We’re thrilled that DataChat has been acquired by Mews
We’re excited to integrate the DataChat team into the Mews family and can’t wait to continue our collaboration in the coming months and years.
AI has evolved quickly over the past decade or so to meet the diverse needs of finance departments.
The best sales people aren’t robots. They understand the challenges their customers face, and know what they value the most.
Many marketing departments are drowning in data, but this doesn’t always mean they’re swimming in insights.
At DataChat, we’ve come up with a different way to Moneyball a business: use AI to give everyone in an organization the skills of a quantitative analyst and data scientist.
DataChat’s Traceability One of DataChat’s key differentiators is traceability. Our platform automatically documents every step it takes to produce your analytics, in plain English. That way anyone can verify what DataChat did, regardless of their technical knowledge. We think this traceability is crucial to making data-driven decisions confidently. If you don’t know how the proverbial… Continue reading Unnesting the Nest: The DataChat Flattening Technique to Humanize Complex Analytics
Rogers Jeffrey Leo John, Ph.D. co-founded DataChat in 2018 and today serves as Chief Technology Officer. In this interview, Rogers talks about the origins of DataChat and how it has transformed the workflows, audience, and impact of data analytics. How did DataChat come to be? The concept of DataChat formed in 2016 after Jignesh Patel,… Continue reading The DataChat backstory: A Q&A with co-founder Rogers Jeffery Leo John
In June, we had the privilege of presenting our groundbreaking research paper, “DataChat: An Intuitive and Collaborative Data Analytics Platform,” at the SIGMOD conference. Our paper introduced the DataChat platform, designed to provide an intuitive, powerful, and accessible data science approach to all users. Since its unveiling, our approach has garnered substantial interest from various industries, prompting… Continue reading Enhancing Data Analytics with DataChat: A Look at Recent Improvements
In data science, we want to avoid GIGO, or Garbage In, Garbage Out. In other words, your output can only be as good as your input. We build models with machine learning to make decisions based on the model’s predictions – for competitive advantage or to anticipate behavior, for example. For more trustworthy results, we… Continue reading 5 Tips for Preparing Your Data for Machine Learning
