KPMG Powered Data Migration enabled by Informatica

Powered Data Migration

Businesses evolve to keep up with our constantly changing world. Organizations combine entities, move apps to the cloud, and transform legacy systems to modern platforms. These actions make moving data part of normal operations. Since data’s the core of operations, migrating vast amounts can be complex and risky.

Data must arrive clean, complete, and accurate. Teams must also think about supporting processes, so systems function and deliver outcomes as expected.

KPMG developed Powered Enterprise | Data Migration, a business-led approach that considers processes, people—and technology—to enable the smooth transition of data.

Our thorough approach includes automated tools and services built to work with Informatica, and Informatica’s CLAIRE AI agent. Together they help reduce delivery risks and speed up migrations—so organizations can realize data’s value faster.

We use AI and machine learning to help identify inconsistent data, clean and move it, then validate accuracy. In recent client engagements, the approach has reduced manual quality assurance efforts by 85% and data quality issues by 90%—so leaders can be confident the data they need will be in quality condition, when and where they need it.

Many organizations have combined Informatica’s technology with our methods, accelerators, and experience for successful data journeys.

And here’s a look into an illustrative data journey. The Powered Data Migration process starts as the system extracts and moves data to a secure staging area. Next, our profiling engine uses pre-defined rules to validate source data. It checks data quality based on target specifications and publishes final validation results through real time interactive dashboards such as this dashboard showing profiling results and the state of data during each cycle by domain, module, or entity, helping your leadership visualize data quality throughout the transformation.

Dashboards enable accurate decision making and provide deeper insights into data errors and traceability through data cleansing and conversion. Reconciliations dashboards summarize reconciled results after the system moves data to the new platform to help ensure the migration is complete and accurate. Customizable dashboards highlight fallout so users can address data that didn’t load.

Whatever size data migrations you face, our business and technology professionals can help. We’ll assess the complexity, recommend deployment options, and help you securely move data faster with less cost and risk, so you’re prepared to take on new opportunities when they come along.

 

Holistic approach to data migration

Peter Dorrington (Executive Leaders Network Moderator)

Hello, everybody and welcome to this webinar brought to you by the Executive Leaders Network with KPMG and Informatica. My name is Peter and I'm going to be the host today. I'm joined by a wonderful panel of experts. What I'd like to do now is just introduce them one at a time. Firstly, could we start with Joe? Joe, if you wouldn't mind, a brief introduction.

Joe Updegrove (KPMG LLP):

Thank you so much, Peter, and hello, everyone. My name is Joe Updegrove, joining from sunny New York City today. I'm a leader in our technology and data practice here at KPMG. Primarily with experience, I'll say, across the data supply chain, so everything from the origination of data through the sourcing and integration of data, which will be one of the primary topics of today when we're talking about Cloud integration all the way through the consumption layer. And then a lot of the foundational components across the data management, data governance, solution architecture. But very much looking forward to the conversation today and excited to be here.

Peter Dorrington:

Great, thank you very much Joe. And next I'd like to call upon Don. Don, if you wouldn't mind a brief introduction.

Don Roberts (KPMG LLP):

Hey, everybody. Don Roberts, joining you here from Charlotte, North Carolina, and in the time of this recording, starting to experience some of the initial rains of the hurricane that are making their way inland. But pleasure to be here with you today, representing KPMG, along with Joe Updegrove. And working alongside our alliance partner, Informatica, today to share with you some of our experiences in moving to the Cloud and data immigration. And similar to Joe, I support KPMG's data management group and our integration services with experience ranging from Cloud data immigration, obviously, but data management, data governance, enterprise data strategy, and architecture. Pleasure to be here with you today and talk to you all.

Peter Dorrington:

Okay. Thanks very much. We're delighted to have you, Don. Next, I'd like to ask Jeffrey, if you wouldn't mind joining us and a brief introduction, please.

Jeff Rydz (Informatica):

Thanks, Peter. Hi, my name's Jeff Rydz. I'm from Informatica. I've been here about 18 years. I'm joining you today from my home office in Central Indiana. I am a Senior Director of Cloud modernization at Informatica and my role is helping our customers move from On-premises technologies to the Cloud with their data integration and data management capabilities.

Peter Dorrington:

Great. Thank you very much, Jeff. And last, but by no means least, joining our panel, I'd like to ask Josh. If you wouldn't mind, Josh, a brief introduction.

Josh Alpern (Informatica):

Sure. Thank you, Peter. Hi, everyone. My name is Josh Alpern and I'm also with Informatica, like Jeff. I'm also talking to you, like Joe, today from my home office in New York City. I've been with Informatica for about 13, going on 14 years. I've done a variety of different things at Informatica and throughout my career, but for the past several years I've been focusing exclusively on modernization. Helping customers that are using On-premises solutions to migrate and modernize to the Cloud.

Peter Dorrington:

Brilliant. Thank you very much, everybody. I'm just going to spend a few seconds just setting the scene for our conversation today and then we're going to get into our panel discussion. So then, if you're a modern business, you're a data business. It's no surprise I think to anybody today that use of the Cloud transformation apps, we're in a complex world. It's also quite volatile, uncertain at times, because it's still emerging as well as being complex and ambiguous.

And within that environment we're finding ourselves as businesses, also transforming ourselves, either organically through things like digital transformation or moving to Cloud or Cloud native environments or through mergers and acquisitions. As different companies become available, we're looking at either joining them into our stable or perhaps offloading a couple of others. And that's making it difficult to do things like forecast accurately about what exactly are we going to need in the short media, more especially the long term.

