#116 A Startup's Early Journey Towards Decentralizing Data - Iterable's Analytics Evolution - Interview w/ Riya Singh
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 here.Riya's LinkedIn: https://www.linkedin.com/in/riyasingh1/In this episode, Scott interviewed Riya Singh, Business Insights Manager at Iterable.Some key takeaways/thoughts from Riya's point of view:~4 years ago, Iterable was in essentially "spreadsheet hell" with lots of manual data work and no standard way of storing or sharing data across domains. While domains had good data capabilities, the integration and coordination between domains was very difficult at best.Most exec questions can't be answered by the data from a single domain so cross domain data integration became a key factor in Iterable continuing to grow. How could they make crucial decisions informed by data if there was so much manual work to try to integrate ad hoc? Could they really trust something done manually each time?Fast time to market for simple, base level capabilities of their data platform was much more valuable than trying to nail every feature upfront. Data consumers understood it wasn't perfect data at the start but it led to much faster exploratory data initiatives which led to valuable insights sooner.You might have a much higher ROI buying tools than trying to really get by on low-cost but not feature-rich tools. If you build a very cost-efficient data platform that no one wants to use, is that actually valuable? How much time will you spend managing the tools or is it worth it to outsource that to a vendor?Combining data across sales, marketing, and product meant Iterable could tailor marketing messages and find better prospects, measure marketing return on investment (ROI), and cost optimize their operations and product among many other new insights. As teams that previously weren't directly interacting start to have more conversations, gaps in your data - whether in data created/collected or data shared - will emerge. Filling those gaps will mean you can answer more high-value questions to drive the business forward.At Iterable, when there is a specific use-case identified for cross-domain data integration, the central data team takes over ownership of what would be considered a consumer-aligned data set in data mesh terms. With only 4-5 domains, Iterable doesn't need to...