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In-Ear Insights: Limitations of Generative Analytics
In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the limitations of generative analytics, particularly for casual users. Discover why subject matter expertise is still essential for accurate data analysis, even with these powerful tools. Learn practical strategies for determining if a task is suitable for generative AI and how to incorporate it effectively into your analytics workflow. Finally, understand the importance of process mapping in maximizing the benefits and mitigating the potential challenges of using generative AI for analytics.
Watch the video here:
Can’t see anything? Watch it on YouTube here.
Listen to the audio here:
https://traffic.libsyn.com/inearinsights/tipodcast-limitations-of-generative-analytics.mp3
Download the MP3 audio here.
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Machine-Generated Transcript
What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.
Christopher S. Penn – 00:00
In this week’s In Ear Insights, this is part two, the cliffhanger resolution from last week’s episode on generative analytics. So, we talked last week about generative analytics, the use of generative AI tools to enable your analytics data. We’ve been sitting around for decades with data, and it just sits there. We do nothing with it. Generative AI tools give us the ability to give this data to tools and then have them tell us “so what?” and “now what?”. But, Katie, you said at the end of last week’s episode, therein lies the challenge: thinking to ask a question, knowing what to ask the machine so you’re getting the right information. I think a lot of us have already made the assumption, “Well, generative AI must have the right answer.” How do we find out before we give them all of our data how do we find out whether or not it even has the right information or the right knowledge so that we can use it to do insightful analysis?
Katie Robbert – 00:53
Yeah, and I stand by those questions. It’s interesting. It’s not just for generative analytics; it’s really for anything. But, if we focus in on generative analytics, my concern is — or my question is — if you’re going to start using large language models to help you with your analysis and insights, how do you know it’s right? This goes back to something we always talk about: you have to have some kind of subject matter expertise to validate that the machines are giving you the right information. That’s not something you can just, like, wake up and be like, “Oh, and I’m a subject matter expert.” Like, those are things that take a long time, a lot of practice, a lot of making mistakes, a lot of trial and error, and just a lot of experience. I think what I’m concerned is going to happen is that companies maybe don’t have the budget or aren’t willing to spend the budget on an analytics team, on a data analyst, on data governance — whatever the thing is — and they’re just g
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By Trust Insights
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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the limitations of generative analytics, particularly for casual users. Discover why subject matter expertise is still essential for accurate data analysis, even with these powerful tools. Learn practical strategies for determining if a task is suitable for generative AI and how to incorporate it effectively into your analytics workflow. Finally, understand the importance of process mapping in maximizing the benefits and mitigating the potential challenges of using generative AI for analytics.
Watch the video here:
Can’t see anything? Watch it on YouTube here.
Listen to the audio here:
https://traffic.libsyn.com/inearinsights/tipodcast-limitations-of-generative-analytics.mp3
Download the MP3 audio here.
[podcastsponsor]
Machine-Generated Transcript
What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.
Christopher S. Penn – 00:00
In this week’s In Ear Insights, this is part two, the cliffhanger resolution from last week’s episode on generative analytics. So, we talked last week about generative analytics, the use of generative AI tools to enable your analytics data. We’ve been sitting around for decades with data, and it just sits there. We do nothing with it. Generative AI tools give us the ability to give this data to tools and then have them tell us “so what?” and “now what?”. But, Katie, you said at the end of last week’s episode, therein lies the challenge: thinking to ask a question, knowing what to ask the machine so you’re getting the right information. I think a lot of us have already made the assumption, “Well, generative AI must have the right answer.” How do we find out before we give them all of our data how do we find out whether or not it even has the right information or the right knowledge so that we can use it to do insightful analysis?
Katie Robbert – 00:53
Yeah, and I stand by those questions. It’s interesting. It’s not just for generative analytics; it’s really for anything. But, if we focus in on generative analytics, my concern is — or my question is — if you’re going to start using large language models to help you with your analysis and insights, how do you know it’s right? This goes back to something we always talk about: you have to have some kind of subject matter expertise to validate that the machines are giving you the right information. That’s not something you can just, like, wake up and be like, “Oh, and I’m a subject matter expert.” Like, those are things that take a long time, a lot of practice, a lot of making mistakes, a lot of trial and error, and just a lot of experience. I think what I’m concerned is going to happen is that companies maybe don’t have the budget or aren’t willing to spend the budget on an analytics team, on a data analyst, on data governance — whatever the thing is — and they’re just g
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