What is it that our leadership tends to be asking of us at the moment? One of the big things of course is that we're agile and we have flexibility to deal with these changes in things like scaling. As we uptick to meet demand, we need to scale quickly or perhaps what we need to do is focus on how do we introduce new technologies very quickly. We also need to be resilient and we need to be able to take into our stride the changes that are going on, so we need systems, which give us the flexibility to deal with unexpected change.

And we either have to navigate around it, so we pivot or we have to plow through it, in which case we need to make sure that we've got the capabilities to do so. All at the same time as keeping all of our data secure, not just customer data, but a lot of businesses, as I say, now, data businesses, that data has real value, so we need to make sure our data is secure yet available to the people that need it.

And then a couple of other things that come up quite a lot. We need to have oversight and management and accountability of what we're doing with our data, how we're using it within our processes and how we can answer questions that come up from time to time, so new opportunities perhaps on new challenges. And last, but by no more means least, we're constantly being asked to control our costs. It's a tough economic environment for many of us right now and so, cost control is really important.

Now, I'll just leave you with one final thought. A lot of organizations are looking at digital transformation or Cloud transformation and one thing I would like to say is that going digital or going to the Cloud and changing nothing else is not truly transformative. So, we do need to think a lot more about, it's not just lift and shift, it presents absolutely unique opportunities for us as an organization. That's our context for today. We live in a volatile, uncertain, complicated and ambiguous world, a lot is being asked of us at the moment as our businesses reshape themselves into new dimensions and new opportunities.

So then what I'd like to do is start off with our first topic of conversation and Joe, I'm going to come to you first because I'd like to hear from you, Joe, in your view, what's driving this need for migration into the Cloud? So, what's behind all of this change?

Joe Updegrove (KPMG LLP):

Yeah, thanks Peter, and that was a great introduction. And maybe just to tell a quick story when we talk about what is driving the need to move to the Cloud. It's been a long journey, so I remember first having conversations about moving to the Cloud with some of the clients and customers that I was working with back probably in the early 2000s. And here we are, 2022, we're still having those conversations and a lot of organizations are just now thinking through, "Well, should I move to the Cloud? What should my Cloud strategy look like? If I've already moved to the Cloud, do I have a diversification strategy that I need to be considering? Should I just sit on one hyperscale Cloud service provider or should I perhaps operate off several?"

There are now other Cloud data platforms that sit across all those environments, so I think it's an evolving story of where we've been and where we're headed, but it's very exciting. And before I maybe get to what, Peter, I'll talk about the why really quickly and I think you addressed some of that in some of your upfront comments. But I often see it as bucketed, maybe into four categories, so speed, number one, scale, number two, accessibility of the data, number three. And the key word, Peter, I heard you mention in your opening remarks, value and not just value from a growth and revenue perspective, but also value from operating efficiencies, cost reduction, et cetera.

And so, if I focus on each of those for a second, speed, everything is faster in the Cloud. Your ability to separate compute and storage, your ability to run multiple different services in parallel, your ability to ramp up virtual machines or ramp down, if and when needed, pretty much on the flick of a switch. You can't do those things on an On-premise environment. Oftentimes, if you're operating within a data center, you're having to stand up new hardware, make procurement requests. So much easier to achieve speed and achieve some of that performance within a Cloud environment.

The next one being scale. The new Cloud environments provide an incredible ecosystem of different services capabilities. You can reach new people, you can reach new customers, the ability to onboard. You heard me talk about separation of compute and storage. The storage capabilities have drastically increased over the last decade or two, so the ability to get massive amounts of data out on the Cloud, process that data. Huge difference from the Legacy On-prem environments.

Accessibility. A lot of organizations have been striving to bring data together. In On-prem environments, we move to large data warehouses, then a lot of us moved to Hadoop clusters. Now, we're looking at Cloud data platforms. Getting the data into one place is part of the challenge and that's a big challenge. Being able to then serve that data up to make it accessible to your end-user community or key stakeholders is the next really critical component of that. Cloud makes it really easy. Self-service analytics, offering up data marketplaces, data exchanges where the data can be made available and the users of that data can come access that data, make requests of it, and then make use of that data to get better insights, analytics, et cetera, really critical and something that the Cloud really helps to enable.

And then the last one is just value. It's what we're all seeking. Peter, you mentioned the lift and shift approach, which we typically do not recommend. There might be some instances where it makes sense. But really figuring out how you get value out of your Cloud environment, what data needs to be moved, how you're going to achieve certain workloads. Thinking through what analytics and insights you can get from a growth perspective, thinking through how you might be able to decommission Legacy applications or perhaps reconsolidate data centers from a cost effectiveness perspective. I think all of those things are driving, I'll say, the why of moving to the Cloud.

And then the what, it's a variety of different factors. Some people are just chasing those whys, some people are just chasing the cost reduction or some of the growth activities, but I'd say it's oftentimes more nuanced than that. And that an organization might be going through an application decommissioning process or they might be looking to implement a Cloud ERP system or Salesforce or ServiceNow where a lot of those new applications or modern applications are Cloud-based. In thinking as you're moving to other Cloud applications anyway, probably makes a lot of sense to move some of your data and other legacy apps out onto the Cloud as well.

