Episode #50: Is AI a Game-Changer for Data Analytics? - Grant Grigorian
How AI can help solve the last mile problem for data teams
Much of the discussion around generative AI focuses on it’s ability to analyze or produce language.
But what about using LLMs for quantitative analysis? Can it produce the sort of insights that an expert human analyst can?
The answer is more complicated than it seems, according to this week’s guest, Grant Grigorian.
Grant is a veteran ops pro and serial entrepreneur. I first met Grant about ten years ago, when he showed me his first app, Path to Scale. It was a very elegant solution for one of the hardest problems in marketing: funnel tracking and attribution.
Path to Scale was eventually acquired by Engagio (itself acquired by Demand Base), and now Grant is back in the founder’s seat, this time looking at how AI can be used to solve what he calls the “last mile problem” in analytics—namely, how to get people to actually understand and act on all their fancy dashboards.
If you’re looking to go deep into how AI can work for data analysis (and by extension, learn a lot about how to use AI more generally), you’ll love this discussion.
We examine what’s broken in the current landscape of data analytics and the complex world of marketing attribution. We then dive into practical applications of AI for data analysis, looking at concrete examples to highlight the strengths and weaknesses of large language models (LLMs) and contrasting them with traditional machine learning approaches.
In closing, Grant reflects on the future of data careers in the age of AI.
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About Today's Guests
A startup guy who loves B2B marketing technology, analytics, and AI, Grant Grigorian is a serial entrepreneur. Coming from an operations background, Grant founded Path to Scale, a marketing analytics company, that sold to Engagio (later acquired by DemandBase). Today he's the CEO and co-founder of Mogi, an app that simplifies marketing data analysis by automatically delivering insights and recommendations to your team.
Key Topics
[00:00] - Introduction
[01:06] - What’s broken in data analytics
[08:45] - Using data to justify marketing’s existence
[18:15] - Analyzing marketing tactics
[24:39] - The “last mile of analytics” problem
[28:56] - Strengths and weaknesses of LLMs to extract insights from data
[45:49] - Difference between LLMs and machine learning
[50:37] - The importance of context for AI
[54:39] - Impact of AI on data careers
[1:00:07] - Motivations as a repeat founder
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