#73 Ship-Posting and Cake Recipes: Measuring the Return of Your Data Initiatives - Interview w/ Katie Bauer

Sign up for Data Mesh Understanding's free roundtable and introduction programs here: https://landing.datameshunderstanding.com/Please Rate and Review us on your podcast app of choice!If you want to be a guest or give feedback (suggestions for topics, comments, etc.), please see hereEpisode list and links to all available episode transcripts here.Provided as a free resource by Data Mesh Understanding / Scott Hirleman. Get in touch with Scott on LinkedIn if you want to chat data mesh.Transcript for this episode (link) provided by Starburst. See their Data Mesh Summit recordings here and their great data mesh resource center hereKatie's LinkedIn: https://www.linkedin.com/in/mkatiebauer/Katie's Twitter: https://twitter.com/imightbemaryIn this episode, Scott interviewed Katie Bauer, a Data Science Manager at Twitter in their Core-Tech group. To be clear she was not on representing Twitter, only her own opinions. The main topic of discussion was how to measure the value and success of your data projects/implementations.Some very useful advice from Katie that can feel a bit obvious when said but is VERY often and easily overlooked: measure for what would make you drive actions. If getting a 10x higher than expected or 90% below expected result isn't going to change your decision, while it may be interesting information, is it really important? If not, don't waste the time to measure it. Especially early on in your data measurement maturity. The point is also to get to an objective evaluation, not overly precise measurements. Set yourself up to improve and iterate. Don't make this hard on yourself.She also gave the pithy statement: what is valuable is not necessarily valued. Katie has a cake analogy that plays into data maturity well. Think about your need and the other person's capability regarding making a cake. Do you need a fancy cake for wedding or is this for a 3 year old's birthday party? One, you probably want to be special. One, if it vaguely resembles something from TV and tastes decent, the consumer will probably be happy. Is the other person capable of making a super fancy layered red velvet cheesecake or is a cake mix in a box probably more up their alley. How mature are the parties on creating measurement data and how mature or advanced do you need the output to be?Katie started the conversation talking about some survivorship bias / other biased ways of measuring. Often, she has seen throughout her career that people having success seek to prove their success via metrics instead of find the metrics that matter the most. That has some pretty obvious flaws so we need to move forward towards better measurement practices. For Katie, measuring the value of data science is pretty meta.Katie recommends starting out with some...

Om Podcasten

Interviews with data mesh practitioners, deep dives/how-tos, anti-patterns, panels, chats (not debates) with skeptics, "mesh musings", and so much more. Host Scott Hirleman (founder of the Data Mesh Learning Community) shares his learnings - and those of the broader data community - from over a year of deep diving into data mesh. Each episode contains a BLUF - bottom line, up front - so you can quickly absorb a few key takeaways and also decide if an episode will be useful to you - nothing worse than listening for 20+ minutes before figuring out if a podcast episode is going to be interesting and/or incremental ;) Hoping to provide quality transcripts in the future - if you want to help, please reach out! Data Mesh Radio is also looking for guests to share their experience with data mesh! Even if that experience is 'I am confused, let's chat about' some specific topic. Yes, that could be you! You can check out our guest and feedback FAQ, including how to submit your name to be a guest and how to submit feedback - including anonymously if you want - here: https://docs.google.com/document/d/1dDdb1mEhmcYqx3xYAvPuM1FZMuGiCszyY9x8X250KuQ/edit?usp=sharing Data Mesh Radio is committed to diversity and inclusion. This includes in our guests and guest hosts. If you are part of a minoritized group, please see this as an open invitation to being a guest, so please hit the link above. If you are looking for additional useful information on data mesh, we recommend the community resources from Data Mesh Learning. All are vendor independent. https://datameshlearning.com/community/ You should also follow Zhamak Dehghani (founder of the data mesh concept); she posts a lot of great things on LinkedIn and has a wonderful data mesh book through O'Reilly. Plus, she's just a nice person: https://www.linkedin.com/in/zhamak-dehghani/detail/recent-activity/shares/ Data Mesh Radio is provided as a free community resource by DataStax. If you need a database that is easy to scale - read: serverless - but also easy to develop for - many APIs including gRPC, REST, JSON, GraphQL, etc. all of which are OSS under the Stargate project - check out DataStax's AstraDB service :) Built on Apache Cassandra, AstraDB is very performant and oh yeah, is also multi-region/multi-cloud so you can focus on scaling your company, not your database. There's a free forever tier for poking around/home projects and you can also use code DAAP500 for a $500 free credit (apply under payment options): https://www.datastax.com/products/datastax-astra?utm_source=DataMeshRadio