This week we chat with Grant Grigorian, a long-time friend of mine and founder of Mogi.ai, to explore whether AI is actually a game-changer for data analytics.

We examine what’s broken in the current landscape of data analytics, the complex world of marketing attribution, and the “last mile of analytics” problem (that is., how do you get business users to actually read and use the analyses you produce).

Then we dive very deep 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 Guest

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.

https://www.linkedin.com/in/grantgrigorian/

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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Resource Links

Mogi.ai

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