>> Woman: From theCUBE studios in Palo Alto and Boston, connecting with thought leaders all around the world, This is a CUBE conversation. >> Hello and welcome to today's session of the 2021 AWS Global Public Sector awards for the award of best migration solution. I'm your host Natalie Erlich and now we're joined by very special guests. We have Omer Enaam, application modernization leader at Deloitte Consulting and Bart Mason, technology lead for the Office of Recovery Services at the Utah Department of Human Services. Welcome, gentlemen. Good to have you on the show. >> Thank you. >> Thank you for having us. >> Well, terrific. I'd love to hear more about your migration from mainframe to AWS. Bart. Let's start with you. >> The state of Utah has a mainframe system and we have our child support application that was first developed in 1996 on the mainframe written in COBOL. The application served us well through the 24 years that we had it running on the mainframe. The issue was that the mainframe, it was getting difficult to find people who knew how to program in COBOL. But the biggest problems were any type of modernization. We were pretty much stuck to using what are called green screens, and there was no real easy way to do any type of modernization. And a lot of our applications that were public-facing or employee-facing, a lot of those web applications had to be written in a separate system and set up to connect and talk to the mainframe system. So it was a system that served us well but it was time to try and figure out what are we going to do about this? Because the mainframe was expensive and it was old technology that didn't let us advance to where we wanted to go in the future. So roughly about 2016, we started to investigate what are the possible ways that we can migrate our child support application off the mainframe. And we went through discussion such as a complete rewrite where we would start from the very beginning and rewrite our child support application. The child support application is a case management and an accounting system. And if we would have done a total rewrite we were told it would be upwards of $200 million to do a complete rewrite. We started looking at other possibilities and came across one possibility, and that is to do a migration off of the mainframe into the cloud. It would be a pre-session where we could do a lift and shift and basically take the code, change it into Java, and put it into the cloud running in EC2 instances. So it was an, we called it an intermediate step to modernization because it would get us one step to where we need to do, or where we need to go. And for modernization, it helps us to, since the program that it was, or the language it was migrated to was Java it made it so that we could do modernization. And we decided that if we did a lift and shift from the mainframe to AWS, that we could modernize at our own pace, we could modernize screen by screen or function by function. So it gave us the ability to control roll-outs and getting our application to where we needed to be. >> Terrific. And Omer, I'd love it if you could weigh in as well. What were, what was the support that you provided towards this migration? >> Yeah, of course. So as Bart pointed out, the state was looking for a approach that had high chance of success, high probability of user adoption with minimal impact to the organization. At the same time, have the ability to for the state to maintain and modernize at their own pace. So we work with Bart and explain to him a few options. And one of the options was using a automated coding data conversion approach where we take legacy programming languages like COBOL and convert them into Java. Just like translating the code from one language to another. And in the process, we guarantee that your your new system will work exactly. It will be functionally equal of what you do currently. And at the same time, it minimizes the risk. And it also allows the state to have no issues with their business continuity and additional training for their staff. So in a nutshell, we brought in a solution demonstrated to Bart and team and they bought into that, the idea that this is exactly what they want to do as a first step. And as we speak, we are working with the state to help them take that system in the cloud to the next level. Now we have unlocked the potential of digital transformation. Bart can build mobile apps in front of that application. That the state can. There are new analytics capabilities for that their employees can be more productive in providing services to the citizen. They can implement native capabilities from AWS to implement a process automation, implement some artificial intelligence-based tools to optimize the processes and make life easy and better for the employees, at the same time more importantly, serve the citizens in a better way. >> Mhm. And Bart I'd love it If you could share some further details on some of the considerations that you had such as risk and whether it could be used later in the future. >> The biggest thing, the biggest risk to us was that if we, as we migrated off the mainframe, there's a risk that we have to recertify our system with the Office of Child Support Enforcement in Washington, DC. When we build a system, the child support system, we're required to have them come in and do a assessment of our application and certify that it is an application that can be used for child support. If we would have done a rebuild from scratch, the risk would be that first a rebuild, from what we've seen can take anywhere from five to 10 years. I've already touched on how expensive it is, but it takes up to five or what we've seen, up to 10 years to do a complete rewrite. And the risk for us