Benn Stancil, CAO and Founder of Mode, is no stranger to The Sequel Show. He delivered some hot takes during his first appearance back in November of 2021, featuring an epic debate about how far analysts should be willing to go to persuade an executive. So, we thought we’d bring him back to wrap up the 2022 season with a spicy final episode. As you’d expect, if you listened to Benn’s first TSS episode, we’re continuing the streak with more controversial opinions. And it all started with a simple question: What should you do when you mess up? This seemingly straightforward question had us going back and forth, leading us down a rabbit hole of anecdotes, covering everything from Census puns to all-you-can-eat restaurants.

You’re in for a very special episode of The Sequel Show this week: This is the first-ever in-person recording in its history. Cyril Marques, Founder and CEO of Montreal Analytics, joined me at our headquarters in SF to talk all about the importance of investing in data (and, of course, he brought some swag with him). For those of you who might not be familiar, Montreal Analytics is a full-stack data consultancy that helps clients build a single source of truth for their analytics. After being unhappy at every company he’s ever worked for, Cyril finally founded Montreal Analytics in 2020 after doing some full-time freelance consulting. Now, he helps companies unlock the most value for their organization, all while running a company he’s energized by. If you want to hear all the secrets from behind the walls of a data consultancy, you’ll like what Cyril has to say.

It’s always fun to reflect on how far we’ve come since the beginning – especially when it comes to sales, sports, and, of course, Census. Joining me on this journey is Peter Kazanjy, one of Census’s first customers! He also happens to be the co-founder and CRO of Atrium, a data-driven sales management software that helps sales managers use data to improve team performance. In this episode, Peter and I chat about how the sales industry has evolved with the increasing availability of data and automation. We explore the past and present state of sales, including how it has transformed into a little revenue factory with a bunch of human-occupied assembly lines. If you like actionable advice and sports analogies for the data-driven sales world, this episode is for you.

What career path do you go down when blood churns your stomach? Data, of course. That’s where this week’s episode with data consultant and advisor, Sarah Krasnik, started: discussing how her exposure to cognitive science at John’s Hopkins (paired with a squeamishness for blood) left her looking for another way to influence people’s perception of the world with logic and information. So, she pursued data science. Leading a data engineering team at PerPay – an all-in-one FinTech app providing underserved consumers with an easy way to make purchases, pay over time, & build credit – inspired Sarah to jump into freelance consulting work so she could start hacking away at some side projects. Because of that work, she’s gained knowledge in tons of different data-related areas. So, if you enjoy some healthy debates and hearing opposing opinions, you’ll like this episode. Is data science really a science? What do you think about the no-code movement? Is it feasible for UI to be improved with “power features”? Listen in as we tackle all this and more.

In this never-been-done-in-the-history-of-The-Sequel-Show episode, we’re bringing you a live recording from our data community event, Summer Community Days. In case you missed it, Summer Community Days was our two-day, free, virtual & practitioner-first conference that presented community expert sessions, keynotes, data hackathons, and local IRL happy hours in major US cities. This episode, featuring Emily Hawkins, was the keynote session kicking off the conference. As the data engineering manager at Drizly, an online alcohol ordering and delivery platform, Emily has the insight to satisfy your curiosity and answer all those burning alcohol-delivery questions you have (no pun intended). What was the most popular kind of alcohol that people gifted this year? What is the most popular time to place an order on Drizly? What day did you usually receive the most orders in 2021? Listen up, because we’re bringing you the answers to all these questions – and more! If you missed this keynote (or just want to relisten), here’s your chance.

We’re taking data down a different path than usual this week on The Sequel Show. For EP 25, I spoke with Jessica Zhang, data scientist at Notion, a collaboration software hub for users to write, plan, and get organized. Although Jessica started with an “on-the-fly” decision to join a financial institution after college, it allowed her to learn all the data analysis bells and whistles and taught her how to connect the dots in large corporations. Of course, she evolved as the economy evolved, making carefully planned out career jumps to see all the different facets of the data industry, and inspiring our wide-ranging conversation. In this unique episode, we discuss the differences between banking and tech data roles, the distinction between consumer and investment banking, the importance of statistical rigor when managing data, the role that persuasion and psychology play in data, how you can use these to improve interconnectivity between departments, and what B2B SaaS can learn from B2C.

This week’s episode is a great one if you feel like you could use some guidance in your data career. For EP 24, I chatted with Scott Breitenother, Founder and CEO of Brooklyn Data Co., a data and analytics consultancy that helps your team build a full modern data stack specific to your needs. While Scott might be deemed “unhireable”, the skills he gained during his early years of management consulting experience (conducting quantitative analysis, structuring unstructured problems, and creating something out of nothing) are what makes Brooklyn Data Co. so good at what they do. In the episode, we take a look at Scott’s career journey, what makes management consulting so intense, the importance of tool conventions, prioritizing career learning over your salary, why your track record and wisdom are more important than your title, and how to empower others through data.

