Are you down with ITP? What about ETP? Are you pretty sure that the decline in returning visitors to your site that has everyone in a tizzy is largely due to increasingly restrictive cookie handling by browsers? Do you really, really, REALLY want Google, Apple, Mozilla, and even Microsoft to get on the same page when it comes to cookie handling and JavaScript subtleties? So many questions! Lucky for us (and you!), Measure Slack legend (and L.L. Bean Senior Programmer/Analyst) Cory Underwood has some answers. Or, at least, he will depress you in delightful ways. For complete show notes, including links to items mentioned in this episode, a transcript of the show, and an update on ITP 2.3 from Cory, visit the show page.

Have you ever noticed that 68.2% of the people who explain machine learning use a "this picture is a cat" example, and another 24.3% use "this picture is a dog?" Is there really a place for machine learning and the world of computer vision (or machine vision, which we have conclusively determined is a synonym) in the real world of digital analytics? The short answer is the go-to answer of every analyst: it depends. On this episode, we sat down with Ali Vanderveld, Director of Data Science at ShopRunner, to chat about some real world applications of computer vision, as well as the many facets and considerations therein! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

What percentage of digital ad impressions and clicks do you think is actually the work of non-human bots? Pick a number. Now double it. Double it again. You're getting close. A recent study by Pixalate found that 19 percent of traffic from programmatic ads in the U.S. is fraudulent. David Raab from the CDP Institute found this number to be "optimistic." Ad fraud historian Dr. Augustine Fou, our guest on this show, has compelling evidence that the actual number could easily be north of 50 percent. Why? Who benefits? Why is it hard to tamp out? Is it illegal (it isn't!)? We explore these topics and more on this episode! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

It's 1:00 AM, and you can't sleep. The paid search manager needs to know whether brand keywords can be turned off without impacting revenue. The product team needs the latest A/B test results analyzed before they can start on their next sprint. The display media intern urgently needs your help figuring out why the campaign tracking parameters he added for the campaign that launches in two days are breaking the site (you're pretty sure he's confusing "&" and "?" again). And the team running the site redesign needs to know YESTERDAY what fields they need to include in the new headless CMS to support analytics. You're pulled in a million directions, and every request is valid. How do you manage your world without losing your sanity? On this episode, analytics philosopher Astrid Illum from DFDS joins the gang to discuss those challenges. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Somewhere between "welcome to the company, now get to work!" and weeks of tedious orientation sessions (that, presumably, include a few hours with the legal department explaining that, should you be on a podcast, you need to include a disclaimer that the views expressed on the podcast are your own and not those of the company for which you now work), is a happy medium when it comes to onboarding an analyst. What is that happy medium, and how does one find it? It turns out the answer is that favorite of analyst phrases: "it depends." Unsatisfying? Perhaps. But, listeners who have been properly onboarded to this podcast know that "unsatisfying" is our bread and butter. So, in this episode, Moe and Michael share their thoughts and their emotional intelligence on the subject of analyst onboarding, while Tim works to make up for recent deficiencies in the show's use of the "explicit" tag. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Listen. Really. That's what you can do. You can listen to this episode and find out what you learn. Or you can NOT listen to the show and NOT find out what you learn. You can't do both, which means that, one way or the other, you WILL be creating your very own counterfactual! That, dear listener, is a fundamental concept when it comes to causal inference. Smart analysts and data scientists the world over are excited about the subject, because it provides a means of thinking and application techniques for actually getting to causality. Bradley Fay from DraftKings is one of those smart data scientists, so the gang sat down with him to discuss the subject! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Have you ever thought it would be a great idea to have a drink or two, grab a microphone, and then air your grievances in a public forum? Well, we did! This episode of the show was recorded in front of a live audience (No laugh tracks! No canned applause!) at the Marketing Analytics Summit (MAS) in Las Vegas. Moe, Michael, and Tim used a "What Grinds Our Gears?" application to discuss a range of challenges and frustrations that analysts face. They (well, Moe and Tim, of course) disagreed on a few of them, but they occasionally even proposed some ways to address the challenges, too. To more effectively simulate the experience, we recommend pairing this episode with a nice Japanese whiskey, which is what the live audience did! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Did you hear the one about how the AI eliminated cancer? It just wiped out the human race! As machine learning and artificial intelligence are woven more and more into the fabric of our daily lives, we are increasingly seeing that decisions based purely on code require a lot of care to ensure that the code truly behaves as we would like it to. As one high profile example after another demonstrates, this is a tricky challenge. On this episode, Finn Lattimore from Gradient Institute joined the gang to discuss the different dimensions of the challenge! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