was that if we did a complete rewrite, we would still be on the mainframe for quite a long time. And we would have to have our system recertified with OCSE. And that can take anywhere from five to 10 years for a recertification too, so the risk was that if we did anything with the complete rewrites it would be several, several years going through rewrites and recertifications to get our system up and running in AWS. And the other problem would be that taking that amount of time would also, it would bring us probably not up to date with the current technologies as we did our rewrite because we'd be focused on rewriting that application and not taking advantages of services and applications that come up and can help us with our rewrites. So one of the biggest risks was that we'd have to do recertification with OCSE, With the migration, coming off the migration because it is a one for one migration where it went from COBOL to Java, we did not have to do a recertification. This allowed us to move the application as is and it functioned the exact same way that recertification was not a problem for us. OCSE said that, told us that it was not a risk or an issue that we'd have to take on. So the biggest risk was recertification for us but with the migration and moving into the cloud we went through their security processes and we came out without any big issues coming out of that. >> Fantastic. Thank you. Omer. I'd love to go to you now. What are some of the unique benefits of working with AWS? >> Sure. I think the biggest benefit is there, the extensive services that are available and having the the proven platform where you cut down your operational costs drastically. So comparing the mainframe costs with the Amazon cloud costs. Clearly the state has benefited a lot from the from a savings standpoint, infrastructure savings standpoint, and at the same time now, as I said, the the system is in the cloud, running on open architecture in the Java programming language, The AWS cloud provides us several capabilities natively which allows the state to use, to digitally transform the experience for the citizens and employees by implementing modern DevOps practices for for managing the, operating the system providing new capabilities to workers and supervisors for analytics to business process automation, having better call center integration capabilities and so forth. So there are endless opportunities. And the state is in the process of executing on a prioritized list Just before the pandemic hit, we worked with the state to lay out the future for their system and for their organization in the form of a one day innovation lab, where major stakeholders from the state gathered with Deloitte and we worked through a prioritization process and determined how we can take this system to the next level and really digitally transform the system and in the process, provide new services and better services to state employees and the citizens. >> Yeah. Terrific insight there. Now Bart, I'd like to shift it to you, asking the same question. What are your thoughts on working with AWS? Why choose them for this? >> We always have been looking at moving a lot of our applications into the cloud. We've been looking at that for several years. The advantages of moving to AWS is, from my point of view, and state's point of view, is that AWS provides a lot of services and it provides the capability for us to do a lot more for our applications. So for example, when we were on the mainframe, one of the biggest problems that we had was disaster recovery. We had a disaster recovery site in the Southern end of our states with another mainframe that we would sync up with our application. The problem was that we have over a hundred data connections. We connect to banks, external entities, internal entities. We have different types of connections. We have to do printing. We have to print checks and several things. Disaster recovery on the mainframe was something that we were never really capable of doing. We could get our application up and running but it just sat on the mainframe. We had no data connections, all that was extremely difficult and extremely expensive to do for disaster recovery on a mainframe and on alternate sites. Moving to AWS, one of the biggest things for us was that disaster recovery requirement. Because now that we're in AWS, it makes it more easier for us to spin up servers once servers go down, restore servers when they go down. We have all of our data connections in one location, and as systems become unrecoverable or have issues, it's easy for us to spin up another one or several in their place, or even our data connection, because they're all located in one place and we're using them all of the time. So disaster recovery was one of the big key components for us. The other component was that, as we modernize our application, we're looking at what AWS services are out there to help us with modernization. We're looking at services such as AWS Batch to replace our batch system. We're looking at databases to replace the current database that we're using. We're looking at using containers to containerize our applications and our ORSIS application, and also microservices. So moving off the mainframe was the first step and putting it all into servers on an EC2 instance. But then we look and say, okay, how can we do this and make this more modern and run better and more efficient? And then we started looking at all the AWS services that are out there, that run outside of an EC2 instance, for example. And we see that there's an endless possibility, and endless capabilities that we have at our fingertips to say, okay, we're off the mainframe less modernize by moving to Batch or let's start looking at containers and things like that to help us with our applications. So disaster recovery and the available services that we can move to to help us with our applications, what we look at. >> Well, thank you both so much for your insights, Bart Mason, Utah Department of Human Services as well as Omer Enaam, Deloitte Consulting and LLP. I'm your host for theCUBE. Thanks so much for watching. (outro music)

