Last year, we examined the wonder that is pumpkin spice, to learn what foods contained actual pumpkin spices. It’s been a year since then; what’s changed, if anything? Market Interest First, before we dig in, let’s look at pumpkin spice itself. The topic of pumpkin spice (or more accurately, pumpkin pie spice), is a perennial […]
Category: data science
{PODCAST} In-Ear Insights: When Algorithm Choices Go Wrong
In this week’s In-Ear Insights, Katie and Chris discuss what happens when algorithm choices go wrong. What happens when junior or naive AI engineers or data scientists make bad choices for algorithms. Using an example from a writing analysis website, we discuss what went wrong, what an appropriate choice should have been, and why it’s […]
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{PODCAST} In-Ear Insights: Should AI Adopt a Clinical Trials Process?
In this week’s In-Ear Insights, Katie and Chris discuss the current state of AI deployment. Companies are rushing ahead to put models and algorithms into action with little to no due diligence, and the consequences can be disastrous. Should AI adopt a practice similar to clinical trials, where a model must prove that it causes […]
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{PODCAST} In-Ear Insights: Data-Driven Decisions and Social Good
In this week’s In-Ear Insights, Katie and Chris look at the upcoming release of hate crime data analysis and broader implications. When data collection is flawed or missing, how do you still make decisions? What do you do when you have to do something because lives are on the line, but you lack enough data? […]
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{PODCAST} In-Ear Insights: Math Skills for Marketing Analytics
In this week’s In-Ear Insights, Katie and Chris discuss math. Specifically, what are the math skills that marketers of all stripes, but especially those who have an interest in marketing analytics, marketing data science, and machine learning/AI applied to marketing need to know. Do you need a Ph.D.? Do you need to spend a year […]
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{PODCAST} In-Ear Insights: Transparency in Research and Data
In this week’s In-Ear Insights, Katie and Chris discuss the importance of transparency and disclosure in research and data. These are troubled, uncertain times – not for the macro situation, but for our willingness to collectively accept statements of fact without verifying those facts or inspecting the methodology behind a piece of data. They also […]
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5 Ways Your AI Projects Fail, After Action Reviews and Post-Mortems
Introduction The recurring perception that artificial intelligence, AI, is somehow magical and can create something from nothing leads many projects astray. That’s part of the reason that the 2019 Price Waterhouse CEO Survey shows fewer than half of US companies are embarking on strategic AI initiatives – the risk of failure is substantial. In this […]
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{PODCAST} In-Ear Insights: Early Warning Systems for Marketing Analytics
In this week’s In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss early warning systems. When looking at your analytics, what metrics constitute leading, realtime, or lagging indicators, and how should you think about using them to determine when trouble is brewing? Learn about what exponential math is, how it factors into trend detection, […]
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The Importance of Tag Memes for Understanding Social Networks
This past week, our CEO Katie Robbert said, “can we be done with the “something about me and tag five people” on twitter thing? it’s really annoying now“. If you’re unfamiliar with the reference, there’s a Twitter meme where you share 5 jobs you’ve held, and then tag 5 people: These tag memes at first […]
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{PODCAST} In-Ear Insights: Predictive Analytics for Unpredictable Events
In this episode of In-Ear Insights, Katie and Chris respond to a listener question: how valid are predictive analytics forecasts when you’re in the middle of massively unpredictable events? How do you deal with the anomalies of a black swan event, and how do you tell the difference between an anomaly and a breakout (continuing […]
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