A show made for analysts: data, data analysis, and software.
Dear Analyst
Claim This Podcastby KeyCuts
4.0(6 reviews)
20 episodes
Updated Weekly
Accepts GuestsHas SponsorsLocation 🇺🇸
42
Podcast Authority
BetaFairBased on show quality, social media presence, reviews, charts, and more
Quality18
Social0
YouTube86
Engagement77
Podcast Overview
A show made for analysts: data, data analysis, and software.
Language
🇺🇲
Publishing Since
2/27/2023
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42
Podcast Authority
BetaFairBased on show quality, social media presence, reviews, charts, and more
Quality18
Social0
YouTube86
Engagement77
5
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3
Good Performance
11
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excellent
Episode Length
15 minutes
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good
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20 episodes over 2.1 years
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Every 38 days
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Recent Episodes
March 26, 2025
Dear Analyst #134: How Stephen Follows used AI to decode the Oscars and film data to debunk the film industry
<br /> Ever wondered if Hollywood's magic is just smoke and mirrors, or if there's actually some cold, hard data behind those blockbuster hits? I sat down with <a href="https://www.linkedin.com/in/stephenfollows/">Stephen Follows</a>, the film data analyst who's basically Indiana Jones, but instead of chasing ancient artifacts, he's digging up the buried truths of the movie industry. We're talking about everything from Oscar speech patterns to why producer experience might be as useful as a screen door on a submarine. If you've ever wondered if the film industry runs on spreadsheets or gut feelings, you're in for a treat. Stephen's journey is a fascinating blend of film school ambitions, Guinness World Records projects, and a relentless pursuit of the hidden patterns behind Hollywood's stories.<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> From Film School to Data Nerd: A Journey of Discovery <br /> <br /> <br /> <br /> Stephen's career path took an unexpected turn from aspiring filmmaker to data aficionado. He candidly shared how he felt a lack of intellectual engagement during his early film school days, a stark contrast to the stimulating world of data analysis. <br /> <br /> <br /> <br /> <br /> <br /> I was just not using my brain.<br /> <br /> <br /> <br /> <br /> <br /> This started his journey into more analytical pursuits. While others suggested a path in Philosophy, Politics, and Economics (PPE), with film as a side hobby, Stephen plunged directly into the film industry, establishing a production company and crafting advertisements, including work for non-profits. However, the allure of numbers and logical inquiry was undeniable.<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> He recounted how, two decades into his film career, he realized he was missing the critical thinking aspect. "Twenty years ago, as I got into film, I realized I wasn’t doing any thinking," he explained. "So, I started doing the numbers for films for my friends." This move eventually led to a collaboration with Guinness World Records. Stephen's initial expectation was that film studios would be heavily reliant on data and spreadsheets, but he quickly discovered that most industry decisions were surprisingly intuitive, rather than data-driven. <br /> <br /> <br /> <br /> <br /> In the first 10 years of doing numbers work, I thought I’d see people at studios using spreadsheets and doing data. But most decisions are not data-driven.<br /> <br /> <br /> <br /> <br /> Excel, AI, and the Oscars: A Technological Toolkit <br /> <br /> <br /> <br /> Stephen's approach to data analysis is a blend of Excel's robust functionality and the burgeoning power of AI, a skill set he's honed through self-learning. Despite identifying as dyslexic, which posed challenges with traditional coding, he's leveraged his strong logical abilities to navigate these tools effectively. "I’m good at logic but dyslexic, so I can’t code," he clarified. Before AI became mainstream, he relied on online tutorials to enhance his Excel proficiency. His early exposure to AI, through a ChatGPT-assisted scriptwriting project, revealed its potential to bridge gaps in his data knowledge. <br /> <br /> <br /> <br /> <br /> I realized I could use AI to fill in the gaps in my knowledge about data. I’ve increased my output 20 times. I can study all films and not just a subset of films.<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> His Oscars blog post titled <a href="https://stephenfollows.com/p/harvey-weinstein-thanked-more-than-god">Was Harvey Weinstein thanked more often than God at the Oscars?</a> analyzed a dataset we don't normally come across every day: Oscar speeches. It involved analyzing 2,000 acceptance speeches and highlighted the industry's focus on storytelling, even in its own narratives. <br /> <br /> <br /> <br /> <br /> The film industry is all about telling stories about the stories they are telling. <br /> <br /> <br /> <br /> <br />
December 2, 2024
Dear Analyst #133: Find or check if a cell contains text from a list of values or partial matching text in a list (3 methods)
Excel and Google Sheets expert, Ben Collins, shares three methods to check if a cell contains text from a list of values or partial matching text in a list.