>> Narrator: From around the globe, it's theCUBE, covering HPE Discover Virtual Experience, brought to you by HPE. >> Hello and welcome to theCUBE's coverage of HPE Discover 2020, The Virtual Experience. I'm Lisa Martin and I've got a couple of guests joining me, Stephen Laaper principal at Deloitte consulting and Brenna Sniderman the Executive Director for the Center of Integrated Research at Deloitte Services, Stephen and Brenna, nice to have you on the program today. >> Thank you, >> (mumbles) >> So we're going to be talking about The Smart Factory. I'd love for you to start Brenna, we'll start with you. Give our audience an overview of Deloitte's definition of The Smart Factory then we can dig into some of the very interesting research that Deloitte has been doing the last few years. >> Sure, absolutely. So the way we think about The Smart Factory is it is a system that's quite flexible that uses data and information from throughout, physical assets to optimized performance, to enable the facility to be more agile, to be proactive, to optimize its assets and to react and change as quickly as possible to shifts going on. It overall enables organizations to just be more intelligent about the way they use their assets to use data, to make more informed decisions and to drive a more optimized process. >> And Stephen for you, one of the things that I found interesting looking at some of Deloitte's research is that the last few years or so, there's been net zero growth in manufacturing labor productivity and labor productivity being an indicator of economic impact. Why in Deloitte's perspective, has that manufacturing labor productivity growth been flat? >> Yeah, it's a really interesting observation. And what we've seen is really decades and decades of management principles, companies using things like Lean, like Six Sigma, taking advantage of labor arbitrage in many cases. And the reality is that a lot of that low hanging fruit is gone. Those projects have been executed well and we're now seeing what we would consider to be diminishing returns as it relates to the investments in those same types of tools. And that is really what's leading many organizations now towards things like the capabilities that you'd find in a Smart Factory. Adding additional technologies to the capability set to really bring companies to that new productivity frontier. >> One of the things that I saw too, is that Smart Factory adoption in one of your studies, can result in a threefold productivity increase. So talk to me about in the last few years, some of the early adopters, Brenna we'll start with you, what are some of the trends that you've seen with those early adopters? any industries in particular that are leading in that respect? >> Well, that's a good question. I think when we recently published a study on lessons from early adopters in the Smart Factory and what we found was that a lot of the organizations that have adopted the Smart Factory have learned lessons that are not necessarily new but some that are new as well. Really I think the biggest challenge has been to figure out how to gather data from a lot of assets that maybe haven't had to produce data before to find out where all the information is from throughout the facility to bring together different groups and different cultures within the organization, whether it's IT and OT and have them figure out how to share information and data and really just to figure out what to do with that information once we've gotten it. Some of the organizations that we spoke with for our research really ran the gamut from aerospace to automotive, to consumer products, to industrial manufacturing. It really has been an interesting spread that we've looked at. >> Stephen walk us through the last three years or so of research that Deloitte has been doing into the Smart Factory from the 2017 study to the 2019 study, to the one that was just released, what's some of the progress that you've seen over the last three years? Is it what you anticipated it would be? >> Yeah, it's interesting. I mean, three years ago, I think a lot of people were talking about Industry 4.0, they were talking about the industrial internet of things, they were talking about The Smart Factory, but we saw relatively few very concentrated efforts to advance those. Now as we fast forward three years, we're seeing that the specific capabilities that each one of those topic areas can enable for organizations, has become much clearer. So correspondingly companies have been planning for these types of investments and they're taking action on much of the capability build and quite frankly, starting to see the value. One of the underlying kind of architectural elements that I think are critical as part of the modern Smart Factory is exactly what Brenna touched on. And that was as it relates to the data. Many assets out there even if they're several decades old likely have a wealth of data associated with them. The challenge is that data is either not readily accessible or it's not well understood. And much of the effort that organizations have now undertaken is not only how do they connect, extract and use that information many times on a real time or near real time basis, but now also combining that information with other assets, other parts of the manufacturing facility, or even their manufacturing