You heard me mention consolidating data centers from a cost effectiveness perspective, that's a big driver. Just broader modernization programs. The tech and data components being a really key part of that. We're looking to reduce the platform costs as we talked about inefficiencies and looking for better speed to insights. So, all those things I think are driving the what. But I think the what and the why are obviously very closely connected, so that's my opening thoughts and the what of is driving that move to the Cloud. Yeah, I know our friends at Informatica, that they are also doing this day in and day out as well, so I would love to hear from Jeff and team as well.

Peter Dorrington:

Yeah, thanks very much, Joe. So, Jeff, we've heard about speed, scale, accessibility, and value. And one thing I'm very aware of, as I'm sure everybody on the call is, is that data itself doesn't have a great deal of value. If anything, it comes with risk and cost, but when you put it to work you can actually get it to do something really interesting. But I'd like to ask you, what's driving you think from your perspective this drive into the Cloud?

Jeff Rydz:

Yeah, thanks Joe and Peter. Obviously, want to echo a lot of the things that Joe said. But focusing on some of the technical drivers that we see for organizations that are moving to the Cloud. First and foremost is a desire to outsource a lot of their maintenance and operations through more of a software as a service model. Almost two-thirds of IT budgets were spent on maintenance and operations, leaving a very small one-third leftover for development and new projects to gain market share and implement innovations.

And a lot of organizations are thinking, "I'm an insurance company, I want to sell insurance policies. I'm a healthcare company, I want to take care of patients. I'm a retail company, I'm looking to buy wholesale and sell retail. This is the core business that I'm in." And these organizations don't necessarily want to be in the running a data center business. That's not their core business, so for a lot of the reasons that organizations do a lot of different aspects of outsourcing, so that they can focus on their core business, they're looking to move to the Cloud and adopt these Cloud native technologies for a lot of the same reasons to outsource a lot of that work that just isn't critical or a critical component of what their core business is.

The second reason that we see would be technical scalability and I know we've touched on scale a couple of times. Recently, we saw a change of the monarchy in the United Kingdom. And with that came a lot of interest in the Royal Family. A lot of online sales and interest in memorabilia and artifacts and oftentimes, we don't know what's going to happen in life that's going to drive consumer demand. And businesses want to be flexible and have the ability to respond to changing market conditions and really scale up their enterprise technically to meet a lot of new demand that they might not have seen coming. So, I think that technical scalability that we've already touched on a couple times is certainly not only a key business driver, but a key technical driver as well.

And really, I guess, one of the third things that I see are our modern usage-based pricing for technology. So many businesses are cyclical. If you're in the transportation industry, one of the busiest times of your year is during the summer months as opposed to maybe spring or fall. You're just simply going to be busier then. Utility companies, we have a lot more data in terms of what's happening with their business and usage patterns from customers and things like that in those summer and winter months. With the retail industry here in the US on Black Friday, the day after Thanksgiving is one of the largest shopping days of the year.

And so, a lot of organizations don't want to say, "Well, this is my high watermark for processing all of the data that I'm going to get. This is the price that I want to be paying in my off season as well." And so that move to the Cloud and move to more modern pricing metrics around usage-based pricing and things like that for the software is a key consideration for customers moving to the Cloud as well.

Peter Dorrington:

Yeah, thanks very much, Jeff. That's a brilliant point because if demand were flat, I think that some of the opportunities we get, well, actually, it's not, "Why should I pay for a system that's designed to only meet the short peaks when actually there are lots of periods when I'm perhaps over provisioning."

So, stay there. So, my next question then would be this. I think this sounds like there's obvious big advantages in a shift to the Cloud, but what are some of the key technology related considerations with data migration? We just heard Joe say, "Wouldn't recommend lift and shift." But there are other things we need to think about as well, aren't there?

Jeff Rydz:

Yeah, absolutely. The first one, and I apologize that this on the surface sounds so simplistic, but it's moving the data itself. It's getting it from where the data has lived for decades and decades On-premise to these new Cloud data stores and Cloud technologies that will help us do things with it, whether that's supporting our business operations or analytics, And figuring out how many widgets we sold yesterday versus how many widgets we think we're going to sell tomorrow.

And when we start thinking about that movement of the data, it actually becomes a lot more complex than you would think. You need connectivity to various systems. Some of these systems that have been around for 50 or 60 years. And so, a lot of Informatica's customers at least aren't looking to go out and buy 10 different tools to connect to 10 different systems. They need that wide breadth of connectivity in terms of one technology that can handle connecting to all these disparate data systems that have been collecting the data over the years.

And then as you try and move it from On-premises to the Cloud, throughput becomes an important consideration. We can't have the actual movement of the data taking forever because it might need to be done multiple times as part of an implementation project. So, we need to get appropriate modern types of throughput where we can move those billions and billions of records within a timely fashion.

And then finally, one of the other considerations I see with actually moving the data itself, is how are we going to address standardization if we're going to put in a new application, a Software as a Service application in the Cloud and our prior systems that there were two different systems we used On-premises that are being consolidated into one. And one used a rating system of gold, silver, bronze, and the other used a rating system of 1, 2, 3, well, there needs to be some standardization of the data there.

And I know that's a fairly simplistic example, but it is just one of a number of examples about the types of data quality issues that are going to need to be addressed as this data is moved from where it has lived for so long to these new Cloud technologies that are giving us all the benefits that Joe and I were just talking about. So, the first point I would make I guess is moving the data itself.

