Versatility in technical and business lineage key to Data Governance success

Raluca Alexandru

0:00 It, even if it's not a process, but it's sort of, you know, if you look at lineage, for example, that's also affected by the complexity of metadata. How do you keep track? How do you understand the provenance of different data sources, and how do you look at it from a, you know, from an end user perspective, and how do you understand the impact that you have on it? And there's a lot of use cases that you can bring, you know, forward in terms of this needs automation in a setup of a modern data governance program. So yeah, I'm not sure if that answered your question, is automation important? How is that...

0:32 How does that change - Yeah... The landscape of data governance, but yeah.

Austin Kronz

0:37 No, it definitely does. It's as, it's as much of what is happening and, like, what needs to happen to support, I think, some of the more modern programs that people are pursuing, right? If you're pursuing data mesh, you can't... It, it, it's not gonna be done manually. I think a lot of what happens with metadata, too, follows a very similar path as just data analytics, right? You kind of have the descriptive, diagnostic, predictive, prescriptive, and in a way, all that is is just an increase in automation. It's kind of the same thing. Like, your lineage example is perfect, right? There's something descriptively helpful about being able to see lineage. That's just one way.

1:12 You'll probably can get a little bit faster at your root cause analysis if you can see the data provenance, if you can see where data's coming from and where it goes. And then there's, okay, how do we get to that more prescriptive element, which is before someone breaks something in that lineage, how can we tell them in their pull request that you're about to break 20 dashboards if you make this change, right? I think that's how we, especially at Atlan, are starting to look at, you know, that flow of reactive to still reactive but faster, and then more of this proactive—... Approach to, to data governance, yeah.

Raluca Alexandru

1:43 Yeah, and you can see that in, like, in the changes that, that, that have popped up more and more in the last year, I would say, in, in lineage, just to keep that example, into tech— the, the definition of technical lineage and business lineage, right? So there was a need to split that up into having the business persona understand better and be able to control better the, the process that where they, they're involved in, and understand the impact that has on lineage, right?

2:08 So that separation, for me, it's already a sign of maturity and, you know, going towards that sort of holy grail of data mesh type of world and where everything is integrated and where everybody understands what their role is. But the technical lineage and business lineage, that, that was something that, you know, really underlined the importance of having different... Well, the same data with the same processes, but then presented in different ways and with different accessibility options to actually influence how that process goes, right?

Austin Kronz

2:37 Mm-hmm.

Raluca Alexandru

2:38 So that's a level of... And there's a lot of automation in there because... And I know you wanna go into the topic of low-code, no-code, of course, and how that serves the business persona, but that's a prime example, I think, of, of adjusting some of the data governance capabilities to serve that very important group of, of users that is, yeah, increasingly active into the data governance organization, so.