When queried to unpack the idea of a “data liaison” more and provide additional clarity and whether this person could be a “project manager”, Miner indicated
“…in a consulting construct, that both myself and Niels [co-founder] provides in some of our larger projects
When queried to unpack the idea of a “data liaison” more and provide additional clarity and whether this person could be a “project manager”, Miner indicated
“…in a consulting construct, that both myself and Niels [co-founder] provides in some of our larger projects. And it’s a really necessary role and some of the other customers we work with, we’ve made this recommendation for them to do this, it’s actually two reasons. One is that, data science requires a lot of focus. When you’re working on data science problem and you’re fumbling with some machine learning thing, you’re messing with the data, an interruption can break down a house of cards in your head that you’ve been building for multiple hours and if you’re responsible for going around to random meetings to discuss use cases and things, you’re never going to get anything done…what you need to do, is you need to kind of pick somebody. I mean honestly, these are some personality types that are better than others, but really it needs to be somebody that could do it if they had to, that understands the real problems, that can represent the data scientists that are actually going to do the work in these meetings. But due to the focus requirement you kind of need to pick somebody to be the sacrificial person to do it, that’s okay going around and talking from experience so that the others can focus. It’s a really important role… in a large organization with a large team.” — https://blog.dominodatalab.com/collaboration-data-science-data-engineering-true-false/
“…in a consulting construct, that both myself and Niels [co-founder] provides in some of our larger projects. And it’s a really necessary role and some of the other customers we work with, we’ve made this recommendation for them to do this, it’s actually two reasons. One is that, data science requires a lot of focus. When you’re working on data science problem and you’re fumbling with some machine learning thing, you’re messing with the data, an interruption can break down a house of cards in your head that you’ve been building for multiple hours and if you’re responsible for going around to random meetings to discuss use cases and things, you’re never going to get anything done…what you need to do, is you need to kind of pick somebody. I mean honestly, these are some personality types that are better than others, but really it needs to be somebody that could do it if they had to, that understands the real problems, that can represent the data scientists that are actually going to do the work in these meetings. But due to the focus requirement you kind of need to pick somebody to be the sacrificial person to do it, that’s okay going around and talking from experience so that the others can focus. It’s a really important role… in a large organization with a large team.” — https://blog.dominodatalab.com/collaboration-data-science-data-engineering-true-false/