#18 Finding (and Sharing) Your Internal Data Pain Points - Interview w/ Molly Vorwerck

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.Molly's LinkedIn: https://www.linkedin.com/in/vorwerck/Monte Carlo's blog: https://www.montecarlodata.com/blog/In this episode, Scott interviews Molly Vorwerck, head of content and communications at data observability vendor Monte Carlo. Scott asked Molly to be on as she is a great community member in general and as Monte Carlo is well known in the data space for putting out high quality content on hot-button issues. The idea is to take how Molly and team get their ideas and apply that process to figuring out major pain points internally and creating a cohesive strategy to at least start discussing them.Molly recommends to constantly be interviewing stakeholders. She talks about interviewing people from multiple sides of a challenge, e.g. not just the data consumers but the data producers and the data engineering teams re data challenges. She tries to give people the space to tell their story and asks open-ended questions to truly get their perspective, not arrive at a pre-specified answer.Molly talks about ways to make the other person feel valued by active listening and making the conversation mutually beneficial. It may be a person you want to interview again so building the relationship is crucial, not just extracting info in a one-time manner.Scott and Molly dig a bit into the idea of blameless post mortems and how valuable they can be for doing data mesh, especially to figure out what happened to cause data downtime and how to prevent the same issue in the future.Data Mesh Radio is hosted by Scott Hirleman. If you want to connect with Scott, reach out to him on LinkedIn: https://www.linkedin.com/in/scotthirleman/If you want to learn more and/or join the Data Mesh Learning Community, see here: https://datameshlearning.com/community/If you want to be a guest or give feedback (suggestions for topics, comments, etc.), please see hereAll music used this episode was found on PixaBay and was created by (including slight edits by Scott Hirleman): Lesfm, MondayHopes, SergeQuadrado,

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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