This week’s episode is a great one if you or your team is focused on hiring data practitioners this year (like most teams). For EP 23, I had the chance to sit down with David Corea, senior analytics manager at CoderPad, a technical assessment software aimed at improving how we interview technical candidates. While CoderPad isn’t specific to interviewing data practitioners, it’s a great example of one of the many ways the data industry can learn from the best practices of the software engineering world. In the episode, we look at how CoderPad uses data to improve their product, how we as interviewers can minimize interview stress, how to measure learning in an organization obsessed with progress, and the importance of the five whys.

This week’s episode is with one of my favorite data experts and thought leaders in the industry and I’m so excited to release it to you all. For EP 22, I sat down with none other than Tristan Handy, CEO and founder of dbt Labs. If you’re a dbt user (or a member of their massive online community), you probably know of Tristan, especially if you’re also subscribed to his newsletter, The Analytics Engineering Roundup (co-written by the also-awesome Anna Filippova). In the episode, we take a deep dive into the role community plays in pushing the data industry forward, inspiring us all to do better, and educating the next generation of data experts to pick up our torch. Don’t worry, we also dive into some nerdy, technical topics (including the subject of Tristan’s last newsletter: the semantic layer).

This week I’m joined by none other than Barr Moses, Co-founder and CEO of Monte Carlo Data and all around awesome data thought leader. Our conversation centered around how to ensure data teams focus on the right systems and goals and don’t waste time hyper-focusing on the wrong metrics. This is a great episode for data leaders and data ICs alike who want to get more out of their work and increase their impact.

This week I'm joined by Jessica Cherny, senior data analyst at Ironclad. Jessica is an amazing example of how to foster a genuine presence on the internet while building a career in data, and her episode is great one for folks looking to get into data or those who are at the beginning of their data careers. We touched on a lot of an interesting highlights from Jessica's education, career, and day-to-day data work, including how (and why) she joined Ironclad as a data team of one, why she created her community Data Angels (which is another amazing resource for women in data), and her recommendations for folks looking to break into the field.

This week I’m joined by one of my favorite data leaders, Egor Gryaznov, the co-founder and CTO of data observability company Bigeye. The thread that connected our conversation this episode was what it means to work with a "keeping the lights on" perspective in data, but we touched on a lot of great material, including Egor's time at Uber, how his first company convinced him to join their data team, how we can leverage computer engineering principles in data (and beyond), data warehouse planning, and the future of Bigeye.

In this week's episode, I sat down (virtually) with Marc Stone, head of the analytics team at ClickUp. Marc truly understands the challenges of going from being the only person working on data to running a very large data team and we tapped into his experience to extract some awesome lessons from his career. Some of the high points include topics like what private companies can learn from governments and nonprofits, how to think about data pipelines (especially when there's a human in the mix), how to architect your systems for speed and robustness, how to stay ahead of your users' needs, and what the appropriate data team size is for your maturity level.

Before we get into the episode notes, I want to take a beat to thank all of you for supporting the Show over the last year. It's been amazing watching it go from a fun way to connect with some of my favorite folks in data to a full, polished podcast (featuring even more awesome people). We couldn't do it without you and I'm looking forward to giving the best and brightest of our industry the stage in the coming year.  To kick things off with season 2, we have a great episode with Jeremiah Lowin, founder and CEO of dataflow automation tool Prefect. In this 1-hour, 7-minute episode, Jeremiah and I dive into the lessons that the wider data industry can learn from the experts in data finance. Some of the major points include: How he got his start in data finance The difference in reporting in terms of uncertainty vs outcomes The case for hearing/focusing on problems vs solutions Building non-political organizational culture Non-managerial growth in data careers As always, let me know what you think of the newest episode over on Twitter at @borisjabes.  Resources: Prefect’s Website: https://www.prefect.io/ Find Jeremiah on Twitter:https://twitter.com/jlowin Find Jeremiah on Linkedin: https://www.linkedin.com/in/jlowin/ Prefect’s Linkedin: https://www.linkedin.com/company/prefect/ Prefect’s Twitter: https://twitter.com/PrefectIO Music by the talented Joe Stevens: https://www.joestevenssound.com/ Know someone you think would make an awesome guest on the podcast (you can totally nominate yourself, too)? Send your suggestions to our content and community team.

