Rick Hernandez of EY talks about how a shared vision—from the C-suite to engineering can drive success in data streaming initiatives. When everyone aligns on the “why” and the “how,” digital transformation isn’t just possible, it’s powerful.
Whether you're an executive setting the strategy or an architect building the backbone, alignment is the key to turning transformation from a buzzword into results. Rick Hernandez, principal technical architect at EY, shares how to unlock shared vision and turn it into enterprise-wide data streaming success.
In this episode, Rick joins Joseph to explore how organizations can connect leadership ambition with technical execution. Exploring how top-down buy-in, clearly defined objectives, and strategic alignment with the right technology can give your organization “wings to a tiger.”
You’ll learn:
If you're leading modernization efforts or championing real-time data, this episode is your guide for building momentum across teams.
About the Guest:
Rick Hernandez is a Principal Technical Architect at EY, specializing in enterprise architecture and digital transformation. He helps organizations implement innovative solutions and optimize IT strategies. Rick’s expertise includes architecture and development in middleware technologies, cloud integration work, business process management, enterprise application integration, and more.
Guest Highlight:
“Technology transformation is not for one area of a company. The overall company needs to actually change to do something better, differently, and more efficiently.”
Episode Timestamps:
*(01:00) - EY’s Data Streaming Strategy
*(05:30) - Data Streaming Goodness: Having a Shared Vision
*(22:00) - The Runbook: Tools & Tactics
*(26:45) - Data Streaming Street Cred: Improve Data Streaming Adoption
*(31:10) - Quick Bytes
*(33:45) - Joseph’s Top 3 Takeaways
Dive Deeper into Data Streaming:
Links & Resources:
Our Sponsor:
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0:00:00.2 Joseph Morais: Welcome to Life is but a stream, the web show for tech leaders who need real time insights. I'm Joseph Morais, technical champion and data streaming evangelist here at Confluent. My goal, helping leaders like you harness data streaming to drive instant analytics, enhance customer experiences and lead innovation. Today I'm talking to Rick Hernandez, principal architect at Ernst and Young. In this episode, we'll talk about how data streaming at EY is making things better, faster and cheaper for the customers. We'll break down the strategy tools and the adoption of streaming for EY and their customers. But first, a quick word from our sponsor.
0:00:39.0 Announcer: Your data shouldn't be a problem to manage. It should be your superpower. The Confluent data streaming platform transforms organizations with trustworthy real time data that seamlessly spans your entire environment and powers innovation across all every use case. Create smarter, deploy faster and maximize efficiency with the true data streaming platform from the pioneers in data streaming.
0:01:07.7 Joseph Morais: Joining me now is Rick Hernandez, principal architect in Ernst and Young. How are you today, Rick?
0:01:12.4 Rick Hernandez: I'm doing really well. How are you doing?
0:01:14.6 Joseph Morais: I'm doing fantastic. You know, not everyone asks me that, so I appreciate it.
0:01:19.0 Rick Hernandez: I am glad. I am glad.
0:01:20.6 Joseph Morais: I really appreciate that. So let's jump right into it. What do you and your team do at EY?
0:01:25.3 Rick Hernandez: I am part of the digital integration practice for EY North America. Well, we're a global practice that does integration services and messaging, data streaming now, amongst other things, I help by being the technical lead for this global practice. And I report to a group of really talented and wonderful individuals that actually handle Fortune 1000 accounts in the United States and Italy.
0:01:51.4 Joseph Morais: Focusing on integration just for a moment, that means you're working with all types of systems, probably every flavor of database, every flavor of data store and destination. So it's fair to say that you really understand the technology stack of customers probably pretty broadly.
0:02:07.8 Rick Hernandez: Oh, absolutely. I've been working in this for about 30 years, actually give you some kind of reference. So we're talking a lot of legacy. When we talk integration, we talk about many things, but there's a lot of emphasis on legacy, S400s and so forth that a lot of our clients have been for a long time they trust these systems, but there are newer technologies that they're trying to actually put together as part of the technology transformation packages that EY work on. So we had to come in and say, okay, well how do we actually make do a merge of all these old technologies and the EY technology in a way that makes sense and where can we replace the old with the new where it makes sense in order to actually help them achieve their goals.
