The Modern Data Stack: Your ticket to product analytics expertise

Data Collection and Analysis 📊 The pillars of product analytics - data collection, depth of analysis, collaboration, and product metrics - define each stage of the maturity model, guiding companies on their analytics journey. 📊 Creating cohorts or segments of users helps weave data sources together and analyze the impact of changes on users. 📊 The modern data stack allows for the collection of clean and accurate data from various sources, enabling better insights for product analytics. 📊 Analyzing the impact of a feature launch on user engagement and metrics is crucial for product analytics expertise. 💡 Choosing the right metrics for product analytics can be incredibly hard, as revenue may be going up at the expense of user engagement and retention. 📊 The focus metric framework suggests that organizations should track all KPIs within a framework to better understand how individual contributions feed into the most important metric. Organizational Collaboration and Culture 💡 Partnering with specialized tools and engineers in product analytics can enhance our capabilities and efficiency, rather than fighting against them. 🤝 Collaboration and creating a data culture are essential for leveraging advanced analytics tools to answer important business questions and drive decision-making. 📊 Everyone should be able to get self-serve insights from the data, not just those with a data science degree or excel expertise. 📊 The impact of having as many people as needed across the organization working on data tools and using data can be significant. 🔍 Everybody should be encouraged to get the answers they need and provided with the data they need, highlighting positive use cases to encourage others.