Tony Bailey - Modernizing Legacy Applications and Infrastructure

Tony, welcome to the show. It's great having you here today. Want to add anything before we jump in?

Tony Bailey: Thank you, Tim. I appreciate you having me on. Look forward to the conversation.

Tim Butara: Awesome. Awesome. So the first thing that we need to discuss to kind of set the stage is obviously why is it so important to modernize these legacy apps or legacy infrastructure?

Tony Bailey: I think the list is long, but I think if you, if you break it down to a few key things, I think there's security and compliance. I think security is at the core of everything that we have to do today. You know, there's a lot of bad actors that are trying to do all kinds of bad things to us every day. So I think, When you're when you're creating your strategy for the future, I think you start with security.

So I'll say security and compliance because you have to meet the regulatory needs with anything that you're going to do. I think with data modernization, you also have the ability to leverage efficiencies. So I think a lot of legacy data centers and such, they tend to get more and more expensive over time.

They tend to become more unreliable over time. And, you know, obviously the cloud is getting literally hundreds of billions of dollars of investment in there. It's a, it's a, you know, it's not going anywhere and it's a very effective platform for most. I think the last piece is scalability and agility.

I think by allowing the data modernization to occur, it allows you to open up the insights of that data in ways that are not difficult, if not impossible in the Legacy environments of today to bring them all together and provide those insights through machine learning or AI or generative AI.

I think there's, there's a requirement to move to data modernization or take advantage of that.

Tim Butara: Yeah, I was just going to ask that, that probably it's even more important now with AI being all over the place.

Tony Bailey: Yeah, I think that, you know, it's funny because it's like the hottest topic in the world, right? I mean, generative AI is going to solve this.

It's going to erase all the jobs. Yeah. As pros and cons, obviously, in the arguments that are made, but I think, you know, at the end of the day, it's an opportunity. And I think in order to take advantage of the promise that generative AI could bring to us. It all boils down to the data, and the data, you know, needs to be clean, it needs to be, have bias removed, it needs to be compliant.

There's so many things that are required for generative AI to actually work to its fullest. It all starts with data, which leads to data modernization, obviously.

Tim Butara: It's interesting that it's, yeah, it's not just AI, it's basically anything to do with the digital has to have really on point, responsible, well put together, efficient, like data usage and how data is handled, avoiding stuff like, you know, data silos, stuff like that.

That's right,

Tony Bailey: that's right. And legacy environments are full of silos. And full of, you know, unstructured data that could be all over the place and so it requires a thoughtful exercise to bring it together and leverage the power that's in the data.

Tim Butara: Yeah, that's a very good point. So obviously, AI is like one major case where this is particularly important.

Are there any other important cases or like sectors where this modernization is particularly important that we should mention here?

Tony Bailey: Well, I think, I think it applies across. I think, you know, at InterVision, we, we service clients in the commercial side as well as public sector. And I think they have different, you know challenges or opportunities.

It depends, again, how you look at it. Public sector tends to be much more regulated, but on the commercial side, things like financial services and health care, obviously are heavily regulated as well. So I think both of those bring a, Compliance requirement that is becoming much more stringent and the penalties can be severe, right?

If you're not compliant with protecting my private information, it could, you could have monetary fines, it could have brand impact, could lead to loss of revenues. I mean, there's all kinds of implications that go with it in those regulated areas.

Tim Butara: So what, what are some strategies and best practices for modernization?

Tony Bailey: Well, I think as I alluded to earlier, I think there's a bit of a, you know, start with a thoughtful process that you're going to go through. What's the data that's necessary? What systems do you want to put forward first? I think you want to start simply. I don't think you want to boil the ocean. I think you want to take some baby steps and take some, you know, less important applications or less important data and do the process so that you become more confident and competent in those areas.

But, you know. There's the data cleanliness. There's the actual data migration to the cloud. There's things like storage optimization because, you know, as you move the data to the cloud, it can quickly become very expensive. So you want to make sure that you have parameters and guidelines that help make it optimized based on the type of data you have.

You know, the the quality improvement and the ability to leverage that data for analytics. You know, again, I'm not necessarily saying generative AI, I'm just talking about analytics in general, the ability to look at the data, the combined data in a new light and shed new insights that will likely be very actionable in the process.

Tim Butara: Obviously, like the cost of data you mentioned, right, it can get very expensive. So that's, I see that as one obvious challenges challenge here. So what would be some other main challenges to, to keep in mind and to consider and to kind of plan for in this context?

Tony Bailey: I think it comes back to the desire to get help.