0:02:48.6 Joseph Morais: Well, this fits perfectly into my next question. So who are your customers and who aren't your customers?
0:02:54.0 Rick Hernandez: EY is a global brand. We basically have clients all over the world. We're 100 different countries, and in all sorts of different verticals as well. So anybody can be a customer as long as they have an interest on technology transformation actually becoming a new, more efficient company in this day and age. My area in particular with talking messaging and the infrastructure or the messaging systems and data integration and data streaming, then that is a subset of those clients. But there are clients usually that are trying to actually change with the times to be able to take care of the clients faster, cheaper and more efficiently than ever before.
0:03:39.0 Joseph Morais: That makes sense. Everybody wants cheaper, faster, better. Since you work with so many impressive organizations that have implemented data streaming, what is your go to data streaming strategy in a minute or less?
0:03:50.4 Rick Hernandez: Oh, wow. There's no such a thing. First of all, usually when a client comes to us, they want to do this technology transformation, they have a pretty good idea in mind of what is it that they want to do. And that usually comes along the lines of, we are a world class organization on something, like our data is fantastic and we do great things. However, we cannot do things fast enough. So they are looking at trying to do something that actually came to be in the batch world, and trying to actually bring it into the real time world, into doing things right away. We have access to the data, let's use that to actually make decisioning and actually serve our customers better. So that's usually the strategy. They actually tell it to us, this is what we want to accomplish and we actually have experience doing that. Ergo, the reason why we do what we do.
0:04:37.8 Joseph Morais: Got you. So they really, they come to you with a certain set of here are our aspirations, here is our pain. And then you kind of figure out how the roadmap to fixing all of that for them.
0:04:47.9 Rick Hernandez: Yeah, when they come to us, it's because like, we're here, we're stuck, we want to keep...
0:04:51.7 Joseph Morais: We tried.
0:04:54.4 Rick Hernandez: So how can we do that? So I'm not going to say that 100% of the time it's usually they tell us what they want to do. There are some times that we say, okay, well, you're here today, you could do better. And the reason why you could do better is because X, Y or Z, because you have your data, you have your insights, but what could you do with that if instead of having to wait a day, you actually have to wait microseconds, will you be able to actually do this better, faster, cheaper for your client? And will you actually be able to enable a better end user experience by doing that?
0:05:37.1 Joseph Morais: All right, excellent. So we set the stage. So let's dive deeper and dig deeper into the heart of your data streaming journey. In our first segment, we call it data streaming goodness. Before implementing data streaming, typically, what are EY customers biggest challenges?
0:05:51.6 Rick Hernandez: I think the main challenge in pretty much every client is to actually have a consistent view from the top to the bottom. Like, oh, what do we want to do? We need the executives to actually be, own that vision. And we need the technical managers to own that vision and we need their technical resources to own that vision and be able to implement the vision. We need to have that coherence of everybody actually growing in the same direction. Technology transformation is not for one area of a company. The overall company needs to actually change to do something better, differently, more efficiently. And so we actually try to approach it that way. We actually get the executives, we get the managers, and then we actually get the technical people and we help them along the way by providing assistance on all these different areas.
0:06:40.1 Joseph Morais: Yeah, it makes sense. I mean, establishing a shared vision is really kind of the catalyst to doing anything large. It doesn't necessarily have to just be data streaming, but trying to get that buy in top down, down to say we're going to change to something, we're going to do something new. We know how hard that is, but we all understand the value in what this pain, going through this pain is going to provide us at the end. So I think that again that, another great segue into the next question. How did these challenges with these customers impact your customers or their downstream customers experience or day to day operations?
0:07:14.5 Rick Hernandez: So it depends upon who the end client is. But the approach is always the same. How can we make this better? How can we fix the process? And can we do that with technology? Yes, a part of that is with technology, but I think more than that is in the aligning on what we're trying to do together as one technology transformation project.
