In right now’s data-driven world, organizations rely closely on correct information to make essential enterprise choices. As a accountable and reliable Knowledge Engineer, guaranteeing information high quality is paramount. Even a short interval of displaying incorrect information on a dashboard can result in the fast unfold of misinformation all through the complete group, very similar to a extremely infectious virus spreads by a residing organism.
However how can we stop this? Ideally, we might keep away from information high quality points altogether. Nonetheless, the unhappy reality is that it’s unattainable to fully stop them. Nonetheless, there are two key actions we are able to take to mitigate the influence.
- Be the primary to know when an information high quality difficulty arises
- Decrease the time required to repair the problem
On this weblog, I’ll present you easy methods to implement the second level instantly in your code. I’ll create an information pipeline in Python utilizing generated information from Mockaroo and leverage Tableau to rapidly determine the reason for any failures. When you’re in search of an alternate testing framework, try my article on An Introduction into Great Expectations with python.