network to generate that value. >> So Stephen follow on question, how does an organization, a company start that process, if as you said, there's myriad assets of varying age, some really advanced, some really old as well as even from, I guess, a generational perspective in the workforce, you've got multiple generations, for organizations that know we've got data that's hidden, where do they start? >> Yeah, absolutely. And I think a really important element of your question is how do you determine where to start? And the reality is that not all of these solutions are created equal. Not all of the assets have data that's interesting enough to be equal. And so really going through a very concerted effort to understand what are the capabilities we're trying to build And what value does it create for our organization? Aligning that to the objectives and the goals of the organization is critical right from the outset. And we see companies that are being most successful in their implementation of the Smart Factory, following that value orientation. And that might not mean that that value comes tomorrow, It might not come next month, but there's a very clear guidance in terms of how the particular capabilities that are being built will lead to value. Organizations that are not doing that, we tend to see random X visual. We see a lot of different efforts underway with very little tied value and correspondingly many of those efforts don't continue because the executive team, the shareholders aren't going to continue those investments in that space without showing them (mumbles). >> So Brenna walk us through, along what Stephen was just saying. I was reading in your 2020 study that positioning a Smart Factory initiative for value starts with human-centered design and I thought this was really interesting that Deloitte research demonstrated successful teams generally focus on the user first, not the technology. >> Well, yeah. And I think to follow on a little bit to what Stephen said about understanding the value and the goal of what you're trying to do before thinking about the technology you need to rush out and implement goes along with this as well. You want to think about what the user is actually going to be using that data for, what is their job, what information are they going to need and think about from their perspective, what is going to be most helpful and effective for them. And I think the value of this is twofold. One is if talent within your organization and folks on the shop floor, see the value of this data and information, they're going to be more inclined to adopt it because it makes their job easier. But also if you have a tremendous amount of data and information from all the different assets and parts of your facility, if an individual has to sift through all of that, to find what's going to be valuable to them, it's not really going to make their job easier. So human-centered design is really thinking about what that individual needs to do their role, and in a lot of the work that we've done, we've almost thought about it as personas where this particular persona or job needs this information, needs to go through these steps and here's the data information we need to show them to enable them to do that. It's just a way for people to leverage information, to make smarter decisions more quickly. >> How does a manufacturing company do that, Brenna, excuse me, without being siloed, like in business units, so I'm thinking, getting cross-functional support all the way up to the top level. >> Mhhh, that's something that we saw quite a bit in our research that many of the groups or organizations that have successfully enacted a Smart Factory have done so because it's not just coming down from the top, it's also coming up from the bottom. You know, although that may sound like a pejorative term, but coming from all angles of the organization. So we see from the strategic level, this is what we need to do to change the way our organization operates in a more effective way. But from the line of business individuals that are using this information and data every day, we need to think about sort of having a groundswell of support work there as well, so that our team members are using this information. So I think it has to be something that comes from throughout the organization. What we've also found your point about silos is bringing in diverse teams and individuals from throughout the organization who have different types of expertise, different perspectives, different things that they're looking at in different ways that they need to use this technology to do their job, will enable us to make sure that, what we're producing is something that's going to be of value to them. >> And along those lines, Stephen question for you, this must need to be looked at, not as what can we do today or the next six months, but over the long-term. So that ongoing enablement and education is going to be critical. >> Yeah, absolutely. Right. And you know, the reality is that some of these investments that organizations are making into Smart Factory, do take quite a bit of thought, research and assessment and those aren't investments that they're making for the short-term, many of them are long-term. The important part about those investments that organizations are making is that they're creating platforms by which teams can continue to evolve the persona-based type solutions that Brenna referred to, so critical. And so, the flexibility, the adaptability, the agility of those platforms and the investments that are being made, really is one of their critical factors. I did want to just revisit the user adoption of these types of solutions. And, I'm a engineer by education. And I could look up