Published inData Mesh LearningWhy ‘Data as a Product’ is the Most Critical Principle in Data MeshWhile there are four guiding principles of Data Mesh, one stands out as the foundation for success: Data as a Product.Oct 3Oct 3
Published inOda Product & TechOda’s online experimentation journey: Lessons learned and best practicesOda’s experimentation story with five takeaways we’ve learned along the way.Jun 31Jun 31
How to Choose the Right Metrics for Your Experimentation PlatformHow collecting the right Experimentation Platform metrics will help you strengthen your experimentation culture and unlock new learnings.Oct 17, 20231Oct 17, 20231
Published inOda Product & TechA Practical Guide to Setting A/B Test Duration in a Bayesian context7 practical considerations to decide on A/B test duration if you are using Bayesian statistics.Aug 29, 2023Aug 29, 2023
Scaling Data Platform Teams: A Step-by-Step GuideSuccessfully scale your Data Platform team with these 5 steps.Jul 24, 2023Jul 24, 2023
Navigating the Spectrum of Centralization vs. Decentralization in Analytics TeamsHow you organize an Analytics function taking into account the (de)centralization dimension and the support vs. growth function dimension.Jun 18, 2023Jun 18, 2023
From Support to Growth Oriented Data TeamsLearn about the top four areas to focus on to transition your data org from a support-oriented to a growth-oriented one.May 2, 2023May 2, 2023
Published inOda Product & TechData as a product at OdaHow we use a product mentality on our datasets, dashboards and Machine Learning models to create value from data.Mar 1, 20235Mar 1, 20235
Published inAdevinta Tech BlogTreating data as a product at AdevintaA deep dive into how our teams implemented one of the data mesh architecture principles: treating data as a product.Sep 21, 20211Sep 21, 20211
Published inTowards Data ScienceData as a product vs data products. What are the differences?Understand what data as a product is by looking at an example.Jul 8, 20215Jul 8, 20215
