductivity.
Co-founder & CEO, 🧪Dataframe