0:07:35.9 Joseph Morais: Interesting. So I love that. So, regardless of the vertical, regardless of the size of the company, you find that, the challenges change, of course, but the approach is always the same. That's really good. I know you already talked about this, but I really want to double click into it. So your customers to turn to data streaming? I know they want to do things better, faster, cheaper, but is there a specific tipping point, Rick, where someone says, you know what? We've been doing batch forever, but this new thing came up and we absolutely cannot do this. How do we move forward? Have you experienced scenarios like that with your customers?
0:08:06.6 Rick Hernandez: Yeah, we have. Yes, we have. And I actually find funny that there are like different stages for clients. They're clients that basically they're coming from the messaging world, and what they want is just a better mousetrap. They just want a better way of doing messaging and you know, basically say, yeah, we can do that. We can actually help you replace your old messaging system with something that is more modern, like a Kafka, like a Pulsar, something else. You know what? Yes, that will help you. But by using these newer technologies, you open the door to a better way of processing your data. Not just point to point getting from A to B to do something, but now basically having a central repository for your data where you can actually do operations upon that data in near real time. So what could you do if you actually have all of your data at your fingertips at every point in time, what could you do better for your customers? And what could you do better for yourself? A big word right now that everybody's utilizing is like anti fraud.
0:09:08.4 Joseph Morais: Yes.
0:09:09.3 Rick Hernandez: We want to actually go and say like, no fraud in our operations. So you are talking about a bank that's actually doing billions of transactions for credit cards or payments and so forth every day. They used to be able to say, okay, well this doesn't fit with that. And next day, the next day they will actually catch that there was something that was going on after somebody already went to like Home Depot and bought something, or went to like a store and bought $1,000 worth of computers, right? But now we go to the bank and say, we can do this better, we can do this faster, we can do it cheaper. We can actually enable your program to be real time. And we have a financial institution that we work on that we were able to do just that. And now they're able to actually say, okay, well right now, within microseconds, we know that this doesn't look right and they can actually take action upon that. That's just one example. But there's many, many, many other ones. So like, again, example I had with manufacturing companies, it used to be that you were actually doing UTT operations.
0:10:15.6 Rick Hernandez: And over time you would basically say, okay, well let's look at how we did. And then you can actually enact change based upon the analytics that you did with that. Now you have the ability to take that information, put it in the stream and perform analytics on it as it goes. So you are able to actually make changes. So those are the kind of things that we can enable with data streaming. So when we're talking to somebody and they were like, no, no. Do we just want to change our messages? Like, yeah, the world is at your hands right now if you just take five minutes to think about it. Just let me explain how this works. And if they're receptive, it's actually a very, very good strategy because allows them not only to fix the problem now, but also allows them to be okay, even five years on the road, because this is future proofing what they're doing.
0:11:04.2 Joseph Morais: Yeah, I really like that first example. Because how powerful is it, to find out a minute later that someone bought three generators out of state with your credit card versus the next day? The amount of remediation of how quickly you can stop that, how quickly you can, get back your credit or maybe avoid your car being locked out and not being able to use it the next day you go on vacation. But the difference between a day and a few minutes is just huge when you think about that particular use case, and that use case with manufacturing, finding out I have a problem now, not half a decade down the line, sounds very important. So again, like you're a master of dovetailing into my questions. So I was going to ask you, since you have such great experience across so many customers, across so many verticals, can you list maybe two or three of your favorite data streaming use cases that your customers have either already built or maybe you're currently building.
0:12:01.5 Rick Hernandez: I can tell you what EY is doing today. We as a firm are completely bought into the paradigm of better, faster and cheaper like right now. We have a financial package that we use. They are selling them to some of the financial institutions where we actually help them to very rapidly come up with POCs and solutioning, kind of like helping them model what is it that they want to do. And we had this platform where you can actually do that, do that very quickly and we can inhale almost like data very, very quickly and then use that to create models and to expand models and so forth. EY has actually bought so bigly into AI. If there's something that we know and we have learned about AI is that it needs to be fed huge amounts of data very quickly. So we are doing that because we're creating our own LLMs and we're creating our own AI solutions. And in order to enable that, then we need to have data streaming and along other technologies as well so that we can actually feed that data into the systems, and that enables our solutions to become very good, very fast.
