To create a high-quality data team, organizations need to prioritize a consumer and end-user focus, technical excellence, and strong teamwork and communication across the entire team.
Putting strong governance processes in place throughout a machine learning model’s entire life cycle is vital to minimizing potential harm and keeping a strong relationship between stakeholders and AI governance.
Data literacy is one of my key concerns. There's a real risk that you build a bit of an ivory tower, and if that's the outcome, then where we're not winning in my mind. So, the role of a CDO is to help people better understand data and to demystify both data and machine learning. I think the way to do that is to make it relatable, and if possible, to make it fun.
Empowering the organization to work with data is a key challenge for a lot of organizations, and definitely for us, as well. It's something that we spent a lot of time over the last year grappling with, and I think there are a few approaches to tackling this. First, I think you have to put training in place to equip those core coding skills and data knowledge. What we found historically is there's a real risk of bottlenecking because data analysts are seen as the only people that can get data or can pull information. That just creates tension and frustration everywhere, but if you can get some basic training in, that's probably going to serve 75% of your needs.