I think that, you know, to take somebody that's been managing an on premise data center and been managing a specific application for the last 10, 20 years and ask them to be experts at moving the data to the cloud, to be experts at modernizing the applications to leverage the cloud is a tall order. And I think there are companies Like InterVision that have years and years of experience and helping, you know, do discovery to understand the needs of the customer, create the white glove escort plan and then have key metrics at the back end to show that we've actually met the.

Intention of that modernization project.

Tim Butara: Do you have any, any examples like from your own work or like, like any interesting stories, maybe both of, you know, how things were really smooth and how things were really successful with these, with these initiatives, and maybe also an example of, you know, a case where it wasn't so smooth, where you had to do a lot of, maybe a lot of thinking, a lot of reworking, a lot of back and forth and stuff like that.

That'd be interesting to know.

Tony Bailey: You know, I've got probably two examples. One on the public sector side of a large utility had been around for decades and had just slew of content of documents. You know, whether they were PDFs or Word documents or whatever format they were in, they just had a slew of documentation and they wanted to bring it together and be able to present information to their customers.

And to the regulatory office departments in a efficient way, as opposed to searching through literally millions of documents to get to the right answer. They wanted to bring this all together through a modernization exercise and make that available. And we, we did a phased approach phase one took, you know, maybe three months and the results were great.

We moved into phase two, which is a much bigger. Subset of the of the information that's available to them. So I think, you know, on the regulatory side, taking, you know, very disparate and sometimes sometimes decades old information and bring that to light and to bear is, you know, I would say is the definition of data modernization on the flip side and kind of an interesting use case.

We had a client that takes in videos and takes in thousands and thousands of videos a day. And they have a requirement to do grading, you know, the, the PG 13 rated are, you know, PG, those requirements had to be put on those videos that were coming in, but the volume of, of the, of the videos coming in was so great that they couldn't meet it with manpower, they wanted to create an opportunity to take the video in.

On one hand, look at the audio and grade the audio to understand, you know, the, the words that are being spoken in the, in the video, as well as the video element. They wanted to analyze the video components to see what was being shown on on the screen. And so taking, you know, literally thousands and thousands of video, then running through this process to make sure that it was following their outcome is another example.

It's kind of an interesting use case to me that. You can grade videos in without people taking a look

Tim Butara: Yeah, I mean, I know that we've had probably stories of like not just in this case But I did this maybe think of it of like claims of something being done with ai, you know Like video processing but then actually there's a lot of manual work involved also But so so i'm guessing that you actually made it word that it was actually done, you know Only in an automated way

Tony Bailey: That's right.

Well, I'm sure there are outliers that if it was a little iffy or the definitive answer, then it probably still requires a person to take a look, but the vast, vast majority of being done in an automated fashion.

Tim Butara: But yeah, that's probably how it's supposed to be with automation, right? I mean, even if you automate everything, you have to have at least someone, you know, doing like the kind of fringe checks, I guess.

Tony Bailey: Yes, there has to be a quality assurance piece in there somewhere. Yes.

Tim Butara: And especially when it comes to AI, there's this term human in the loop, which, you know, at times might be a little bit of a buzzword, but I guess, you know, it's a buzzword for good reason, because it is effective at conveying exactly what you want it to convey.

And it also applies here very heavily.

Tony Bailey: And I don't think it goes away. I mean, I think those guardrails have to be in place in the process. They have to constantly be monitoring and you know goes back to things like data bias and all that stuff. You have to keep course correcting to make sure it's staying in the truth lane.

Tim Butara: Yeah, yeah, very good point. And so since we already started talking about the human aspect a bit more in addition to, to the tech and, and the very heavy data aspects, I'm wondering like about the people culture side, water, some of the most important things, important changes here in terms of like culture management, because obviously if we're talking about something legacy.

You know, there might be resistance, there might be, especially on, depending on the size of the organization, that there might be a lot of like challenges on the people's side as well.

Tony Bailey: Yeah, I think change management, I mean, this is something that's been going on forever, right? I think change management is critical.

And the way that we approach change management is, you know, the, it starts at the top down. If the top is not convinced, That they should be a part of this project and they're not being held accountable to the project. I think that's the 1st failure. I think the 2nd point is, is how do you upscale your existing resources?

And that upscale process is a process is not everyone that's been around for 35 years is going to suddenly become, you know, a data modernization expert or generative AI expert without significant training. And so you have to build that into the process. You know, I think how things were done and how they could be done are very different animals.

And. You know, if you're going to do it thoughtfully, there are certain aspects of your business that you want to be managing with your internal resources, but it also opens up the opportunity to leverage experts in the field as. You know added value to the process and potentially running it in the long run for you because, you know, sometimes there are relatively low level or easier to do tasks that if you take your high expensive resources internally and have them doing relatively menial tasks, you're mismatched on the skill set.

