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.

As the axiom goes: people don't leave companies; they leave their managers. And, good analysts are constantly being approached with new opportunities. So, what's the secret formula for hanging on to analytics talent? Assuming simply chaining them to their desks isn't an option, then the trick is keeping them happy and motivated. On this episode, the gang discusses their experiences and perspectives on the topic. Tim tried to quit the show just before recording, but he then discovered that Michael had chained him to his desk. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Tell me about a time you produced an amazing analysis. Please provide your response in the form of a Jupyter notebook that uses Python or R (or both!) to pull words from a corpus that contains all words in the OED stored in a BigQuery table. I mean, that's a fair question to ask, right? No? Well, what questions and techniques are effective for assessing an analyst's likelihood of succeeding in your organization? How should those techniques differ when looking for a technical analyst as opposed to a more business-oriented one? On this episode of the show -- recorded while our recording service clearly thought it was in a job interview that it needed to deliberately tank -- Simon Rumble from Snowflake Analytics joined the gang to share ideas on the topic. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Mama always said: life is like a box of chocolates, and online learning is sometimes like one of those boxes where you don't know which piece is delicious nougat and which piece is some sort of nasty, coconut-y cream. Well, maybe not your mama. But, it's a big world, so, surely, there's a mother out there somewhere who would agree with the sentiment. On this episode, the gang chatted with Google Consumer Insights Analyst Lizzie Allen-Klein about different learning styles and different approaches and options for learning new (and hard!) analytical skills. And there might have been an embarrassing interlude where Tim and Michael exhibited their respective possession of some Y-chromosomes. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

For this mini-episode, Tim sits down (virtually) with Episode #077 guest Jodi Daniels from Red Clover Advisors to chat about the world of privacy post-May 25, 2018 (GDPR), as well as the upcoming California Consumer Privacy Act of 2018 (CCPA). Has the free-wheeling world of free-flowing consumer data ended, or are companies simply learning how to behave with more care? Give it a listen to find out?

Business Intelligence. It's a term that's been around for a few decades, but that is every bit as difficult to nail down as "data science," "big data," or a jellyfish. Think too hard about it, and you might actually find yourself struggling to define "analytics!" With the latest generation of BI tools, though, it's a topic that is making the rounds at cocktail parties the world over! (Cocktail parties just aren't what they used to be.) On this episode, the crew snags Taylor Udell from Heap to join in a discussion on the subject, and Moe (unsuccessfully) attempts to end the episode after six minutes. Possibly because neither Tableau nor Superset can definitively prove where avocado toast originated (but Wikipedia backs her up). But we all know Tim can't be shut up that quickly, right?! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

The company: "Hey, you. That's a mighty nice test you've run. We should be doing that a lot more of those." You: "Um...okay. But, I'm only one person." In this episode, the gang chats with Pinterest's Andrea Burbank (Twitter | Pinterest) about how she (loosely) dealt with this scenario: from sheer force of will to get some early wins to strategic thinking combined with late nights, an obsession with checklists, and a willingness to be flexible as she slowly, but firmly, pushed the organization to steadily increasing test volume and test reliability. And sweet potato gnocchi. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Humans are creatures of habit. And analysts — those of us who haven't been so drawn into the world of artificial intelligence that we have become cyborgs, at least — are humans. In this episode, the gang explores the good and the bad side of analytical habits: what analyses we gravitate towards, how we go about approaching those analyses, and, to some extent, how those habits are impacted by our organizational environments. With a side dish of, "What is a data scientist, anyway?" (because who can resist a question that is both rhetorical AND controversial?!). For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Once upon a time, there was some data. And that data cried out to be extracted and analyzed and packaged up like the most exquisite of gifts and then presented gloriously to an eager and excited group of stakeholders. But, alas! Will this data story have a happy ending? Perhaps. Perhaps not! And that's the subject of this episode. Sort of. Our intrepid hosts ask the question, "How can we communicate more effectively by applying the tricks of the data journalism trade?" To answer that question, Walt Hickey, late of fivethirtyeight.com and now the founder and curator of the daily Numlock Newsletter, joins the gang to chat about how he combined an education in applied mathematics with an interest in news media to become a data journalist. Along the way, the discussion explores how Walt's insights can be applied to business analytics. And there's a terrible analogy about meat that gets butchered along the way (thanks, Tim!). For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Are you reading this? If so, then you are literate. But, are you (and are your stakeholders) data literate? What does that even mean? On this episode -- recorded in front of a live audience at Marketing Evolution Experience in Las Vegas -- the gang tackled the topic. Mid-way through the show, they were delighted to be joined on stage by Gary Angel (unplanned, but due to a series of unfortunate travel and communication mishaps -- recording with a live audience is exciting! He is officially over halfway to joining the podcast's Five-Timers Club)! It was an engaging discussion with some smart questions from the live audience. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.  

