はてなブックマーク

This post first appeared in The Analytics Engineering Roundup. Business intelligence is on a collision course with AI. The collision itself hasn’t happened yet, but it’s clearly coming. The inevitability of this has been clear roughly since the launch of ChatGPT, but no one knew exactly what shape that would take. Today I want to propose how that collision is going to happen and what will happen i

This post first appeared in The Analytics Engineering Roundup. I’ve been writing this newsletter since September of 2015. This will be the 10th year I’ve had the opportunity to reflect on a year gone by and make predictions about the year ahead. Data science and the rise of PythonIn the early years (2015-2017), the data ecosystem was dominated by data science. Data viz, developments in the Python

Data quality dimensions: What they are and how to incorporate them Poor-quality data can kill a data-driven project before it even starts. ‌Unfortunately, data quality remains a struggle across all industries. Eight years ago, the situation was dire. A Harvard Business Review study conducted in 2017 found that only 3% of corporate data met basic quality standards. While things have improved, Gartn

We’ve talked about the core data quality dimensions and how to think about them with intentionality. We’ve explained how to put those in a framework that is guided by your business. Let’s use this page to talk about the actual foundational data quality checks you should be running on your data, and how dbt Cloud can help make these tests simpler and automated. We’ll cover the concepts of: uniquene