The second would be moving ancillary processes. We touched on the need for data integration to move the data, but data integration is an ongoing type of task. We're going to need to consistently integrate with our other systems. Maybe some of them are still On-premises and haven't moved to the Cloud yet. How are we going to extract that value from our data? Are we writing machine learning scripts to use that data, business intelligence technologies to visualize the data, prep technologies to allow our analysts, as Joe was talking about earlier, a little bit more self-service in the Cloud? What are all of these ancillary technologies that use the data On-premises that are now going to need to use that data in the Cloud?

And I think a big consideration, especially for those customers who are putting in new Software as a Service applications are the updating of their business processes. Oftentimes so many companies, if they're going to move one of their older Legacy systems from On-premises to the Cloud, they need to figure out how their business processes are going to change as well. And so, many organizations are looking to automate those business processes.

For example, Informatica had a jewelry store customer that at the onset of the pandemic in March 2020 had to shutter their store. And prior to that time, they really didn't have an online presence. And so, what they were able to do in a short amount of time was create a website where people could go and browse their jewelry and even buy things online. But the key consideration wasn't the creation of the website or piggybacking off template functionality that exists to allow people to shop online, it was how to automate that business process.

So that if I bought a necklace online, well, how is that getting taken from the store that it physically resides at and becoming available to a place where I could do some drive-by pickup? Maybe it needs to be gift wrapped. We need physical security presence, timing needs to be identified, and things like that. Certainly, we see a lot of Informatica customers looking to automate a lot of their business processes as part of their digital transformation.

And I guess finally, the third point that I would make is actually validating the move. Once that data is moved, are we sure that we got it all? Is there a process to keep it up-to-date moving forward? Those ancillary processes that I talked about moving, were they moved appropriately? Are they all working correctly? Are they working in the right order? If something went 1, 2, 3 in the On-premises world, we better sure it's going 1, 2, 3 in the Cloud world and not 2, 1, 4 or something like that. We want to make sure that we have validated our move well.

And unfortunately, for too many customers, this represents one of the biggest parts of the effort about the move to the Cloud, but is oftentimes dramatically underfunded and underestimated. So, I would encourage our audience to keep that validation and testing in mind as they look to move to the Cloud.

Peter Dorrington:

Great. Thank you very much, Jeff. So, you really helped me by enhancing this view about lift and shift. If you're re-platforming, as you say, look at the data you have and how you are going to move it. You're clearly going to need it. You're going to need to look at how you work with that data. And that final point is critical, isn't it? About validation, does it do what you thought it was going to do?

Let me see if I can bring Don in. Don, same question then. What are some of the key technology related considerations with data migration from your point of view or what else might be sitting around that topic?

Don Roberts (KPMG LLP):

Yeah, thanks, Peter. Don Roberts here, again. I would say first and foremost, our upfront technology planning is critical. When we're looking to migrate client data to the Cloud, we're working with our technology partners and consistently looking at each migration opportunity to identify what tech is needed to migrate successfully. What do we need to do to the data along that journey? What client tools are available? What technologies are available to us? What tools can we bring to the table, perhaps compliment the toolbox that's made available to us at the client?

But having the right technology, and I don't say methodology, too, to a large degree, but those two things often lead to a more effective, a more timely migration of data. A migration of data that's more likely to meet transformational deadlines and having that right tech and support from the client is critical to make that happen. I'd say more often than not, we'll see clients attempt to migrate data on their own, leveraging existing technology or under-supported systems.

But I'd encourage folks listening to this to be open-minded about the solution your business needs to make it work and to make it work well. But really challenge yourself to make sure that you're using the right technology and procuring the right technology if need be, even if temporarily in the Cloud. But technology provides a holistic capability, especially in Informatica suite that we like to take to market. The migration is going to allow you to leverage pre-built accelerators that we have in our methodology that we can bring to the table, pre-built validation rules. We can come in and leverage Informatica suite to provide the profiling, conversion, reconciliation capabilities.

All these technology configurations are critical to the quality of your data as you migrate it, such as source system, the source data validations, the data conversion routines, source data validations, reconciliations. And really thinking about technology beyond the ETL engine and migrating from Point A to Point B. What could we do as a professional service firm to provide a more holistic service and visibility to the client data as it's moving real time? And we'll leverage additional technologies to give that visualization and dashboarding to our clients at points of profiling and reconciliation and other important parts along the journey for clients to maintain a degree of comfort and auditability of what's moving.

But more broadly with technology, on the professional services delivery side, it's about how can we drive scale efficiency and speed while also reducing risk of moving the data from Point A to Point B. And then how do we give comfort to our client with visibility and governance. I'd say another thing, often, we'll see clients that they don't have a repeatable or a scalable solution. They may have licenses to certain tools or the right tools, but don't have the internal skillset or the scalability to solve this one-time need or this wave of migrations.

And so, we'll see that a staffing challenge at times. We'll see constraints on client technology stacks. But overall, we just don't typically see the support available to facilitate the migration that's needed. And we've done a good job at KPMG of really marrying Informatica's technology and their ecosystem, which is very important with our methodology and experience of moving data to the Cloud. And we've created a service offering around called power data, migration, and there's some really key considerations of making that journey successful for clients, and we'll get into a little bit more of that.

But really with the important technology variables, again, like I said, it's what can we leverage within Informatica is often our go-to ETL engine and what else do we need to bring to the table? And teaming with Informatica has been a very successful journey with us. Their migration engine has been very powerful for us to not only get data from Point A to Point B, but get that data there in the condition we need it. And it's been very easy for us to configure and scale and those are all benefits to our clients.