September 10, 2024
Dear Analyst #132: How the semantic layer translates your physical data into user-centric business data with Frances O’Rafferty
<br /> When you think of your data warehouse, the "semantic layer" may not be the first thing that pops in your mind. Prior to reading <a href="https://www.linkedin.com/in/franbarrett">Frances O'Rafferty</a>'s <a href="https://moderndata101.substack.com/p/semantics-and-data-product-enablement">blog post</a> on this topic, I didn't even know this was a concept that mattered in the data stack. To be honest, the concept is still a bit confusing to me since I'm not building data warehouses and data products all day. Frances grew up in northern England studying mathematics during the recession. The decision to jump into data was a function of what jobs happened to be available at the time. Frances worked through a variety of data warehousing, BI, and ETL roles before looking more into the data management space like data modeling and cataloguing. This conversation is a deep dive into the world of data warehousing, data catalogues, and of course, the data semantic layer.<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> Enforcing data warehouse conformity for an insurance company<br /> <br /> <br /> <br /> Imagine an insurance company where the policies are in two different systems. Which database contains the "right" policy for a customer? This is the mess Frances had to deal with when she helped build out the insurance company's data warehouse. What I thought was interesting is Frances' team looked at the source data and then interviewed people in the business to understand how the data is generated and how the data is being used. The questions she was asking were pretty high-level:<br /> <br /> <br /> <br /> <br /> * What do you do on a day-to-day basis?<br /> <br /> <br /> <br /> * What works well and doesn't work well?<br /> <br /> <br /> <br /> * What would you like the data to do?<br /> <br /> <br /> <br /> <br /> Source: LinkedIn<br /> <br /> <br /> <br /> Data quality validation checks and global lookups were set up so that if a new piece of data entered the warehouse and it didn't match, then the administrator would get an alert. They would then have to figure out what to do with that rogue piece of data to fit the rules that have been set up.<br /> <br /> <br /> <br /> A methodology Frances brought up I've never heard before is the <a href="https://www.kimballgroup.com/data-warehouse-business-intelligence-resources/kimball-techniques/dw-bi-lifecycle-method/">Kimball methodology</a> for setting up a data warehouse or BI system. The main tenets of the methodology are basically how modern data warehouses are setup: add business value, structure data with dimensions, and develop the warehouse iteratively. This is an image of the lifecycle from their website:<br /> <br /> <br /> <br /> Source: Kimball Group<br /> <br /> <br /> <br /> Focusing on different layers of the warehouse "stack"<br /> <br /> <br /> <br /> Frances' team first focused on the data source layer and tried to figure out where all the data came from. After that, then came the consolidation layer. That consolidation layer is where the data gets split into facts and dimensions. <br /> <br /> <br /> <br /> I figured even for a data warehouse project, Excel must come into play at some point. Excel was used fro all the modeling to figure out what the dimensions and facts were. It wasn't a core part of the warehouse but it was still a one-time use tool in the development of the warehouse.<br /> <br /> <br /> <br /> The final layer is the target layer where we are getting more into the business intelligence realm. There are different ways the insurance company wanted to see the data. So Frances team had to create different views of the data to answer questions like: What premiums have we received? What transactions have come through? The actuarial team wanted to see what the balance was on an account so another view was created for them. <br /> <br /> <br /> <br /> Frances noticed that different regions would call the data different things but they were all s...
20 total episodes available
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Frequently asked questions
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- What is Dear Analyst?
- How often does this podcast release new episodes?
This podcast updates weekly.
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This podcast is available on 10 platforms including Apple Podcasts, Spotify, and more. You can also use the RSS feed directly.
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No, this podcast does not typically feature guests.
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