Josh Gray from Artemis

Data Adoption

Artemis tackles the complex challenges of data adoption across various industries, with a particular focus on healthcare and B2B SaaS. Josh Gray explains that while data structures may be similar, the use cases differ significantly, making it difficult to scale data cleaning processes 1. He emphasizes the importance of high-quality data for AI tools, as poor data quality can lead to inaccurate AI outputs 2.

We think it's the opposite. We think, I'm sure if you follow Aaron Levy. Levy. I forget that his last name, but the fact box. He put out a great tweet essay about three, four weeks ago where he talks about there's always more work to do. And allowing your engineers or analysts to do 50% more means they can get 50% more done faster, which means you can hire maybe more people and so on and so forth.

--- Josh Gray

Healthcare, traditionally slow to adopt new technologies, is now more open due to the economic incentives provided by AI advancements 1.

   

Data Quality

The value of data lies in its quality, a principle that Josh Gray learned through his experience in financial markets and alternative data 3. He highlights the transition from Guelph Box to Artemis, driven by the realization that data cleaning is the most challenging aspect of data analytics 3.

I learned a ton and learned, really, the key learning there was the value of the data set lies in this quality. How do you take a data set from an expense line on your income statement to a revenue generated asset is in the quality that you can resell or derive value from in your business to play on that competitive insight.

--- Josh Gray

Gray's journey involved self-education in data infrastructure and collaboration with his co-founder, who shared similar experiences from the engineering side 4.