Data Quality is STILL a fundamentally unsolved issue!
Having met a couple of customers in the banking sector this week in South East Asia, a real pain that have been constantly shared was data quality.
That's due to incomplete information, inconsistency, lack of standardized entries and so on. Issues that we all probably know of.
Not that's a surprise of any sort, but the fact that traditional ways are still used to tackle the ever existing and growing issue is what puzzles me. In an ever evolving world where we are witnessing advancements in AI and other areas at unprecedent pace, and seeing organizations still struggle with foundational challenge should not be the case.
It goes without saying the importance of proper data foundation. Anything less than that would lead to improper analytics, entity resolution, decisioning, etc. I'll probably need to (and will) write a blog about this in the coming weeks.
I'm keen to hear from anyone about their approach/vision in tackling data quality issues, and what are some of your most pronounced challenges?