So oftentimes we see companies will outsource the lower level and focus their resources on the more strategic elements of the business. And so I think, you know, it's, it's not, it's not one size fits all. I think each individual customer has a different need and a different journey, but change management and upscaling are probably the two critical elements to the, and I would also say a culture of innovation is also important, right?

And I think you have to. We're not going to be the company of old. We're going to be the company of tomorrow. And you have to foster that belief that innovation is good. It's going to lead to better customer outcomes. It's going to lead to, you know, better employee satisfaction. It's going to lead to better revenues or whatever the outcome of the organization's innovation move is.

Tim Butara: Yeah, and it's all connected, right? Because this innovation driven culture should kind of be promoted and fostered top down, as you mentioned before. And it's basically just a part of the whole change management thing. That's exactly right. And I'm also wondering, since you manage the outsourcing, I'm wondering, two questions here.

The first is are there any extra considerations with outsourcing when it comes to stuff like security?

Tony Bailey: Well, first, I would, you know, make sure that this is not the first time they've done it. All right, I do think a lot of people have things on their website that says we're experts at this, but, you know, if you don't have references and, you know, similar industry, similar size, similar challenges, and someone can say, let me talk to those customers.

I think you have to validate 1st. I think the other piece is you have to have a clear set of KPIs. What does success look like? So you can't just say, I want you to move this. This application to the cloud that that's. 10 years ago, right? It's I'm going to move it. I'm going to drop costs and I'm going to increase customer experience.

I'm going to generate this much revenue. I'm going to retain more employees, whatever those metrics are, have a hard and fast set and hold them to it. And if, if along the way, they're not providing that white glove escort, they're not delivering on the KPIs. You have to find alternatives. You know, you don't just ride it into the ground.

You got to keep managing it and staying on top of it. And that's, I think, part of the change management. It's not just change management of the employees of the organization. It's change management of the project team to that happy place in the destination.

Tim Butara: Yeah, that was well said. And the second question here, maybe not necessarily restricted to outsourcing modernization related stuff, but just outsourcing in general, interested in your thoughts on how, you know, the, the prevalence of AI being able to do more and more, how that will impact companies outsourcing, meaning.

Will they be inclined to, to rather than outsource trust more and more of these menial tasks to AI instead of outsourcing or will there be stuff like maybe, you know, outsourcing the whole AI thing, the whole AI aspect to, to an external partner?

Tony Bailey: That's a tough one. I think it's going to depend a lot on company size and appetite for risk.

I think, you know, the, the market that we serve is the upper SMB and lower enterprise. And I think in that group, they're going to leverage third party experts more than say the fortune 500 or the global 1000. I think they have a lot more resources, and therefore they'll probably do more of it themselves internally.

But I think in the near term, the thing that I see that's working is employee productivity gains through tools like Microsoft Copilot or Amazon Q is a augmentation to make you better and more effective as an employee. But in the long run, it's going to do a lot more. It's going to help, you know, design new products and, and, you know, do all kinds of, Revenue optimization and next best actions and all these different things.

It's going to, I think a lot of that is going to be automated and it won't require as many either internal or external resources to help in those areas.

Tim Butara: Well, thanks so much for the great conversation, Tony. I really enjoyed your insights and I'm glad that we kind of expanded it a little bit into some sub areas that we didn't really plan on discussing, but I think that we delivered a lot of value.

To anybody listening right now, and I hope that both of us also really enjoyed it. Just for anybody listening right now who maybe want to learn more about you, or maybe get in contact with you, what's the best way for them to do that?

Tony Bailey: Well intervision.com is the, is the website. And in there is a multitude of areas.

We tend to focus in four centers of excellence. We have a cloud center of excellence, a cybersecurity workforce, modernization and modern infrastructure. Those are the four areas that we have a very deep capability in. And as I said, we, we service the upper SMB and lower enterprise. And I think in that area, there is a skill gap.

There is a lot of. Need open positions. And so they could either fight to try and retain those employees themselves or they can leverage experts that have been there done that and likely in a more cost effective way than Trying to find the unicorn out there to help you in a particular space So please visit intervision.com. Again, i'm Tony Bailey. I'm the EVP here and email is tbailey at intervision.com if you'd like to reach out.

Tim Butara: Awesome. We'll make sure to also include everything in the show notes. And Tony, thank you again so much for your time, for your insights. It was really great chatting with you today.

Tony Bailey: Tim, I appreciate your time. Thank you.

Tim Butara: And well to our listeners, that's all for this episode. Have a great day, everyone. And stay safe.

Outro:
Thanks for tuning in. If you'd like to check out our other episodes, you can find all of them at agiledrop.com/podcast as well as on all the most popular podcasting platforms. Make sure to subscribe so you don't miss any new episodes and don't forget to share the podcast with your friends and colleagues.