#71 Adventures in Data Maturity - Creating Reliable, Scalable Data Processes - Interview w/ Ramdas Narayanan
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 hereRamdas' LinkedIn: https://www.linkedin.com/in/ramdasnarayanan/In this episode, Scott interviewed Ramdas Narayanan, Vice President Product Manager of Data Analytics and Insights at Bank of America. To be clear, he was not representing the company and was sharing his own views.Ramdas came on to discuss lessons learned from building effective data sharing at scale on the operational plane over the last 5-10 years so we can apply those to our data mesh implementations. A key output of the conversation is a guiding principle for getting data mesh right - your goal is to convert data into effective business outcomes. It doesn't matter how cool or not cool your platform is or anything else - drive business outcomes! It's easy to let that get lost in the tool talk and everything around data mesh.Per Ramdas, when looking at creating a data product, or really any data initiative, you need to align first on business objectives and that will drive funding. In the financial space, that is direct literal funding but even outside, you should have the same mindset. Make sure you get engagement and alignment across business partners, technologists, and subject matter experts. How are you using technology to address or solve the business problem?Ramdas has seen that if you don't focus on creating reusable data, you can create silos - you need cohesive data sets, not bespoke data sets for every challenge as that just doesn't scale. You should also study the data sources you are using - is there additional useful data you could add to your dataset or could you use that data for other purposes - keeping an eye out for additional data to drive business value will really add a lot to your organization.When working with developers, Ramdas recommends helping them understand how the business is going to consume and use the data and then figure out if they should deliver data as something like an API or web service or more of a custom batch delivery. It is important to also work with data consumption teams to be reasonable in their consumption demands - getting them to modernize can be a challenge and that can put an unreasonable burden on producing teams.Ramdas talked about how crucial conversations and culture are...