Put this in your pipe and smoke it: all of the tracking we try to do of people is actually technology designed to track content. And, even that tracking of content was a hacked-together repurposing of a system designed to deliver content. In other words, we've got layers of fiction upon fiction that we're trying to muddle through (and, often, ignore) as an industry. The result? A ridiculous level of inefficiency whereby brands overspend to ineffectively reach their target audiences with direct response messages, and well-intended intermediaries grow their bank accounts. Ugh! On this episode, the gang invited Sergio Maldonado from PrivacyCloud (and, by day, from Sweetspot Intelligence) to chat about the broken environment we're operating in, as well as how GDPR and financial considerations may just force us onto a path of shaking it up! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Regression. Correlation. Normality. t-tests. Falsities of both the positive and negative varieties. How do these terms and techniques play nicely with digital analytics data? Are they the schoolyard bullies wielded by data scientists, destined to simply run by and kick sand in the faces of our sessions, conversion rates, and revenues per visit? Or, are they actually kind-hearted upperclassmen who are ready and willing to let us into their world? That's the topic of this show (albeit without the awkward and forced metaphors). Matt Policastro from Clearhead joined the gang to talk -- in as practical terms as possible -- about bridging the gap between traditional digital analytics data and the wonderful world of statistics. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Thanks for stopping by. Please get comfortable. We're going to be taking a few notes while you listen, but pay that no mind. Now, what we'd like you to do is listen to the podcast. Oh. And don't worry about that big mirror over there. There may be 2 or 3 or 10 people watching. Wow. We're terrible moderators when it comes to this sort of thing. That's why Els Aerts from AGConsult joined us to discuss user research: what it is, where it should fit in an organization's toolkit, and some tips for doing it well. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

If you have a smartphone nearby and you are not wearing a foil hat, chances are that some brand somewhere -- and probably several brands in many places -- know where you are. Is that creepy? Maybe. It's likely removing a few taps when you check what the weather will be like tomorrow, and there might just be a coupon for a discounted hamburger just waiting to pop up when you get near your favorite QSR around lunchtime! In this episode, James Fogelberg from Landmarks ID joins the gang to discuss the ins and outs of using the ubiquity of mobile to the advantage of both brands and consumers. 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 walked out of a meeting with a clear idea of the analysis that you're going to conduct, only to find yourself three days later staring at an endless ocean of crunched data and wondering in which direction you're supposed to be paddling your analysis boat? That might not be an ocean. It might be an analytics rabbit hole. In this episode, the gang explores the Analysis of Competing Hypotheses approach developed by Richards Heuer as part of his work with the CIA, inductive versus deductive reasoning, and engaging stakeholders as a mechanism for focusing an analysis. Ironically, our intrepid hosts had a really hard time avoiding topical rabbit holes during the episode. But, acknowledging the problem is the first part of the solution! For complete show notes, including links to items mentioned in this show and a transcript of the discussion, visit the show page.  

Michael, Tim, and 12,998 of their closest friends descended on Las Vegas for Adobe Summit the last week of March. With luck, Tim will have worked through the after-effects of the sensory overload of Vegas combined with the sensory overload of Adobe Summit by mid-April, but who knows? The guys (really...just the guys -- Moe was in the U.S., but she was in Austin at CXL Live) sat down to share their hot takes from the show. Attribution, Adobe Sensei, Adobe Launch, the Philadelphia Eagles, and more! For a picture of Michael and Tim recording this episode, head over to the show page.

Some people (possibly even one of the co-hosts of this podcast...on this very episode) have been known to say, "People have this dependency on Excel, which is freakin' weird!" We know it wasn't Tim, because he wouldn't have filtered his language! Whether it's a symptom of weirdness, an illustration of inertia, or an invisible hand of inevitability, though, Excel remains omnipresent. Is that a good thing? Is it a bad thing? Is it merely "a thing?" In this episode, the gang dives into the topic: the good and bad of Excel, the various paths to a future where its ubiquity is no longer a given, and different strategies and considerations for moving towards that future.  For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.