When building a new data stack, we often dive right into technologies, tech stacks, and all the fancy tools. I used to be one of those people and didn't realize that all these tools should serve one purpose - providing value for the company. That's why this time, I took the time to lay the groundwork and figure out how I can contribute to the business's growth. Based on that knowledge, I chose the data stack. Basically, I divided requirements gathering into two stages: engaging with stakeholders and investigating available tools and resources. During stakeholder discussions, I conducted interviews to gather as much information as possible by asking questions. Afterwards, I dedicated time to explore the existing tools and data sources. This process allowed me to comprehend the capabilities of the current stack and identify areas that require further attention. Let's start with interviews. I went and booked 8-10 meetings with all key people in the company: CEO, CTO, heads of departments, developers, marketers, etc. Basically, I spoke to anyone who showed interest in analytics. All interviews followed the same structure to ensure maximum efficiency. I asked three basic questions: 1. How analytics is used in your work? Also, asked to name examples of dashboards/reports/tools they are currently using. 2. What are pain points? What's not possible or hard to do with current setup? 3. What you wish existed that would help you solve your problems? Use these questions as guidelines and rephrase them to adapt to your specific situation. Encourage a flowing conversation and elaborate on the answers. Request links to dashboards and examples of reports to gain a better understanding. Explore the limitations of current solutions and discuss potential improvements together. Approach this discussion as a casual conversation rather than a formal interview. Take detailed notes of all the points discussed.

It's one thing to read articles or watch videos about perfectly crafted data architectures and think you're way behind. But back here in reality, things get messy & nothing is ever perfect or 100% done. Most of us are usually working on architectures that are: - Old & outdated - Hacked together - Mid-migration to new tools - Non-existent Or perhaps you're one of the lucky ones that recently started from scratch and things are running smoothly. Regardless, the best way to learn what's working (and not working) is from others. I believe this could be one of the best insights this community can collectively offer each other. So let's hear it. What does your data stack look like for the following components? 1. Database/Storage 2. Ingestion 3. Transformation 4. Version Control 5. Automation Feel free to add other items as well outside of these 5, but we can focus on these to keep it organized.