In today’s episode, I talk with Erik Bernhardsson, the former CTO at Better.com and early engineering manager at Spotify. We talked about how to build recommendation systems (including whether you should build them at all). We also covered some best practices on building and scaling data teams, what the essence of music is, and whether data teams should be thought of as an engineering discipline (and what programming languages we should embrace in the data world). We also took a deep dive into how to manage and govern the data team and how to measure the impact.  Also - if you're looking to stay up to date on the best and most colorful data industry commentary between episodes, check out our new newsletter: The Operational Analytics Diggest.  Resources: Erik’s Website: https://erikbern.com/ Find Erik on Twitter:https://twitter.com/bernhardsson Find Erik on Linkedin: https://linkedin.com/in/erikbern/ Erik’s Github: https://github.com/erikbern Music by the talented Joe Stevens: https://www.joestevenssound.com/

In today’s episode, I talk with Benn Stancil, chief analytics officer & co-founder at Mode. Honestly, this might be my favorite episode of the Show to date. We had a hearty discussion about data teams and Benn's perspective as someone who started his career as an analyst with an economics degree. Our conversation turned to more philosophical notes around what makes a data professional persuasive (and why that matters), why being wrong is OK (and sometimes a pivotal part of a successful career), and the function of data teams in organizational decisions.  As always, if you enjoyed this episode, please share it with a data-loving friend and let me know your thoughts on our discussion over on Twitter at @borisjabes.  Resources: Benn‘s Substack: https://benn.substack.com/ Find Benn on Twitter:https://twitter.com/bennstancil Find Benn on Linkedin: https://www.linkedin.com/in/benn-stancil/ Find Mode on Linkedin: https://www.linkedin.com/company/mode-analytics/ Mode’s site: https://mode.com/ Music by the talented Joe Stevens: https://www.joestevenssound.com/

In today’s episode, I talk with Lior Gavish, the co-founder and CTO of Monte Carlo. We talk about his security background, his previous company Sookasa, and the origin story of Monte Carlo, and much more (including the infamous data mesh). We also get into managing and detecting operational issues, what data managers should learn before installing alarms, and what it means to think with a “failure is a feature, not a bug” mindset. Bonus: If you’ve ever wondered if it’s better to be wrong consistently or randomly by 1%, we talk about that, too. Resources: Monte Carlo’s site: https://www.montecarlodata.com/about-us/ Connect with Lior on Linkedin:https://www.linkedin.com/in/lgavish/ Find Monte Carlo on Twitter: https://twitter.com/montecarlodata Find Monte Carlo on Linkedin: https://www.linkedin.com/company/monte-carlo-data/ Music by the talented Joe Stevens: https://www.joestevenssound.com/

Well, Sequel Show fans, we officially have some amazing theme music. Huge thanks to the talented Joe Stevens for our new theme song that makes all my synth-wave dreams come true.  In today’s episode, I talk with Stefania Olafsdottir, the co-founder and CEO of product analytics platform Avo. We covered an amusing amount of ground--as always in The Sequel Show--from the etymology of cow vs beef to the visualization of weather data. The meat of the show focuses on Stef’s early background as an associate researcher to the accuracy and specificity (or lack thereof) of data role titles today. We dive into the influences of data teams on their companies, how these teams evolve, and the facts, assumptions, and suggested actions these data champions can present. Don’t worry, we also discussed Stef’s experience as the CEO of Avo and took a look at how the company got its start (and her vision for the future of Avo). You don’t want to miss it. Resources: Avo’s site: https://www.avo.app/ Connect with Stef on Linkedin: https://www.linkedin.com/company/avohq/ Find Avo on Twitter: https://twitter.com/avohq Find Avo on Instagram: https://www.instagram.com/avohq/ Music by the talented Joe Stevens: https://www.joestevenssound.com/

In our latest episode, analytics engineer and community builder extraordinaire Claire Carroll sat down with us to talk about her journey from data analyst to analytics engineer to co-founder of analyticsengineers.club, an educational community for aspiring AEs. We cover a lot of ground, discussing her data-industry origin story, the fundamentals the next generation of AE should have in their toolkit, and the goals of her new training course. More broadly, Claire breaks down the gaps in the current data ecosystem and the difference between being a data consumer and a data creator. It's an episode you're not going to want to miss out on. As always, let me know what you think over on Twitter at @borisjabes.

In this live episode with Snowplow CEO and Co-founder Alex Dean, we dive into all things Snowplow and data collection. Alex runs us through the history of Snowplow--how the company was born and how it evolved--as well as what parts of the platform have grown in complexity. We break down what it means to think about events in a mind-frame of abundance vs scarcity, how to adapt to ever-changing regulations around privacy, and data architecture and infrastructure best practices. Alex rounds the discussion out with some advice on how to approach anonymous users over the next five years, and what a world where the warehouse is a single entity means for post-collection data structures.  Let me know what you think over on Twitter at @borisjabes.