0:13:17.6 Rick Hernandez: Because as we all know, LLM starts not being great. So that's what we're doing all the time. So that suggests EY, but some of our clients are doing the messaging replacement, some of them actually go to the next level where they are actually doing data streaming and solutioning on top of that. And then some of them are the level three that are whole nerve system of, everything that they do gyrates around data streaming. We basically encompass everything from the small steps all the way to the overall overall solutions.
0:13:52.3 Joseph Morais: No, it's very exciting to hear that, a systems integrator the size of EY is not only helping your customers adopt data streaming and stream processing and all those fun things, but you're also leveraging yourself to build value added systems for your customers as well. It sounds like the world is really thirsty for data streaming.
0:14:09.2 Rick Hernandez: There's many companies out there actually trying to get in on this specifically because of that, because they actually see the power of being able to actually fed huge amounts of data very quickly into systems that in a real time basis can operate and make decisions, smart decisions based upon that data. Data is king and data is queen. It's like the whole executive branch over there is about data. But then there's the other part of that which is anybody can actually create something that will do streaming. Anybody can create something that can actually move data around. But how do you put your arms around it in a way that you can make it secure, observable, that you can monitor and that you can basically enact a set of rules around this so that not only are you doing what you thought it was super cool, but also doing it correctly, legally, within all the constraints of your particular industry. So we are going beyond to actually move more into the governance and the monitor and the observability space as well.
0:15:08.7 Joseph Morais: Yeah. You hit on something that's very important and that it's easy to do data streaming. I guess really any nascent technology, distributed technology, when you're POCing, you're doing it small. Because you're like, let's just prove it works. But like you said there's no observability. We're not using any authentication. We're not doing this at production scale. And it's all those pieces that I find, at least in my experience, where people shoot themselves in the foot, they're like, yes, we know this technology is going to work for us, but we don't know how to do this at production scale. And that's where it gets really tough.
0:15:39.5 Rick Hernandez: And their tools, right? That's correct. There are some tools that are very great at doing something very quickly, but maybe they are not full feature enough that can actually give you all the components that I'm talking about. When I actually talk about, like any of these tools, EY is big on whatever is best of breed, whatever is best in the market. Also, we also have relationships with lots of different companies. So you can actually use the correct tool for the correct job. But I always say that you want a tool that will not only just do what you want to do, but actually do it correctly. Not only would allow you to connect A to B, but it allows you to visualize A to B, to monitor A to B, to see what's happening back and forth, to be able to recover something that actually went from A to B but didn't quite make it. How do we actually take all these processes and make them together in a way that makes sense? That is a solution. The other thing was a tool. This is a solution.
0:16:36.3 Joseph Morais: Tool versus solutions. I like that a lot.
0:16:39.3 Rick Hernandez: But there are tools that actually have most of these things built in, and those are obviously the preference. However, we always go with whatever, I mean, a combination of what is the best that we can do and whatever the client actually allows us to work with.
0:16:53.0 Speaker 2: Oh, and by the way, since you're a partner of ours and you mentioned that best in breed technology, I'm totally taking that as a compliment.
0:17:01.5 Rick Hernandez: You may, you may take it as a conclusion. It's great to actually have products that are full fledged, that have everything that is required and needed. And I look forward to the day when even more of those things that are needed are incorporated. And so I see that you all working very hard in trying to get some of those new products and new features fully fledged.
0:17:22.9 Joseph Morais: Yeah. Like table flow.
0:17:24.4 Rick Hernandez: Like table flow, right. Yeah. So we were talking about table flow not that long ago and I love the idea that we started with iceberg, but I also like so much that you, I'd say, like, well, you know what Iceberg is not the only one in the market. Let's go with the data lake as well. And I'm assuming that you're probably thinking who did down the road? I don't know that for a fact, but maybe you are. Which basically is allowing us to say to our clients, wow, you know what? You go and you take whatever you think actually fits best in your environment because the tool will support you.
0:17:54.5 Joseph Morais: Yeah. Ultimately we want to make it so that you can take your streaming data and bridge to analytical estate that you want. We know that we're focusing very heavily on Iceberg now and also delta tables, but who knows what the future is? We want to just ensure that wherever you want those streams to go, that whichever analytic engine you want, it's available for you. So I don't think that's out of the question. Let's shift to talk about governance a little bit. How important is tracking and enforcing the flow of quality data as it enters a data streaming system?
