Trust in data breaks when numbers are wrong. Data teams need clear rules about what counts as valid, like whether an order can be zero or negative, or how high is too high. Without these definitions, executives stop trusting the data and stop using it.
Trust is one of those things that when you lose it, it's really, really tough to get it back. Like if you put something out for a business group and, you know, the numbers are wrong, it's probably the last time, especially if it's an executive audience, it's like the last time they'll ever touch your work, right? But I mean, trust is, you know, comes from having good data quality. And, you know, in order to have good data quality, you have to be able to define what What is good data quality? Like, what does it mean to have, you know, what is the data quality around a value of an order, right? Like, what is a bad value? You know, should an order ever be zero? Should it be negative? You know, should it ever exceed $1 million? Like, there's all these things that, you know, data teams, we can't, you know, weigh in on, right?
