Episode 92: Analytics Engineering, Locally Optimistic, and Marketing-Mix Modeling with Michael Kaminsky-Datacast

Show Notes

(01:48) Mike recalled his undergraduate experience studying Economics at Arizona State University and doing research on statistics/econometrics.

(04:59) Mike reflected on his three years working as an analyst in the Boston office of the Analysis Group.

(09:08) Mike discussed how he leveled up his programming skills at work.

(11:05) Mike shared his learnings about building effective data-driven products while working as a data scientist at Case Commons.

(17:20) Mike revisited his transition to a new role as the Director of Analytics at Harry’s, the men’s grooming brand — starting a new data team from scratch.

(23:04) Mike unpacked analytics and infrastructure challenges during his time at Harry’s — developing the data warehouse, an internal marketing attribution tool, and a fleet of systems for automated decision-making to improve efficiency.

(27:21) Mike reasoned his move to Mexico City — spending time practicing Spanish, among other things.

(32:22) Mike talked about his journey of starting a new consulting practice to help companies get more value out of their data, which was primarily shaped by his network.

(36:30) Mike shared the founding story behind Recast, whose mission is to help modern brands improve the effectiveness of their marketing dollars.

(42:09) Mike dissected the core technical problem that Recast is addressing: performing media mix modeling in the context of “programmatic” channels.

(46:14) Mike shared the story behind the inception and evolution of Locally Optimistic, a community for current and aspiring data analytics leaders.

(49:29) Mike walked through his 3-part blog series on Agile Analytics — discussing the good aspects, the bad aspects, and the adjustments needed for analytics teams to adopt the Scrum methodology.

(53:25) Mike unpacked his post “A Culture of Partnership,” — which discusses the three key activities that can help an analytics team identify the most important opportunities in the business and work effectively with key stakeholders and partner teams to drive value.

(57:25) Mike examined his seminal piece called “The Analytics Engineer,” which generated much attention from the analytics community — which argues that the analytics engineer can provide a multiplier effect on the output of an analytics team.

(01:03:24) Mike shared the motivation and pedagogical philosophy behind the Analytics Engineers Club (co-founded with Claire Carroll), which provides a training course for data analysts looking to improve their engineering skills.

(01:07:57) Mike anticipated the evolution of the quickly evolving modern data stack (read his Fivetran article “The Modern Data Science Stack”).

(01:09:22) Mike unpacked how organizations can build, start, and maintain the data quality flywheel (read his Datafold article “The Data Quality Flywheel”).

(01:11:40) Mike shared his thoughts regarding the challenge of sharing complex analyses.

(01:13:15) Closing segment.

Mike’s Contact Info

Further Resources

Notes

My conversation with Michael was recorded back in October 2021. Since then, Michael has been active in his work projects. I’d recommend:

About the show

Datacast features long-form, in-depth conversations with practitioners and researchers in the data community to walk through their professional journeys and unpack the lessons learned along the way. I invite guests coming from a wide range of career paths — from scientists and analysts to founders and investors — to analyze the case for using data in the real world and extract their mental models (“the WHY and the HOW”) behind their pursuits. Hopefully, these conversations can serve as valuable tools for early-stage data professionals as they navigate their own careers in the exciting data universe.