0:18:28.0 Rick Hernandez: It's so important. Like I mentioned before, anybody can connect something from A to B. That's not just what we want. What we want is to ensure that what's going from A to B is correct, that it actually makes it to B from A.
0:18:42.0 Joseph Morais: Yes.
0:18:42.6 Rick Hernandez: Right. So we need to be able to govern what goes out, observe what's going on and monitor it so somebody can actually act upon it in case that there is a problem. Those are three things that are very, very important. And governance is something that, I mean, if you are having creating a tool today and you don't have built in governance, what are you thinking? It's what will differentiate your tool from being the tool from somebody else that's just a wannabe. And the reality of it is it's so important that we have whole groups of people, architectural committees within our clients that are directly working on justice. Just governance, just observability, just monitoring.
0:19:23.5 Joseph Morais: Wow.
0:19:24.8 Rick Hernandez: It is that level of important. It's better to start with the right foot. It's better to actually start with the right processes. It's better to start with the right ideas, with the right architecture, with the right product set. It's just easier. Most of the clients will start with something small, but they never have a plan to just stay there. We say we're staying with this and then we want to grow it, so you actually start with it correctly. Then by the time that you grow it, you have the basis for doing something good, something that is repeatable, something that extend, observable, then monitored, something that you can go over. Once it actually starts sprawling out throughout a corporation, if you actually have many different chiefs, it just complicates everything. And at the end of the day, we are the same company. What we are trying to do is actually make the company do better. So it's better to actually have something that is unified.
0:20:14.1 Joseph Morais: Yeah, it's easy to point the snowball in the right direction, like when it's small. But once it gets big, it's very hard to stop it from rolling.
0:20:22.7 Rick Hernandez: You need somebody very courageous to stand in front of that and go like this.
0:20:26.6 Joseph Morais: Right. That's how you stop it. Someone has to stand in front of it and nobody wants them to put snow on their face. So I bet you didn't expect this question, but what's the future of data streaming and artificial intelligence at EY?
0:20:40.7 Rick Hernandez: It used to be that AI was only on certain areas when you were actually looking for insights into data or something like that. That's probably how you and me actually got into this area.
0:20:53.2 Joseph Morais: Absolutely.
0:20:54.0 Rick Hernandez: But there's more than that. There is a discovering on how to do processes, how to automate the way that something goes, automated workflows, how to go about not just basically just getting the insights, but applying the insights. So agentic AI is big right now because it's able to act upon a discovery, it's able to act upon an interaction. And that is that next step where it's going to allow us and somebody else to create probably a website or something in your browser that will be able to do something for you, kind of like an agent will do today. So that's kind of where everything is moving right now. And again, lots of data moving around. That basically means that we want something that will be, what, transactional in nature, something that will actually stay, something that will be resilient, something that would actually scale. We want all those different things so that it can actually handle that infuse of people working with this AI all the time.
0:22:02.0 Joseph Morais: Next up is the Runbook where we break down strategies to overcome common challenges and set your data in motion. How does EY evaluate and select the right tools to use? For example, how do you balance scalability, cost and integration with existing systems?
0:22:17.5 Rick Hernandez: Most of the people that work at EY at the architecture level is people that have had long years of experience actually doing exactly this. We've seen stacks from Microsoft, from IBM, Confluent. We have seen stacks from some microsystems back in the day. All these companies actually doing something and offering that something to the end user through that experience where I can actually see all the gotchas, what is it good for, what is it not so good at and so forth. We have whole teams inside EY that their job is to go and take a look at these things and make sure that, what is it that they do? What is the functionality and what is it that it can do and can't do? And we actually go and present that to our clients and say like, okay, sometimes we ask, of these five things, which one do you think will be best? And we can say that, wait, we cannot tell you which one is that we recommend, but we can actually tell you which one is the more capable, which has that more a fuller feature set, which one actually has all those things that we're talking about that can actually do the integration, but can also be observable and monitored in gobert so that you can actually be doing things the right way, not just for today, but five years down the road.