What's in a job title? That which we call a senior data scientist by any other job title would model as predictively...  This, dear listener, is why the hosts of this podcast crunch data rather than dabble in iambic pentameter. With sincere apologies to William Shakespeare, we sat down with Maryam Jahanshahi to discuss job titles, job descriptions, and the research, experiments, and analysis that she has conducted as a research scientist at TapRecruit, specifically relating to data science and analytics roles. The discussion was intriguing and enlightening! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Remember that time you ran a lunch-and-learn at your company to show a handful of co-workers some Excel tips? What would have happened if you actually needed to fully train them on Excel, and there were approximately a gazillion users*? Or, have you ever watched a Google Analytics or Google Tag Manager training video? Or perused their documentation? How does Google actually think about educating a massive and diverse set of users on their platform? And, what can we learn from that when it comes to educating our in-house users on tool, processes, and concepts? In this episode, Justin Cutroni from Google joined the gang to discuss this very topic! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

A simple recipe for a delicious analytics platform: combine 3 cups of data schema with a pinch of JavaScript in a large pot of cloud storage. Bake in the deployment oven for a couple of months, and savory insights will emerge. Right? Why does this recipe have both 5-star and 1-star ratings?! On this episode, long-standing digital analytics maven June Dershewitz, Director of Analytics at Twitch, drops by the podcast's analytics kitchen to discuss the relative merits of building versus buying an analytics platform. Or, of course, doing something in between!

The episode was originally 3.5 hours long, but we edited out most of Michael's tangents into gaming geekdown, which brought the run-time down to a more normal length.

For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

We're not sure what's going on with this episode. For some reason, we have a bunch of first-time listeners, and they're all from Apple devices! Maybe it's because the show only comes out every two weeks, and the first-party cookies we've been using to track our listeners are now expiring after seven days! (This is a hilarious episode description if you're well-versed in the ins and outs and ethical and philosophical aspects of WebKit's Intelligent Tracking Prevention (ITP) 2.1. If you're not, then you might want to listen to the gang chat with Kasper Rasmussen from Accutics about the topic, as it's likely already impacting the traffic to your site!) For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Have you ever attended a conference? Did you know that analysts over-index towards introversion? Have you ever struggled to figure out how to start a conversation over a cold pastry and a cup of tepid coffee at a conference breakfast? IS there actually a point in developing and executing a strategy when it comes to attending a conference? Is it annoying to listen to people who speak pretty regularly at conferences pontificate about speaking at conferences? Some of these questions are answered on this episode! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page. We made this up, but it seems plausible.

We thought we deserved a break from the podcast, so we went looking for some AI to take over the episode. Amazon Polly wasn't quite up to the task, unfortunately, so we wound up sitting down as humans with another human -- Erik Driessen from Greenhouse -- to chat about the different ways that automation can be put to use in the service of analytics: from pixel deployment to automated alerts to daily reports, there are both opportunities and pitfalls! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Do you know something that is really simple? Really Simple Syndication (aka, RSS). Did you know that RSS is the backbone of podcast delivery? Well, aren't you clever! What's NOT really simple is effectively measuring podcasts when a key underlying component is a glorified text file that tells an app how to download an audio file. Advertisers, publishers, and content producers the world over have been stuck with "downloads" as their key -- and pretty much only -- metric for years. That's like just counting "hits" on a website! But, NPR is leading an initiative to change all that through Remote Audio Data, or RAD. Stacey Goers, product manager for podcasts at National Public Radio, joins the gang on this episode to discuss that effort: how it works, how it's rolling out, and the myriad parallels podcast analytics has to website and mobile analytics! "For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

"Hey, Google! How do you measure yourself?" "I'm sorry. I can't answer that question. Would you like to listen to a podcast that can?" National Public Radio has long been on the forefront of the world of audio media. Why, you might even remember episode #046, where Steve Mulder from NPR made his first appearance on the show discussing the cans and cannots of podcast measurement! On this episode, Mulder returns to chat about how much more comfortable we have become when it comes to conversing with animated inanimate objects, as well as the current state of what data is available (and how) to publishers and brands who have ventured into this brave new world. "Alexa! Play the Digital Analytics Power Hour podcast!" For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

What does it really take to bring data science into the enterprise? Or... what does it take to bring it into your part of the enterprise? In this episode, the gang sits down with Dr. Katie Sasso from the Columbus Collaboratory...because that's similar to what she does! From the criticality of defining the business problem clearly, to ensuring the experts with the deep knowledge of the data itself are included in the process, to the realities of information security and devops support needs, it was a pretty wide-ranging discussion. And there were convolutional neural networks (briefly). For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