And really, I guess, I wrap up here and building on what Jeff shared about really moving data itself and validating the move. And those are critical steps because they're critical to ensure that we're providing a complete service to our clients and that validation and Informatica suite tools, like I said, let's us do that effectively and bolting on additional technologies to give clients comfort is very important.

But more important than not, the complex large scale data immigrations that take place out there and that's usually what they are. They're never usually one source to one target, but leveraging Informatica's intelligent Cloud services or intelligent data management Cloud, current rebrand. But those have been very powerful tools. And in summary, I think there's a variety of technology and configuration configurations that have to take place to support a data migration. And I know while this question was a little bit more tailored to technology, there's also a lot of business perspective that has to be put into this. And like I said, the methodology we take and marry that with the technology I think leads to a very successful journey.

Peter Dorrington:

Great. Thanks very much, Don. So, it sounds to me like for many organizations this is not something they're doing every week. It sounds like there are opportunities for it to go well and for it perhaps not to go quite so well. So, Don, let me come back to you then and say, "Well, how do organizations accomplish this successfully? What's the recipe for success here?"

Don Roberts (KPMG LLP):

Yeah, I'd say success is often elusive, I think, when clients are trying to tackle this in-house, but some are doing it more successfully than others. And I just touched on, from the technology aspect, that's a big consideration with any data migration journey. But you can't treat that journey just as a technology play and I touched on methodology and that being critical. But often, we're seeing organizations end up with difficulty in delayed initiatives, delayed transformation, dirty data.

The technology's critical, but that's why we're in the market using what we feel are industry leading tools such as Informatica's platform, but really to increase the probability of success and getting your data into its destination in the cleanest possible way. We're often seeking to figure out, how do we complement a selected technology with really a methodology that's business-led and how do we leverage that to get data to its destination?

I briefly touched on power data migration. KPMG service offering. And that's really focused on client data migration to the Cloud. And while we're in the market supporting those migrations of all sizes and complexity, we're also providing AI and analytic support while we migrate that data. That helps bring in a lot of insight and support of decision making and that helps get a successful migration in addition just to getting to the destination.

But we're migrating data faster and we're doing it smarter and we're doing it with less risk with the automated solution. That's less risk to the clients and our firm and to Informatica, but most importantly, the client is looking at that transformation timeline. And anything we can do to get data to its destination timely and successfully, we have to rely on these tools and configurations and accelerators that we've developed.

And we really guide that effort in a multi-phase approach, but quickly, we have a methodology. It covers five phases. In that vision phase, we're really focused on identifying what are those source systems, how do we prioritize them. How do we leverage our inventory of pre-built data extraction scripts, source specific? And we'll work to understand the client specific environment, what type of data and how much are we dealing with. We'll also complete a review of the quality of the Legacy data and create a strategy and a plan to make this work and start prototyping.

And then we'll move into a validate phase where we'll start planning for the build and the remaining customizations is needed. And we get into that build and construct phase, so that's where we're building out the acquisition transformation, the mapping. And really helps us guide us in implementing the solution and then getting the data quality we need and the monitoring in place. And really moving into the deployment where we're turning the factory lights on, the conveyor belt per se, to move that data. 

And we're performing GoLive cutovers and executing data immigration-run books. And we got our GoLive reconciliations and controls operating and really helping support that data conversion.

And we'll wrap that up with UAT and then we'll move into an evolved phase, post GoLive and post migration. That will help us learning lessons, conducting GoLive support as needed and transition any ongoing support back to the client. And like I mentioned, really providing insight to the client as we're doing this with real-time visualization. And that gives the client a lot of comfort, especially as it pertains to their audit and controls requirements. And some governance is that wrapper that really helps. I think that could have put a bow on the service and comfort around what we're doing because the data is critical.

And I would say really to make all this support happen, what's been critical is the delivery, the methodology, globally. So, our global delivery capability ensures us to overcome any challenges with geographic time zones. We're following the sun as that work effort and across different countries and regions to make sure that we get the quality and the speed we need to our clients.

And I touched on governance, but lastly I'd like to emphasize really de-risking the process. And how our visualizations and dashboards can provide that compliance, which is such a critical part of data migration and successfully getting your data and accurately getting your data and completely getting your data. And we do, we've got a long way to build those dashboards and insights that can drill down to the data bit to see what exactly is going on or what may need attention. And really, overall, organizational success with data migration is a big mix of selecting and exploiting the right technologies and having those right accelerators and that proven delivery methodology to help make it work.

Peter Dorrington:

Great. Thanks very much, Don. Let me see if I can bring Josh in here. So, Josh, I'm going to ask you from an Informatica's point of view, the same question. What's the recipe for success here? How do we ensure a successful data migration?

Josh Alpern:

Yeah. It's a very, very interesting topic and suffice it to say that I think that the four of us here could probably spend days, if not weeks, just talking about this and all of the lessons learned that we've seen across customers in the years of doing these types of projects. So, I'll keep it relatively brief and just focus on a couple of things that I think are especially important.

But before I dive into that, I want to just refer back to a comment that Jeff made earlier about the starting point for these projects are environments that have been around for years, if not decades. And whether or not we're talking about an operational environment or an analytics environment or both, the sprawl and the scope of data that populates these environments is truly just absolutely massive. And so, that's the picture that I generally want to paint as I talk about a couple of the best practices that we see our customers using to ensure that these projects are successful.