0:23:33.3 Joseph Morais: Yeah, that's really important. I think people miss that sometimes. It's like, does this solution, does this just work for now? Does this just address today's issues or does it address issues that will happen five years down the line? And if the answer is no, you might want to look at a different solution. On the other side of that, are there any tools or approaches, like batch workloads that EY actively avoids or advises their customer avoid?
0:23:57.9 Rick Hernandez: We don't avoid work. The whole point of our being is to actually tackle the solutions to help our clients. So if our clients actually ask us that, hey, we need help with this, and we basically say, if we can, if it's within our capability sets, we go after it. I personally do a lot of legacy work just because that's like my area background. I came from that and into all the open technologies. There are some people that actually like the legacy technologies and that's where they want to be. But guess what? That legacy technology was not created to work with the newer stuff. We had to actually create some type of interface that the old and the new can actually work with. So we actually have a team that does exactly that, mainframe modernizations, or I like to think data staging. We actually take the data from a mainframe and actually stage it in something like Kafka or something like that, so that all the new technologies are actually hitting this staged data area while leading the mainframe to do what it does best. So there's many ways of actually doing these things. And it just depends again on the client and what is it that they're trying to do.
0:25:12.7 Joseph Morais: Absolutely. We see mainframe integration quite often here at Confluent, for a bunch of reasons. One, mainframes are expensive to access. So offloading that, putting that data where it can be accessed elsewhere outside of the mips and concepts and things like that is great. But also like you mentioned, it now allows you to take this data that was never meant to be incorporated in data streaming, incorporate it and build way more interesting technologies. And you know and I know that mainframes aren't going away anytime soon.
0:25:40.0 Rick Hernandez: Yeah, they're not going to go away. The mainframes are going to be around forever and they are perfect for what they do. So let's be sincere about that. If it were created for something and for that something they are perfect and nothing is better. However, with the way that the technology is working, there are certain ways of doing things that they couldn't think about 50 years ago. So that's why we actually have to come up with these ways of coupling the old and the new in a way that actually makes sense. That's why companies like Redis exist. The reason companies like Confluent exists, is to actually be able to take that data from the staging so that somebody else can do something with it. That's something that I like about the Kafka as a product, is that Kafka is not just a messaging, it's just not just a messaging platform. It also is, well, to a certain extent a data storage.
0:26:30.4 Joseph Morais: Absolutely.
0:26:31.5 Rick Hernandez: And so by actually taking that data and staging it there, you are basically just saying, okay, well use this as your data store for now and we can actually make that data to live for a certain amount of time. So to all effects, it acts as a very effective cache.
0:26:52.4 Joseph Morais: Let's dive into how you got EY customers to fully commit to data streaming. Rick, if someone gave you the requirement we need more data and faster, how do you translate that into, we at EY generally implement data streaming to make that a reality? Take us through that conversation where someone starts with the pain or the requirements and then getting into that data streaming conversation.
0:27:12.0 Rick Hernandez: I think we start by asking what are you trying to do? Because that is a requirement that it's been kind of like distilled somewhat, but it doesn't really tell us what is the business case for it. So at the end of the day, what are you trying to do? And then when they actually tell us what they're trying to do and it is a use case that applies into like near real time space, then that's something that we go, okay, data streaming actually works well in this particular instance because you are trying to actually get data, you're trying to actually move it quickly. We're trying to move a certain amount. So the scalability components of it actually comes into the question. You can actually both horizontally and vertically scale it so that we can have certain volumes and so forth. And then, I mean, what is the need beyond that? Where does it have to go? Where does it come from? I mean, there might be some certain instances where it's very easy to get data out of a source by using Kafka technology, using Confluent technology. There might be instances where it would be very easy to actually sync some of that data by using that technology as well.
0:28:17.7 Rick Hernandez: So when we take everything into account, the overall solution is screen data streaming idea.
0:28:24.4 Joseph Morais: So what about dissent from your customer's leadership? How do you make an effective conversation around the importance of data streaming, stream processing, integration and governance?