WHERE were you the first time you listened to this podcast? Did you feel like you were JOINing a SELECT GROUP BY doing so? Can you COUNT the times you've thought to yourself, "Wow. These guys are sometimes really unFILTERed?" On this episode, Pawel Kapuscinski from Analytics Pros (and the Burnley Football Club) sits down with the group to shout at them in all caps. Or, at least, to talk about SQL: where it fits in the analyst's toolbox, how it is a powerful and necessary complement to Python and R, and who's to blame for the existence of so many different flavors of the language. Give it a listen. That's an ORDER (BY?)! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Happy New Year! Sure. The ball has dropped in Times Square, and a new year means an opportunity to look forward. But, we wanted to take a quick look back first -- on the industry, on the podcast, and on the world in general. From GDPR to Bayesian statistics to machine learning and AI to... podcast (and #mattgershoffed) stickers, 2018 was, clearly, the Year of the Analyst. So keep analyzing! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Have you ever had stakeholders complain that they're not getting the glorious insights they expect from your analytics program? Have you ever had to deliver the news that the specific data they're looking for isn't actually available with the current platforms you have implemented? Have you ever wondered if things might just be a whole lot easier if you threw your current platform out the window and started over with a new one? If you answered "yes" to any of these questions, then this might be just the episode for you. Adam "Omniman" Greco -- a co-worker at Analytics Demystified of the Kiss sister who is not a co-host of this podcast -- joined the gang to chat about the perils of unmaintained analytics tools, the unpleasant taste of stale business requirements, and the human-based factors that can contribute to keeping a tool that should be jettisoned or jettisoning a tool that, objectively, should really be kept! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

What's the hot new technology of 2018? AI? Deep Learning? Pole-dancing robots? Maybe. Or, maybe it's customer data platforms (CDPs) -- a topic we actually covered way back in January 2017 on episode #053 with Todd Belcher, who, at the time, was with CDP provider BlueConic. Since then, Todd left BlueConic to start CDP Resource, which is, well, a resource for companies looking to select, implement, and maintain a CDP. We asked Todd to come back on the show to give us the rundown on how there is now -- finally -- clarity, consolidation, and maturity in the space, as all of the providers have aligned around a common definition of what a CDP is, what it does, and how it should do it. Alas! The space isn't even remotely there yet! We have yet to even reach the peak of inflated expectations! Which was probably why it was such an informative discussion. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Perspective is a good thing. We've all agonized about a misreported metric or an unsatisfying entry page analysis and had to remind ourselves that we're not exactly saving lives with our work. On this episode, though, the gang actually meanders into life-and-death territory by chatting about one of the uses of data outside of the world of digital marketing and websites and eCommerce: natural disaster preparation and response. Sherilyn Burris from Cascia Consulting joins Michael, Moe, and Tim to chat about her experiences in a variety of roles in just that area, how she uses data, how the data landscape has evolved over the past 15 years, and what she has learned about communicating data to politicians, to the media, and to the general public (which has some intriguing parallels to the communication of data in digital analytics!). For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Remember when you used to keep all of your data packed into data boxes and stacked up on a bunch of data shelves in your state-of-the-art data warehouse? Well, it might be time to fire up the data forklift and haul all of those boxes out of the structured order of your data warehouse and dump them into a data lake so that it can float and sink and swim around in semi-structured and unstructured waters. On this episode, Rohan Dhupelia joins the gang to talk about his thoughts and experiences from engineering just that sort of move at Atlassian. So, pop in your earbuds and strap on your data swim trunks and give it a listen! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

To think, it was barely-considered subtle humor that we used two trailing zeros in episode #001. But, despite our best efforts to destroy our reputations or our livers long before we centupled that original episode, we failed on both fronts, and we now need that third significant digit! For this special episode, we invited listener questions, and our listeners responded. Some of them blew right past the time limit on their questions, but that's okay: we blew (slightly) past the one hour mark for the show. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Are you deeply knowledgable in JavaScript, R, the DOM, Python, AWS, jQuery, Google Cloud Platform, and SQL? Good for you! If you're not, should you be? What does "technical" mean, anyway? And, is it even possible for an analyst to dive into all of these different areas? English philosophy expert The Notorious C.M.O. (aka, Simo Ahava) returns to the show to share his thoughts on the subject in this episode. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.