Number one, first and foremost is a very detailed and comprehensive analysis, at least to the greatest extent possible of that environment to ensure that you have a very, very complete and accurate picture about all of the data and all of the data infrastructure that you have. That you're at least contemplating migrating to the Cloud, because without that complete picture, there are going to be a lot of different pitfalls that you're potentially going to encounter. And I'm going to come back to that in just a moment.

So, having as complete and accurate a picture as a starting point is really, really critical. And then secondly, you have to leverage automation to the greatest extent possible when you're doing these migrations. Given the size and the scope and the sprawl that I was just talking about, we're talking about potentially not millions, not even billions, maybe even trillions of records in some of these environments across many different data stores, applications, databases, flat files, mainframe, relational. You name it, any flavor, you have to take it all into account, which is not to say, by the way, that you need to migrate it all.

This brings me to my first really key point and that is when we talk about the analysis and having that comprehensive picture of your environment, one of the reasons you want that picture is to ensure not just that you bring all of the data into the Cloud that your organization needs, so that on the one hand you're not leaving anything behind that might be critical to your success. But also, so that you're not bringing data that you don't need.

One of the things that comes along with these types of environments that have been in place and developing over years and years and years is that there's a lot of what we call an Informatica deadwood built up over time. There's a lot of stuff that's out there that while it's sitting there in the On-premise environment might not be doing anybody any harm. Yeah, there might be some additional storage costs or storage infrastructure that's necessary. But, generally speaking, it's just sitting there and not getting in the way.

But as soon as you talk about re-platforming or moving all of that data to the Cloud, well then, it becomes a totally different story because you could end up wasting a huge amount of time and effort, moving a lot of data and analyzing a lot of data. And to Don's point and Joe's points earlier about doing all of the validation and checking and data quality controls, you don't want to be doing that on data that you don't need going forward. So, that accurate picture works in two ways, ensuring that you're capturing everything you need, but also, that you're not taking along anything that you don't need.

And then, when it comes to automation, again, given the size and scope and complexity of these data sets and data infrastructure that we're talking about, frankly, if you're not leveraging automation to do this, you're starting behind the eight ball. You're starting with a huge, huge disadvantage right out of the gate. And generally speaking, just to wrap up and tie these two things together, we see our customers evaluating these migrations to the Cloud across the three dimensions of time, cost, and risk.

And when you look at the two things that I just talked about, having a comprehensive analysis, number one, it helps you reduce the amount of time of your modernization because you're only focusing on the data and infrastructure that you need. You're reducing the risk of either leaving something behind or taking something that you don't need. And of course, you're also having a positive impact on the cost because you're only focusing on exactly what it is that you need.

Likewise for automation. Automation, of course, has a huge impact on the amount of time, overall, that it's going to take to complete a modernization. Suffice it to say that without a high degree of automation, it's going to be a lot of manual effort. And guess what? That's going to take a lot longer to accomplish. Without the automation, your risk level is going to be way higher because of all of that manual effort. There's a lot more. There's a much greater chance that people are going to make mistakes when they're hand coding or recoding things manually.

And then, in terms of cost, of course, if you're reducing the overall amount of time, the elapsed time on the project, if, as Don mentioned earlier, you're leveraging automation to do all of the validation of the data as opposed to just doing manual checks or spot checks or something like that, you're going to have a very, very positive impact on the cost as well. So, I would strongly encourage everybody to focus on those two things. That upfront analysis, which is so critical to start off on the right foot and then, leveraging automation to the greatest extent possible all the way through the process.

Joe Updegrove (KPMG LLP):

Hey, Josh, those were great points. And Peter, I know we got to keep moving, but just two things I just wanted to add to that. One, where you talked about the deadwood, so that data that's not used. We're all data folks here, so I'm not going to throw out percentages that I don't know exactly off the top of my head. But when there's been multiple studies done of people looking at the data that is used within the data warehouses and data lakes that have been built previously, it's a very low percentage of the data. It's less than 10%. I forget the exact number, but the usage of that data is very low, so that point around the deadwood, 100% agree with you.

On the flip side of that, to avoid some of that deadwood, and I thought you articulated it very well, oftentimes, the way that we'll work with that with organization is by starting with, what are those business use cases? What are the required archetypes that are needed in order to support the business? Working backwards from or the right side of the architecture, from what's the real business value. Whether it's analytics, reporting, making the data accessible via data exchange.

And then we're working backwards to identify your data needs and requirements that way, but I think it's all tied together. But also just emphasizes, Don I think the point you brought up earlier, reemphasizes that point around business and data teams, all needing to work very closely together.

Peter Dorrington:

Great points, Joe. Josh, I'll just come back to you because I think I've heard consistently through all of the discussions so far, the importance of the way that you manage data. I'd like to get your views then on why is it so important that the broader data management capabilities are considered as part of a migration to the Cloud? I would have thought that that was probably in the highlight of everybody's mind, but why is it so important from your point of view?

Josh Alpern:

Yeah, it's a really interesting question because I would frame it this way. For the people that are attending today, you're all either in the middle of implementing a Cloud strategy or you're trying to figure out what your organization's Cloud strategy is. And it's one thing to migrate what you're currently doing in your On-premise environment to the Cloud. But I'm going to tell you that when you're talking to the leadership in your organization, you're talking to the boards of directors in your organizations, if you were to say to them, "Hey, I have great news. We're going to be able to do tomorrow, everything that we're doing today On-premise, but guess what? We're going to do it in the Cloud."

