The Sequel Show

Some of our topic highlights include:

• Confidence intervals and precision needs vs wants
• The Spam bot problem and issuing corrections
• Shipping to vision vs shipping to need (and knowing when to say no)
• Market-based economy for data asks 
• The Microsoft SQL servers con
• Analytics-driven change rather than product team-driven change

As always, I'd love to hear your thoughts on the episode over on Twitter @borisjabes.

Want to discuss the best practices we covered in this episode? Come hang out in The Operational Analytics Club, where all your favorite data leaders gather to sharpen their skills. 

Know someone that you think would be an awesome guest on The Show (hint: you can totally nominate yourself)? Reach out to our content and community team. 

Resources:

• Benn on LinkedIn
• Benn on Twitter
• Benn’s blog
• Mode on LinkedIn
• Mode on Twitter
• Mode’s website

Music by the talented Joe Stevens: https://www.joestevenssound.com/

Some of our topic highlights include:

• His background and path to founding Montreal Analytics
• The importance of investing in your data
• Why clients trust Cyril and his team
• How Montreal Analytics leads organizations in the right direction
• Learning through trial and error
• Whether a shared data stack is the best path forward

As always, I'd love to hear your thoughts on the episode over on Twitter @borisjabes.

Want to discuss the best practices we covered in this episode? Come hang out in The Operational Analytics Club, where all your favorite data leaders gather to sharpen their skills. 

Know someone that you think would be an awesome guest on The Show (hint: you can totally nominate yourself)? Reach out to our content and community team. 

Resources:

• Cyril on LinkedIn
• Montreal Analytics on LinkedIn
• Montreal Analytics on Twitter
• Montreal Analytics’s website
• Montreal Analytics on Medium

Music by the talented Joe Stevens: https://www.joestevenssound.com/

Some of our topic highlights include:

• The right amount of precision in different scenarios
• What modern sales is (and what you need to be successful)
• Why salespeople are crucial to your customers 
• Using automation to inform sales behavior
• The trick for identifying talent before you have the data and track record
• The importance of pipeline hygiene versus “being good at sales”
• Remote work vs clustered work

As always, I'd love to hear your thoughts on the episode over on Twitter @borisjabes.

Want to discuss the best practices we covered in this episode? Come hang out in The Operational Analytics Club, where all your favorite data leaders gather to sharpen their skills. 

Know someone that you think would be an awesome guest on The Show (hint: you can totally nominate yourself)? Reach out to our content and community team. 

Resources:

• Peter on LinkedIn
• Peter on Twitter
• Atrium on LinkedIn
• Atrium on Twitter
• Atrium’s website

Music by the talented Joe Stevens: https://www.joestevenssound.com/

Some of our topic highlights include:

• How Sarah got into the world of data
• Data modeling vs data building (and depth vs variety)
• The organizational pieces that need to fall in place before you get to the science in data science
• How experience in sales helps with technical product translation
• Why Sarah started writing (and what she gets out of it)
• UI power features and their feasibility
• The code vs no-code debate

As always, I'd love to hear your thoughts on the episode over on Twitter @borisjabes.

Want to discuss the best practices we covered in this episode? Come hang out in The Operational Analytics Club, where all your favorite data leaders gather to sharpen their skills. 

Know someone that you think would be an awesome guest on The Show (hint: you can totally nominate yourself)? Reach out to our content and community team. 

Resources:

• Sarah on LinkedIn
• Sarah’s newsletter
• Sarah on Twitter

Music by the talented Joe Stevens:  https://www.joestevenssound.com/

Some of our topic highlights include:

• Emily’s background, her journey to data, and what she does at Drizly
• Answers to your burning questions, like: What type of alcohol is the most popular?
• How Emily moved up the data ranks from engineer to manager
• Value of optimizing old vs building new
• The evolution of data sharing throughout Emily’s career
• How data engineers and data analysts differ at Drizly

As always, I'd love to hear your thoughts on the episode over on Twitter @borisjabes.

Want to discuss the best practices we covered in this episode? Come hang out in The Operational Analytics Club, where all your favorite data leaders gather to sharpen their skills. 

Know someone that you think would be an awesome guest on The Show (hint: you can totally nominate yourself)? Reach out to our content and community team. 

Resources:

• Emily on LinkedIn
• Emily on Twitter
• Drizly on LinkedIn
• Drizly on Twitter
• Drizly’s website

Music by the talented Joe Stevens: https://www.joestevenssound.com/

Some of our topic highlights include:

• Jessica’s early career in banking
• Differences between data across different types of companies
• How data roles differ from consumer banking to investment banking 
• The importance of statistical rigor and running experiments
• The art of persuasion and corporate psychology in data 
• How to improve interconnectivity between departments 
• What B2B SaaS companies can learn from B2C companies
• Striking the right balance in data

As always, I'd love to hear your thoughts on the episode over on Twitter @borisjabes.

Want to discuss the best practices we covered in this episode? Come hang out in The Operational Analytics Club, where all your favorite data leaders gather to sharpen their skills. 

Know someone that you think would be an awesome guest on The Show (hint: you can totally nominate yourself)? Reach out to our content and community team. 

