Root Cause Analysis
Creating rules and expectations is one thing, as it allows you to detect any issues in your data, but troubleshooting is another.
An observed system should give you many clues and means to check the origin of the error, which will lead to efficient data issue resolution.
In a company, resources are key. The team’s time should be dedicated to value creation, not maintenance or troubleshooting under pressure. You need to know how to use the resources efficiently to avoid wasting time and, ultimately, money.
The best way to keep these costs under control is to evaluate them using key performance indicators (KPIs). Some interesting team or project metrics that you may like to follow include the mean time to detect and the mean time to resolve. The former designs the period between the incident’s occurrence and its detection, while the latter describes the amount of time spent resolving the issue. The goal of the head of data, and all data engineering...
