Jay Hormes

Background

Jay has worked in data analytics for more than 15 years at several technology companies, managing data analytics and data science teams. Along the way, Jay has worked with business teams across each organization, including Marketing, Sales, Account Management, Operations, Product and Customer Support, helping them make better decisions with data.

Most recently, the analytics team Jay led had the same problem as most data teams: too many requests, too few people. Every question became an analysis or a dashboard, the work spilled over to data engineering, and many stakeholder requests never got done.

We tried out-of-the-box AI tools. The big step forward came when we built our own, made for our data and our way of working. Quality got special emphasis: the AI was taught the checks the team had developed by hand over the years, and whatever worked on one project became the standard on the next.

Since then, AI has become the team's main enabler: the team can serve its stakeholders faster and better. AI brought data analytics, data science and data engineering much closer together. All three now work from the same tools and context, which made them more efficient and collaboration significantly better. Data science builds better models from the analytics team's AI outputs, and data engineering sees more easily where work is needed.

The result: about four times the output per analyst, measured the same way as before AI. AI now runs on every project, and the data chatbot cut ad-hoc requests by 90%.

Grounded Analytics was started because getting that AI journey going was challenging: the team had several false starts and made mistakes along the way. We had to figure out much of it ourselves. With what we know now, AI adoption can go significantly faster, shortening the process from months to weeks. That saves both time and money.