#64 The Crucial Value of Data About Your Data: Approaching Data with a Product Mindset - Interview w/ Sadie Martin

Provided as a free resource by DataStax https://www.datastax.com/products/datastax-astra?utm_source=DataMeshRadio (AstraDB) https://www.patreon.com/datameshradio (Data Mesh Radio Patreon) - get access to interviews well before they are released Transcript for this episode (https://docs.google.com/document/d/1KGYF2GKYhaoRUl_FTYXZkloqaahw-v-Oe15HbZLcn9c/edit?usp=sharing (link)) provided by Starburst. See their Data Mesh Summit recordings https://www.starburst.io/learn/events-webinars/datanova-on-demand/?datameshradio (here) and their great data mesh resource center https://www.starburst.io/info/distributed-data-mesh-resource-center/?datameshradio (here) In this episode, Scott interviewed Sadie Martin, Senior Product Manager, Data Platform at Q4 Inc about applying a product mindset to data in general. This is really crucial to getting data as a product right but also in building out your data platforms and even some processes for data mesh. Scott's summation of some key points: Anyone can apply a product mindset, not just the product manager Giving yourself the time before starting work to investigate and create you measurement framework, including your baselines, is crucial to measuring data work progress and choosing where to focus Approach your data work with intentionality Really understand what you are trying to accomplish and what your immediate customers/consumers are trying to use the data for to accomplish. Sadie started as a data analyst where the team didn't have a product manager - they were doing a lot of work and weren't sure if things were likely to work or even if what they did had a positive impact after it was done. So she started to take on some of the task of answering those questions and transitioned into being a product manager for data. So, what is a product mindset? For Sadie, the easy definition but with lots of hidden depth, is "it's all about really understanding the problem". For most organizations, really thinking about the problem you are trying to solve is new relative to