#196 Data is a Team Sport - Learning to Collaborate Through Data - Interview w/ Andrew Pease
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. You can download their Data Mesh for Dummies e-book (info gated) here.Andrew's LinkedIn: https://www.linkedin.com/in/andrewpease123/In this episode, Scott interviewed Andrew Pease, Field CTO of North Europe at Salesforce. To be clear, he was only representing his own views on the episode.Some key takeaways/thoughts from Andrew's point of view (mostly written by him):Sensitizing people to data and improving their data fluency can be a challenge. Lots of people have had some less than perfect past experiences - perhaps a dry, abstract class has given them "statistics trauma". It's important to make it digestible for them to get started.Organizations typically evolve into silos so IT systems/approaches often evolve into silos too - Conway's Law. The bigger those organizations and silos are, the harder they are to bridge / the deeper the divides.Much as we'd like one, there is not a single silver bullet architecture for all organizations to overcome these silos.Without relevant IT architectures and processes, it can be challenging to put relevant and timely data and actionable insights into the business people's workflows. You won't get it "perfect" the first time, but get started and learn to improve through experience.You should reiterate to people that data is there to augment their role, not to replace it. It's there to help them be more efficient and successful in their work. That's a key part of data fluency, not just understanding how to use data but where data can help.Feedback loops are very important to increase data quality levels and data value. It's important to build in these loops to make end-users feel like they are a part of a constant and never-ending improvement exercise. It shouldn't be a big burden but...