You would probably get a lot of not very excited reactions, a lot of shrugs of the shoulder. The question that everybody is going to ask is, "So what?" And even if you took it a step further and you said, 'Yeah, but we're going to be able to do it at a lower cost than we're doing today," okay, that might get some more attention or a little bit more excitement. But I'll refer to the old adage that you sometimes hear in C-Suites and boardrooms around the world, which is you can't cut your way to growth. You can't cut expenses and have the net result be that your organization is growing.

And guess what? Modernizing to the Cloud is all about growth. It's all about being to attack new business opportunities, to execute faster, to increase time to value when your company or your organization does see a new opportunity out there. So, it's very, very much a growth mindset. Now, I will say if you think back a few years when the Cloud computing first became very buzzword-y, there was a lot of focus on cost reduction.

That people looked at Cloud computing as a way to get rid of all of the On-premise infrastructure that they were using and depending on and spending money on year after year. And that does remain part of the equation certainly, but a few years ago, if not even more than that now, the focus really changed when we talk about the Cloud from cost cutting to growth and increasing the amount of business overall. And that's really where we're at today and that's what the focus on modernizing to the Cloud is all about.

And so when you talk about taking all of the operational or analytical capabilities that you have today and moving it to the Cloud, yes, that's great. But the real prize is all of the amazing things that you're going to be able to do in the Cloud going forward, into the future that you're not able to do today, and how fast you're going to be able to do those things. In that sense, the Cloud represents a dramatically new approach compared to what people have done traditionally.

And so, all of those additional data management capabilities that are available in the Cloud, whether you're talking about serverless computing, elastic computing, all of the ways to get data into Cloud-based data stores very, very quickly and easily without a lot of friction. All of those things are the things that are going to be exciting to the decision makers and stakeholders and leaders of organizations that are contemplating modernizing to the Cloud.

Peter Dorrington:

Brilliant. Thank you very much, Josh. Let me come back to Joe again. So, Joe, same question to you. Why is it so important that the broader data management capabilities are considered as part of this migration to the Cloud? It must be very high on your agenda, in that of the KPMG methodologies.

Joe Updegrove (KPMG LLP):

Yes, very high on the agenda, Peter. And also, hard to add much after what Josh just said because I thought he did a terrific job on articulating that. But maybe one way I can try to do that is just via a recent anecdote or story of working with a client. We're actually working with CTO for large asset manager. And one of the questions that was asked to this exec was, "Well, in your Cloud migration," for which they've had a very successful Cloud migration, "what were some of the key components or what was the one critical component that led to the success of your Cloud data migration?" And what he said, I think it shocked me and I think it shocked everybody in the room, but he said, "Data governance."

And typically, data governance has been thought of as this, "Something I have to do." It's more compliance related and it's more policies and standards. And when we dove into it a little bit further, and by the way, I think data governance is way more than that and it's something I'm pretty passionate about and probably get more excited about data governance than most. But when we actually dove into that a little bit and he started describing what he meant by that, it was, okay really going through a very in depth and thorough data cataloging process, so broader metadata management. Understanding, Josh, that point you were making before, profiling the data, understanding what data elements were out there, starting to link that back to a logical business glossary, so the terms and definitions of that data.

So now, they understand the data. They understand where the data sits. And now, as they start onboarding that data into the Cloud, they know exactly what data is being on-boarded, they know the data quality associated with that data as they're onboarding it. They have a sense as to the lineage associated with that data. Where it's coming from, from a source perspective, what the target is, and then how they're going to make use of that data.

It was really cool to hear that, I thought, and it's also representing a shift. Josh, you talked about the shift from a focus on cost to a focus on growth. I think we're also seeing a shift as part of many of these Cloud migrations. It's like the underlying foundational components across data management, data governance. Some of those things I just mentioned. Metadata management, data cataloging, data quality, data lineage. If you've got a mature enough model setup moving towards master data management. Some of those more complex technical capabilities.

As organizations are making these Cloud migrations, those data management and governance capabilities, I think a lot of organizations are now recognizing the importance of those as well. You don't have to do it all at first, and you can do it on a domain specific basis or data source specific basis. You don't have to boil the ocean. But I think including some of those concepts and as you're going along on the Cloud journey, become really critical into the future success of the program.

And that's why I think this great relationship we have amongst KPMG and Informatica, because we at KPMG can offer a lot of you around the strategy, the design, the implementation side. Informatica has the product suite across all those areas. Whether it's governance, whether it's data cataloging, whether it's the movement of that data, they've got the suite of platforms and products to help enable that. So, really, really, it's an exciting topic I think for all of us because Cloud data migration doesn't just mean moving data from here to here, it's the whole suite of capabilities that exist within the broader data space.

Peter Dorrington:

Great. Thanks very much, Joe. And I guess a little strange to say perhaps, but yeah, I'm excited by that as well. After a week, it's linked to the standardization and the metadata and accessibility. If we can all agree what a customer is, then we can make that data available to everybody else.

Now then, I'm going to make the assumption that data migration program has been a success. So I'm going to go, Don, to you. I mean, how do you measure success, both in the short and the long term? So, what should be our criteria for we know that we've completed at least this part of the journey?

Don Roberts (KPMG LLP):

Yeah, I'd say the first way of measuring success really is to reconcile the outcomes with the upfront objectives. But when I think about success in the long term of our clients and the result, I think about the results that we would be able to provide and the experience that they had. I'm thinking about how smoothly was the migration, how smoothly did it occur, how timely? Was there any risk exposure? I'm thinking long-term successes.