0:28:33.5 Rick Hernandez: Yeah, yeah, it starts like that as well. It starts like, okay, what is it that you're trying to do? It's usually because there is a technology transformation effort completely in place and they are trying to accomplish something on the business side. What is it that you're trying to accomplish on the business side? Oh, we want to basically go end to end and reduce n amount of time or n amount of interactions. Okay, that makes sense. Okay, does that screen data streaming to you? Probably not. But then you basically start talking to them more about, okay, well how are you thinking of accomplishing that? This is, well, we have data in all these different systems and you have to jump through like 20 different areas in order to do this and do that, blah, blah, blah, blah, blah. And then you start thinking of it as like, okay, now I see it. Now I see that there's possibly a data streaming scenario here, like a Kafka scenario, where you have data coming from different places and they have to actually be staged in one particular area, has to be converted, transformed, and then actually routed to different places.
0:29:33.0 Rick Hernandez: So now we're thinking sources, we're thinking syncs, we're thinking transformation, we're thinking operational control, we're thinking governance, we're thinking monitoring, we're thinking observability, screens data streaming to you. That's how you actually go in that transition. So we come in with a lot of listening, not necessarily talking, a lot of listening. Because at the end of the day, what we want is a solution that will actually solve their problem. Not basically saying, oh, this is cool, let's do that.
0:29:58.7 Speaker 2: As I was kind of thinking through what you were saying, I kind of framed it like mapping the challenges to the technology. Listen to their challenges and then it becomes blaringly obvious that these solutions are the right ones. And then you really just kind of, hey, show them that mapping and then they get it.
0:30:15.7 Rick Hernandez: But this is what is cool about it, once you actually get them to understand that what they are talking about really is data streaming, then they start thinking about it and then they go like, oh, if we can do this, then we can do that. And then all of a sudden it goes like, we are a world class organization. We have all this data, we have all these different things that actually make us the best in class. We are great. We actually do it through streaming now, now we become that much better. I was telling you the other day, this is like giving wings to a tiger, right? He just actually makes it that much better and faster. It actually makes it that much more powerful because now you basically have your data actually doing work for you. You're able to actually do the analysis on the fly and actually operate upon that quicker than you were able to do before. That can only make whatever you're trying to do better.
0:31:14.8 Joseph Morais: Now, let's shift gears and dive into the real hard hitting content. The data streaming meme of the week.
0:31:19.4 Speaker 4: What we need to do now is get focused and stop pointing fingers. You're a problem. You're a real, real problem.
0:31:27.3 Joseph Morais: Yeah, I kind of imagine this being you, Rick. Like you looking at the broken data pipeline and just giving it a hard time.
0:31:35.9 Rick Hernandez: I beat this guy before. I don't know why, but I beat this guy before. There is a saying in this that you're only as good as what you actually need to do your work. If your data is not good, it doesn't matter how good you are, the rest of it, you're not going to do well. That's why governance, I'm sorry to keep harping on it, but that's why governance is so important. That's why your data has to actually have schemas and contracts on them. That's why we actually had to have version control. That's why we had to have the ability to adapt to something from different versions. You go from 1.0 to 1.1 that it doesn't break. We need to have that kind of ability because if not your data pipeline becomes problematic. And then I had to go all like, this fella turns it's like, you are the problems. We don't want that. I don't want to be that guy. I don't want to be angry at people.
0:32:35.8 Joseph Morais: You want to be happy. Of course. Yeah. If a broken data pipeline is the weakest link in a chain, that chain is not going to be very strong.
0:32:42.7 Rick Hernandez: That's exactly right. Especially because the format of the data is the cornerstone of your streaming. Right. If your data is not good, it doesn't matter how great your technology is on the other side. Garbage in, garbage out. So we have to actually get that done right and correctly the first time. And there are tools for that.
0:33:10.6 Joseph Morais: All right, before we let you go, we're going to do a lightning round, byte sized questions, byte sized answers. That's byte like hot takes but schema backed and serialized. Are you ready, Rick?
0:33:20.3 Rick Hernandez: Oh, man, no.
0:33:23.2 Joseph Morais: What's something you hate about IT?
0:33:25.6 Rick Hernandez: Oh, the hours.
