Quantifying The Return On Investment For Your Data Team

Data Engineering Podcast

Tobias Macey에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 Tobias Macey 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.

2y ago 1:01:53

MP3•에피소드 홈

Summary

As businesses increasingly invest in technology and talent focused on data engineering and analytics, they want to know whether they are benefiting. So how do you calculate the return on investment for data? In this episode Barr Moses and Anna Filippova explore that question and provide useful exercises to start answering that in your company.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management
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Your host is Tobias Macey and today I'm interviewing Barr Moses and Anna Filippova about how and whether to measure the ROI of your data team

Interview

Introduction
How did you get involved in the area of data management?
What are the typical motivations for measuring and tracking the ROI for a data team?
- Who is responsible for collecting that information?
- How is that information used and by whom?
What are some of the downsides/risks of tracking this metric? (law of unintended consequences)
What are the inputs to the number that constitutes the "investment"? infrastructure, payroll of employees on team, time spent working with other teams?
What are the aspects of data work and its impact on the business that complicate a calculation of the "return" that is generated?
How should teams think about measuring data team ROI?
What are some concrete ROI metrics data teams can use?
- What level of detail is useful? What dimensions should be used for segmenting the calculations?
How can visibility into this ROI metric be best used to inform the priorities and project scopes of the team?
With so many tools in the modern data stack today, what is the role of technology in helping drive or measure this impact?
How do your respective solutions, Monte Carlo and dbt, help teams measure and scale data value?
With generative AI on the upswing of the hype cycle, what are the impacts that you see it having on data teams?
- What are the unrealistic expectations that it will produce?
- How can it speed up time to delivery?
What are the most interesting, innovative, or unexpected ways that you have seen data team ROI calculated and/or used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on measuring the ROI of data teams?
When is measuring ROI the wrong choice?

Contact Info

Barr
- LinkedIn
Anna
- LinkedIn

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.
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Links

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA