In this week’s In-Ear Insights, Katie and Chris discuss how to do practices like predictive analytics and classical AI/machine learning when you’re data-poor. What data is available to forecast and work with? How do you create data when you don’t have it, and what strategic advantage might this confer? Tune in to find out!

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What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

In this In-Ear Insights, what do you do with your analytics when you are a data poor? So this is a question that came up when I was on the road recently, I was in Canada at a at a speaking gig doing a talk on predictive analytics, which was kind of fun, because I’ve done that one in a while.

And one of the folks said, I love this, this is this is great.

This makes sense.

Canada as a whole is a data poor nation.

We don’t have the same federal agencies like the US does that provides all this great data.

And it’d be nice to be able to forecast, you know, this industry stuff.

What do we do when we’re data poor, not the individual company, but like the consortiums, the public agencies, they just don’t have data.

So Katie, what do you do when your data poor?

That’s such a good question.

Because I feel like, you know, we take for granted the fact that in the United States, we are not data poor.

If anything, we are overwhelmed with the amount of data out there, our challenge is finding the good quality data that we can really trust.

But there’s no shortage of data that we can use.

So I would say to someone who feels like they are data poor, the first place to start is to try to find something similar enough.

So you know, Can Can these Canadian resources use a proxy? So a proxy being like, can they look at European data or United States data or Australian data? Is it close enough? And so there’s, there’s obviously a lot to unpack with that.

But that would sort of be my first instinct.

You know, Chris, what would you say?

So, a couple of answers I gave was, one, there are data sources that are geographically agnostic that y