What else did I get out of the effort? A roadmap for further maturity of data that Joe just mentioned. Did I just get my data moved from Point A to Point B? Did I get any insights out of this? Any deadwood identified? I'm thinking, did I have accessibility to my data when I needed it? Was it usable data in the target system? That's always a good measure. Were you able to focus on the data quality issues, like I mentioned upfront? What did profiling and data quality get for me?

I'm thinking about how comprehensive were the controls? How comfortable do I feel with accuracy and completeness? There's a variety of ways to measure success in the long run. And on the flip side of that coin, in the short-term, I'd be thinking about how do I measure success while I'm in the thick of it? I think a lot about having a leading data technology platform to manage that data. That's always critical. But I'm thinking about how fast is the migration occurring? What percentages of data are falling into certain buckets? There's a variety of factors, common factors that affect speed. But I'm also thinking about efficiency, job uptime.

Thinking about how low were our failure rates, how valuable were the identifications, certain business rules and results. Thinking about leveraging visualization, reusable aspects of visualization along the process. Was I able to configure that visualization for insight that was in-depth enough to provide value to the client. I want to elaborate a little bit more on visualization, but that real-time, short-time, short- term success in the profiling dashboards, I'm thinking about whether or not, I can see real-time that data quality results as a result of the conveyor belt running and the factory lights being turned on and the data moving.

I'm thinking about how many domain modules and entities and I'm thinking about that data level summary. And how far down can I drill down into that data to resolve issues if necessary. And then also, reconciliation, I'm thinking about how quick and easy can I perform health checks. And how quick and easy can I validate the data that's being moved from an accuracy and completeness perspective. And like I said, similarly drill down into the match and fallout data and in the reconciliation process. I think there's a variety of ways you can look at success both in the short and long term.

Peter Dorrington:

Great. Thank you very much, Don. And I'm going to give the final word on this with the same question to Jeff. So, Jeff, how do we measure migration success both in the short and the long term?

Jeff Rydz:

Well, in the interest of time here, Peter, I'm going to have a very succinct answer here. I've got three points that I would like to make. First and foremost is probably the most simplistic and a little bit of a glib answer, I apologize for that. But was it delivered on time and was it delivered on budget? Because those are very common ways that we go about measuring any type of project, including our migrations.

The second is really related to a migration to the Cloud. And it's like this, "Did you shut down your Legacy estate? Are you confident enough in your move to the Cloud that you are no longer using your Legacy estate that has been thrown away and retired?" Because that's really an indication that yes, your migration to the Cloud was successful. I know a lot of organizations like to run in parallel for some time, which is great and oftentimes encouraged. But ultimately at the end of the day, you know have a successful migration when you've shut down that Legacy estate.

And then really, your third consideration is the tools that you leverage in order to make that migration successful, what are you doing with them now? Are you able to leverage that platform? Are you able to leverage those technologies to continue helping to automate and move your business forward? Or did you invest and use tools just for your migration? So, really the ability to use that platform for more than just the migration is probably the third consideration that I would think of.

Peter Dorrington:

Thank you very much, Jeff. That was so good. I've actually made a note of that. I'm going to come back to it. So then, well, in the interest of time, as you say, let me do the briefest of summaries, tell you what's going to happen next and then we'll be moving on.

Firstly, I really like this point about the need and I like the four factors that came out of there, the need for speed, for scale, accessibility and for value. And it's not just the data. We need to think about how we're going to integrate it with our processes. So, we need to be able to iterate, not just the data, but the way that we use it. And success, reductions in time, cost, and risk. We'll come back to success criteria in a moment because I think Jeff just landed on some great ones.

Now, one thing I did also hear was the importance of automation in the migration process. Use tools that can help take on some of the heavy lifting, so that we don't have to constantly be putting in a lot of effort to keep doing the same thing over and over again. So, automation, and I think one of the things I heard as well that I liked was you can't cut your weight to growth. In other words, when we get this right, it's not just about cutting costs, which is important, but it provides us a platform for growth because it gives us that agility, resilience, that ability to do new things and do it at scale quickly.

Final couple of thoughts, master data management, managing the data. I know lots of financial institutions talk about assets under management. I like thinking about data under management and data governance. Particularly, when we're trying to simplify and standardize our data, so that everybody understands what it is and what it does and where they can get it.

And finally, to quote the bit that I've just captured from Jeff, success criteria, on time, on budget, absolutely. Did you shut down your Legacy estate? Have you got to the point where you feel safe that you can do that? That you can actually say, "We have finished this stage of our transformation. We no longer need all of that Legacy." And finally, and I don't think it's just the tools, it's the tools and the learning that you use during your transformation. What are you doing with them now? Do they continue to add value as your organization moves forward into the future?

So, what we're going to do next? Firstly, thank you for your attention. What we're going to do is give you an opportunity. If you would like to continue this conversation, one-to-one or ask questions, then please do so, so look out for that.

But with that, all that really remains for me to do is firstly to thank our four expert panelists today, so Josh, Jeff, Don, and Joe, thank you all so much. And to KPMG and Informatica without whose support, this would not happen. To the Executive Leaders Network for doing all of the logistics and all of the behind-the-scenes magic. But most of all, thank you for your attention. And until the next time, thank you and goodbye. Take care now. Cheerio.

Josh Alpern:

Thanks, everyone.