ESG Videos

Announcer: The following is an ESG 360 video.

Mark: The term digital transformation is a very popular term right now. Like many things, digital transformation, IT transformation, one might hear data platforms and so on. They all sound aspirational, they certainly sound good. But one of the things I want to do is drill down a little into what really underpins such capabilities because it's one thing to put them on a PowerPoint, it's entirely another to actually deploy them. I'm joined today by Mike Leone, my colleague who covers many things, hyper-convergence is one. But today, we're gonna concentrate, Mike, on data platforms, data analytics, which I'm looking forward to because I must say I don't know a lot about either of these things. So first off, are those part of the same thing, those two terms and what are they?

Mike: It is, yeah. Great question. So data platforms are the underlying, let's say, hardware and software that help consume and make data actionable, and obviously, you need some tools to do that. So data platforms help enable that almost end-to-end view of your data, including how it's stored, where it's stored, connecting data sources, being able to transform and really make consumable to your analytics engines to make those data-driven initiatives or meet the data-driven initiatives that you have in your organization and create some insight into your business, what you can do better, what you can't do better, all that kind of stuff.

Mark: When you say data-driven and make data actionable, let me very clear, you're not talking, I think, about an actual application per se, you're talking about how we manage, optimize, arbitrage, improve the management of our overall data, correct?

Mike: Both actually.

Mark: Oh, okay.

Mike: Yeah. So I mean, organizations are building business intelligence applications on those data sets to really take it into the business analyst and say, "All right. Let's look at this data, right?" A good example is, think of a retailer. I need to know in real-time or close to real time, "Hey, people aren't buying blue shirts anymore." Part of it is analytics on the infrastructure and understanding, "All right, something in the infrastructure is broken. I need to fix it. I need to be predictive about this. I need to understand all of this." And there's so many great buzzwords in the analytics and data platforms space. There's AI, there's ML, there's IoT, there's smartek [SP]. There's all these terms that people are throwing around and data platforms really encompass all of them.

Mark: Right. I do wanna come back. That's really interesting. So you're saying it is a terminology that encompasses both improving IT but then also using improved IT to improve business.

Mike: Right.

Mark: Yeah. Okay. What for the uninitiated, which again, I'm guessing is me clearly, what is critical in that area of focus, that industry right now? What do I need to know about this part of the industry as it progresses?

Mike: Yeah. I mean, we see it a lot on the cybersecurity side with the lack of expertise and that's creating a big issue for a lot of organizations. While there's a lot of experts in the analytics space, it's still really hard for organizations to get a grip on just their data in general. It's so big, it's constantly growing, and then on top of that, it's figuring out, "All right. Well, what do we want to do with this data? What type of insights are we looking for?" So it's a matter of understanding, one, the data you have, which is a challenge, and two, how can we make the data we have actionable?

So a lot of the challenge right now is organizations are trying to figure out how to gain insight from the data that they have and all of the data that they have. It's easy to take one database and one data warehouse and look at the data and say, "Great, I can derive some value from this." But when you're starting to look at these massive organizations that have 50 different data sources that are all generating different data types and different data sizes, being able to dip into all of those pools of data, it takes a lot of work and a lot of effort to get there.

Mark: And that work and that effort, I'd like to just drill down a little into how that's done. You talked about the availability of expertise, staffing. Is that about deployment and policies? In other words, does the software then take over the actual management and the regurgitation or the discovery of these insights? So how much is manual is what I'm getting at and how much is or should be automated?

Mike: It's funny that you mentioned manual. You know, the open source community is a big driver of, I think, analytics and data platforms. And with open source, there's a lot of the challenges associated with support expertise across tools, integration of those tools, etc. So what we're starting to see now is that there's a lot of organizations that are looking to simplify, simplify everything from the consumption, the integration of data sources, to actually getting to the point of visualizing. And because of that need for simplicity, that need to reduce the complexity of workflows, of management of data, of the actual analysis, there's a big shift into consuming cloud resources and cloud services to be able to do your analytics. And that's why the big players, the Amazons...

Mark: Actually doing the analytics in the cloud.

Mike: Right.

Mark: So not just using the resources out there to manage your on-prem application, but actually doing it there.

Mike: Exactly. Right. So it's a nice on-ramp for organizations that are not necessarily just looking to get started but looking to expand what they already have. So I can go to the Amazon, Google or Microsoft and they have a number of services that are easily consumable for me as an end user who just wants an easier on-ramp. Regardless of where the infrastructure lives, I just need help getting on the analytics path.

Mark: How far are we down the track here? I mean, are we really quite good already or are we just scratching the surface?

Mike: I would say we're somewhere in between. The interesting part and you mentioned this, the interesting part with these data platforms and this end-to-end stuff and where we are, you know, there's organizations that have been doing analytics forever. There's business analysts who have been in those roles for a number of years trying to gain insight into their data. The whole concept around this data platform and where we are, it's a matter of empowering the business analytsts to do more, it's empowering IT to make it easier for the analysts to do more and consume the data effectively, and then it's taking that, and I haven't mentioned this yet, the data scientists who are doing the crazy far out stuff that we don't even wanna wrap our heads around, empowering them to just go deeper.

Mark: You've given us plenty of things to think about, Mike. Thank you very much. I hope you enjoyed watching.