Build Better Analytics And Models With A...

Summary

A lot of time and energy goes into data analysis and machine learning projects to address various goals. Most of the effort is focused on the technical aspects and validating the results, but how much time do you spend on considering the experience of the people who are using the outputs of these projects? In this episode Benn Stancil explores the impact that our technical focus has on the perceived value of our work, and how taking the time to consider what the desired experience will be can lead us to approach our work more holistically and increase the satisfaction of everyone involved.

Announcements

• Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.
• When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!
• Your host as usual is Tobias Macey and today I’m interviewing Benn Stancil about the perennial frustrations of working with data and thoughts on how to improve the experience

Interview

• Introductions
• How did you get introduced to Python?
• Can you start by discussing your perspective on the most frustrating elements of working with data in an organization?
  • How might that compound when working with machine learning?
• What are the sources of the disconnect between our level of technical sophistication and our ability to produce meaningful insights from our data?
• There have been a number of formulations about a "hierarchy of needs" pertaining to data. When the goal is to bring ML/AI methods to bear on an organization’s processes or products how can thinking about the intended experience act to improve the end result?
  • What are some failure modes or suboptimal outcomes that might be expected when building from a tooling/technology/technique first mindset?
• What are some of the design elements that we can incorporate into our development environments/data infrastructure/data modeling that can incentivize a more experience driven process for building data products/analyses/ML models?
• How does the design and capabilities of the Mode platform allow teams to progress along the journey from data discovery to descriptive analytics, to ML experiments?
• What are the most interesting, innovative, or unexpected approaches that you have seen for encouraging the creation of positive data experiences?
• What are the most interesting, unexpected, or challenging lessons that you have learned while working on Mode and data analysis?
• When is a data experience the wrong approach?
• What do you have planned for the future of Mode to support this ideal?

Keep In Touch

• LinkedIn
• @bennstancil on Twitter

Picks

• Tobias
  • Venture Unlocked Podcast
• Benn
  • Wrap Text by Bobby Pinero
  • Counting Stuff by Randy Au
  • Ray Data Co by Mr Ben
  • Modern Data Democracy By JP Monteiro
  • Bad Blood Podcast
  • Bad Blood Book

Links

• Mode Analytics
• Tidyverse
• Airflow
• Fivetran
  • Data Engineering Podcast Episode
• dbt
  • Data Engineering Podcast Episode
• Conway’s Law
• Cinchy
  • Data Engineering Podcast Episode
• Reverse ETL

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA