disclaimer: I am the CPO of Trackingplan, a SAAS observability and data quality solution for digital analytics and marketing data
Based in your experience, I would like to know how you currently detect problems in your data and how much effort does it take to maintain a minimum of quality?
- Do you have a dashboard to monitor them?
- Do you carry out manual QA processes from time to time or per release?
- Do you have automated tests?
- etc…
Thank you in advance
Great question, David.
Just to be clear, are you asking how does data.ai maintain the quality of our data? Or are you asking other data.ai users how they maintain the quality of data at their own companies?
I'm curious how community members are handling it in general. But yes, of course, it would also apply to how they ensure that the data that arrives at Data.ai is correct.
Does data.ai have its own data quality module?
It is often said about analytics, but it also applies to AI models, as you know; garbage in, garbage out 😅
Thank you Alex
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