Understanding human failures in data
Tiankai Feng discusses human errors in data, highlighting miscommunication and change management as key challenges in data quality and strategy.
Transcript
I always realized that the biggest problems and the reasons why data efforts fail are human. Right? So it's always like a miscommunication, a lack of knowledge, unevenly distributed expertise. Right? Something always is there where it led to a wrong decision and the wrong decision then is impacted by how data is there, at least the compliance issues, etcetera, etcetera. And I kept it with me from all sides, right? As an analyst, I realized I am a weak link in the whole thing, right? Because if I translate it wrong and I have it, then I'm the problem. It was about working and focusing on data quality. I don't know where it comes from, but human beings are manually entering data. And of course they're also human beings, so that is a problem too. But also people in between don't talk to each other, so they don't know why the data exists the way it exists. They just assume things and then misinterpret it too. Also on a strategic level, change management overall is a big topic. And you don't do change management well, then you're gonna end up with people sabotaging it or being really passive about change. And that itself is a problem, right? And all of this, I realize this is a big issue, but there still seem to be a lot of people actually writing practical advice about the human side of data.