Quantifying The Return On Investment For Your Data Team
Data Engineering Podcast
Perspectives on the Biggest Gap in Data Management Tooling and Technology
The host concludes the episode by discussing the biggest gap in data management tooling and technology with the guests. They highlight the importance of interoperability, developer experience, and reliable data and AI.
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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
- Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack
- 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?
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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.
- Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
- If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com) with your story.
- To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers
Links
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
Sponsored By:
- Rudderstack:  Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at [dataengineeringpodcast.com/rudderstack](https://www.dataengineeringpodcast.com/rudderstack)
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