Resources:

• Jessica on LinkedIn
• Notion on LinkedIn
• Notion on Twitter
• Notion’s website

Music by the talented Joe Stevens: https://www.joestevenssound.com/

Some of our topic highlights include:

• Scott’s journey to becoming “unhireable” 
• Why management consulting is so intense
• Why you need tool conventions
• The risk of analysis paralysis
• The value of career learning vs salaries
• To what degree trust is earned through work and wisdom rather than title or name
• Empowering others through data

As always, I'd love to hear your thoughts on the episode over on Twitter @borisjabes.

Want to discuss the best practices we covered in this episode? Come hang out in The Operational Analytics Club, where all your favorite data leaders gather to sharpen their skills. 

Know someone that you think would be an awesome guest on The Show (hint: you can totally nominate yourself)? Reach out to our content and community team. 

Resources:

• Scott on LinkedIn
• Scott on Twitter
• Brooklyn Data Co. on Twitter
• Brooklyn Data Co. on LinkedIn
• Brooklyn Data Co.’s website
• Locally Optimistic on Twitter
• Locally Optimistic on Slack
• Locally Optimistic’s website

Music by the talented Joe Stevens: https://www.joestevenssound.com/

Some of our topic highlights include:

• How CoderPad approaches technical interviewing for their data team
• What makes running technical interviews at most organizations so difficult
• How we, as interviewers, can do a better job at helping candidates see we’re on the same side
• What analytics teams should track during their hiring process
• How to build KPIs that reflect your rate of learning in your organization
• The five “whys” (and how to ask them)

As always, I'd love to hear your thoughts on the episode over on Twitter @borisjabes.

Want to discuss the best practices we covered in this episode? Come hang out in The Operational Analytics Club, where all your favorite data leaders gather to sharpen their skills. 

Know someone that you think would be an awesome guest on The Show (hint: you can totally nominate yourself)? Reach out to our content and community team. 

Resources:

• David on LinkedIn 
• CoderPad on Twitter
• CoderPad on LinkedIn
• CoderPad’s website

Music by the talented Joe Stevens.

Some of our topic highlights include:

• The history of dbt Labs (and the dbt community)
• How community became an opportunity for the dbt Labs team to transfer their internal culture and values outward (and how they did so) 
• The original vision and purpose behind the dbt community (and how it’s matured)
• How community helps data practitioners glean second-hand gold from software engineering (and what this means for functional engineering)
• How real time is real time enough?
• How to help engineers better conceptualize the world of modern data
• The bull and bear cases for the semantic layer
• If data and analytics will develop into a singular-language world

As always, I'd love to hear your thoughts on the episode over on Twitter @borisjabes.

Want to discuss the best practices we covered in this episode? Come hang out in The Operational Analytics Club, where all your favorite data leaders gather. 

Know someone that you think would be an awesome guest on The Show (hint: you can totally nominate yourself)? Reach out to our content and community team. 

Resources:

• Tristan on LinkedIn 
• Tristan on Twitter
• Tristan’s newsletter
• dbt Slack community
• dbt Labs on Twitter
• dbt Labs on LinkedIn
• dbt’s website

Music by the talented Joe Stevens. 

Some of our topic highlights include:

• The history and background of Monte Carlo
• Why the planning process is so important for data teams
• How to set (and crush) a data goal
• The difference between being obsessed with systems and being obsesses with goals
• How data teams waste time
• How often data downtime and data issues are tied to a lack of knowledge transfer and process within an org
• How we can continue to improve the corporate culture around data

As always, I'd love to hear your thoughts on the episode over on Twitter @borisjabes.

Want to discuss the best practices we covered in this episode? Come hang out in The Operational Analytics Club, where all your favorite data leaders gather. 

Know someone that you think would be an awesome guest on The Show (hint: you can totally nominate yourself)? Reach out to our content and community team. 

Resources:

• Barr Moses on LinkedIn 
• Barr Moses on Twitter 
• Barr Moses on Medium
• Monte Carlo on Twitter 
• Monte Carlo on LinkedIn 
• Monte Carlo's website

Music by the talented Joe Stevens. 

As always, I'd love to hear your thoughts on the episode over on Twitter @borisjabes.

Know someone that you think would be an awesome guest on The Show (hint: you can totally nominate yourself)? Reach out to our content and community team. 

Resources:

• Jessica Cherny on LinkedIn 
• Jessica Cherny on Twitter
• Data Angels on Twitter 
• Ironclad on LinkedIn 
• Ironclad on Twitter
• Ironclad's website 

Music by the talented Joe Stevens. 

As always, I'd love to hear your thoughts on the episode over on Twitter @borisjabes.

Know someone that you think would be an awesome guest on The Show (hint: you can totally nominate yourself)? Reach out to our content and community team. 

Resources:

• Bigeye’s website
• Bigeye on Twitter
• Bigeye on Linkedin
• Bigeye on Medium
• Egor on Linkedin

Music by the talented Joe Stevens. 

As always, I'd love to hear your thoughts on the episode over on Twitter @borisjabes.

Know someone that you think would be an awesome guest on The Show (hint: you can totally nominate yourself)? Reach out to our content and community team. 

Resources:

• Marc Stone on Linkedin 
• ClickUp's website
• ClickUp on Linkedin
• ClickUp on Twitter

Music by the talented Joe Stevens. 

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.