back to early in my career and say, "Hey, look, I built solutions, "using data for manufacturing shop floor equipment. "And I created those solutions for others." But the reality was that I created it in a way that an engineer would you consume that data. And the reality is the persona-based approach really lets us focus on how is a particular individual in their job going to consume that data in a way that enables them to make the best next decision which ultimately has a positive outcome for the company. And in some cases that might mean not exposing them to all the complexities that happen underneath the surface. The modern smartphone, for example, enormously complex device, yet intuitive to use, easy to pick up, easy to interact with. The modern Smart Factory is also very similar in that frame. >> Along those lines of agility, but also designing with certain mindset, culturally IT and OT are different. Brenna, one of the things that I found interesting in the research was the marriage of IT and OT, how do you advise or let's go to clients that were part of that 2020 study, what lessons can the next wave of adopters learn where it comes to bridging those two IT OT mentalities and different cultures? >> Yeah, that's a good question. And I think the different cultures is sort of, key insight that is helpful. With respect to IT, they work on different timeframes, they think about investments in a different way, they think about technology in a different way than individuals who are in OT, who are on the shop floor, who are using these tools every day. and what we found was that bridging that divide and bringing them together, is a challenge that many overlook and something that really the importance of it, can't be overstated. I think to get back to Stephen's point about adoption, if those within the OT space have an understanding of what IT is doing and why, they're just likelier to adopt and to use. And conversely, if those in IT have a deeper understanding of what those in OT are doing and what types of tools they need, they're likelier to come up with solutions that are going to be effective. I think the cultural divide is something that's practically important to understand, to address and not to overlook because I think the last thing that anyone implementing any sort of Smart Factory solution wants is to roll out a solution that was sort of baked in one area, but not taking into account the other as well. >> Great point. Stephen, I want to go back to you for a second. Just understanding along the lines of the cultural differences and the design principles that need to be factored in. When the COVID-19 pandemic hit in March of 2020, for clients that you were talking to that were in whatever stage process of rolling out Smart Factory initiatives, where are they now? And what are some of the advantages that you see that organizations that aren't yet adopting Smart Factory initiatives should be doing to prepare to thrive in this new normal? >> Yeah, absolutely. Let me start with some of those advantages right at the outset. So many organizations now have been looking at advanced solutions, perhaps, to enforce social distancing within the manufacturing environment or perhaps contact tracing within the manufacturing environment and the advantages organizations are seeing that are already on that Smart Factory journey is they're finding they have largely a lot of the infrastructure required to be able to do that already in place. So that has been an enormous accelerant for companies that are already on the journey. The reality is that many organizations, are unable to have their experts, their engineers, their vendors, many of the people that are supporting the equipment and the people in their manufacturing plants around the world, they're not able to get them there. And companies that have been on the Smart Factory journey, specifically as it relates to creating what we would call the digital twin of many of their assets, where they can now see not only visual representations of those assets, but can also see that the data flowing off those assets and in the most advanced solutions, being able to see those together, they'd be able to unlock remote support, in a way that organizations that have not been on this journey simply can't. And we're starting to see some very distinct results, as it relates to those who are able to continue running at scale, and those who are struggling in the COVID environment. >> And Stephen, last question for you. I know you've got a session or a demo on Smart Factory an AI that you're doing at Discover 2020. Tell us a little bit about that and what the participants can anticipate. >> Yeah. So we're really excited to be able to bring Factory AI as we call it, in a live virtualized session. That session is going to cover what we have built around we'll call it a mini manufacturing line. And usually we'd have that with you at the conference, or we take that around the country, to many of our manufacturing clients to really show them, the power of adopting many of these different types of capabilities in the manufacturing environment. So what we're going to be showing and what viewers can expect to see is a demonstration of edge capabilities, of computer vision, of advanced internet of things, all wrapped into several high-impact use cases. So we're looking forward to you're doing that. >> Excellent. Well, Stephen, Brenna, thank you so much for your time discussing The Smart Factor. This is such an interesting provocative topic. I wish we had more time, but appreciate you speaking with me today. >> Thanks for having us. >> Thank you. >> You're watching the cube, Lisa Martin for HPE Discover 2020, The Virtual Experience. Thanks for watching. (upbeat music)