0:33:28.4 Joseph Morais: As somebody who carried a pager most of my career, I get that. What's the last piece of... What's the last piece of media you streamed?
0:33:39.9 Rick Hernandez: Oh, Facebook. It was cats. Facebook cat.
0:33:45.2 Joseph Morais: That's the first time I heard that answer in any of my shows. But it is a perfect answer.
0:33:50.0 Rick Hernandez: My son has like seven cats. He's always sending me stuff like I'm putting it on Facebook or his cats doing stuff like, these are like the smartest cats. They're always on the computer doing things. I'm like, hire them.
0:34:01.4 Joseph Morais: When there's seven cats in the house, I imagine some of them are going to stand out. So what's a hobby you enjoy that helps you think differently about working with data across a large enterprise?
0:34:11.2 Rick Hernandez: Writing. I'm a writer.
0:34:13.5 Joseph Morais: Nice.
0:34:14.1 Rick Hernandez: Yeah. And it helps me to take off my data hat and my engineering hat and actually think more with the heart and it resets me for the next day.
0:34:28.4 Joseph Morais: So what is your advice for a first time chief data officer or somebody else with a very equivalent impressive title?
0:34:36.1 Rick Hernandez: Chill, we got this.
0:34:39.2 Joseph Morais: Just call EY. We got this. That's great. And that, that fits perfectly into my last quick byte. Any final thoughts or anything to plug, Rick?
0:34:47.6 Rick Hernandez: No. Well, I mean, not going to plug anything like that. But just know that there's a lot of really good content out there. There's a lot of really good like engineers and companies and everything. If you are part of like a global company, that basically needs to have somebody all over the place with something. A global SI is like the way to go. EY designated office is 160 countries and we actually have people that do global support, just like me. So I myself actually work in Latin America, Europe, Asia Pacific and all of North America. So we always have somebody there to like, help you out.
0:35:25.9 Joseph Morais: That's excellent. Well, thank you so much for joining me today, Rick. And thank you to EY for the partnership. And for the audience, stick around because after this I'm giving you my top three takeaways in two minutes. Great conversation with Rick. Here are my three top takeaways. Well, this concept of a shared vision, if you're trying to implement something new that is hard, you want to have a shared vision. And how do you get to that shared vision? Well, Rick talked about mapping challenges to technologies. So you first start, what are the business challenges, what's not working today? And then, once you realize that this sounds obvious like something that's data streaming, you then map that technology back to those challenges and you create that shared vision. And then you have to evangelize that shared vision. Whether you're, kind of, if one implementing it as an engineer or you're some middle manager or all the way up to CTO, you want everyone to have that vision so they can understand why are we enduring the pain of change. Rick also talked about this idea of tools versus solutions, which I absolutely adore.
0:36:27.2 Joseph Morais: There are plenty of open source tools out there, technologies that work especially in a proof of concept or when you're at very low volume or very low throughput. But what happens when you're at enterprise scale, does it have the observability, does it have the security? Can it scale up and scale down? And that's what really differentiates tools versus solutions. And I think Confluence got a really great set of solutions. And the last thing I want to take away, and this is going to stay with me for a while, the idea of adding wings to a tiger, right? So EY gets to work with some of the biggest, most well established companies in the world and they're tigers at what they do, but they're not necessarily great at data. They're not doing it in real time. They've probably been around a long time. They're doing things in batch. So when you take one of these amazing best in breed companies and you give them data streaming, that's adding that wings to that tiger. It's kind of like drinking a Red Bull and having a tiger fly away. And ultimately, when you add wings to a tiger, you get that tiger's data to work for them.
0:37:24.2 Joseph Morais: Really impressive takeaway. That's it for this episode of Life Is But a Stream. Thanks to Rick for joining us, and thank you for tuning in. As always, we're brought to you by Confluent. The Confluent data streaming platform is the data advantage every organization needs to innovate today and win tomorrow. Your unified solution to stream, connect, process and govern your data starts at Confluent IO. If you'd like to connect, find me on LinkedIn. Tell a friend or coworker about us and subscribe to the show so you never miss an episode. We'll see you next time.