As you scale, it’s important to think about the end goal of your data.
Since that time, I have seen countless organizations struggle when the data operations that worked for dozens of teammates stops working when the team grows to hundreds or thousands. In this article, I’ll share a few key takeaways that I hope will help you as you start to think about scaling your data operations.As you scale, it’s important to think about the end goal of your data: business intelligence activities, especially analytics.
The bottleneck had shifted from a performance problem to an operations problem. Our analytics and operations teams could query whatever data they wanted. Their queries were reasonably fast, but there was too much data. Nobody could find anything of value because we hadn’t implemented the warehouse with BI in mind.With this experience in mind, here are three lessons about building these tools:Tie documentation to value that data teams are producing.
Clarity and alignment come from adopting a single, centralized tool. That’s worth trading the loss of micro-efficiencies that you get from having multiple teams or orgs that want a slightly different tool.When working with data, automation is extremely possible. Automation is more within reach than it is with many other problem spaces like robotics. Instead of waiting for a data steward, you can machine-generate at least basic information about tables in your data warehouse.
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