Welcome to episode 6 of the first season of "Your DataOps Advantage," a podcast series by Hitachi Vantara! In this episode, podcast host Bill Schmarzo (our CTO of IoT and Analytics) sits down with our CIO, Renée Lahti, to highlight outcomes from Hitachi Vantara's DataOps and Digital Value Enablement journey. This dynamic duo will discuss tips and tricks for overcoming big rocks, what they learned about the process and the big results they've produced for the organization and its customers. Tune in – it's time to plant a money tree of your own!
Bill Schmarzo is regarded as one of the top Digital Transformation influencers on Big Data and Data Science. His career spans over 30 years in data warehousing, BI and advanced analytics. As the current CTO, Analytics and IoT for Hitachi Vantara, "The Dean of Big Data" guides the company's technology strategy and drives "co-creation" efforts with select customers to leverage IoT and analytics to power digital transformation.
Renée Lahti develops and implements information technology (IT) initiatives that align with Hitachi Vantara's mission. She leads the transformation of the Hitachi Vantara IT group from a conventional organization with many operational silos to a dynamic, agile team that efficiently focuses on business outcomes. Today, the IT group is an organization of cross-functional teams that include business and IT functions to work together using iterative, agile sprints.
Hello. Welcome to the Hitachi Vantara DataOps advantage podcast. My name is Bill Schmarzo and I'm the chief technology officer of IOT and analytics here at Hitachi Vantara. The DataOps advantage podcast is going to track the trials and tribulations of different organizations of different sizes across different industries as they wrestle with how do you get value from your data? What we're going to do is we're going to talk to these organizations about how they're leveraging DataOps to uncover value in their data and help them to figure out how to drive return on their data investments.
Welcome everybody to the final episode of the DataOps advantage journey, the journey that Hitachi Vantara has gone on to resurrect its data-lake to make it more relevant to the business and less of a technology experiment. One of the things that you're going to hear us talk about, and I talked to Renée, our CIO here at Hitachi Vantara, is this concept around second surgeries. A second surgery is something that we've seen a lot in technology. I first came across second surgeries in the 1990s when working with Brad Cirric at Business Objects. We saw customers all the time having all these data warehouse BI problems where they would build this data warehouse and then they try to put BI intelligence tools on top of it and it didn't work. We're now seeing that the same problem with data-lakes, and to a certain extent with data science, where organizations built their data-lakes and threw Hadoop in there and they put some data in there and they hired some data scientists, and they waited for magic to happen, and they waited for magic to happen, and they waited for magic to happen. Bueller, Bueller, Bueller? Of course, it never happens. So, what happens is, there's nothing wrong. Nothing wrong with a failed first project because you have to understand and learn the technology. The problem that CIOs have is to believe that what they built is going to scale and be operationalized. So, what's happening now is organization like what we did here at Vantara organization, they're realizing that they need to do a second surgery. They need to take what's good about what they built, but change the frame, change the context, and focus on how are we going to deliver business value. So, to transition this data-lake from this technology exercise. It's something that one of my clients called a collaborative value creation platform that allows the organization to work together to leverage and exploit the economic value of data to deliver business impact. So, Renée, let's talk about this data-lake second surgery journey you went on and, and how you got to where you were and how we got to where we are now.
Yeah. When CIOs have started their journey. They just put a bunch of data, build a big data-lake, and build it. They will come and have the aha moment. We all do. We all have. They are coming there and showing up. You realize you may have wasted something and suddenly going through this workshop and the journey that we're on with the DV work, we didn't waste it. You had to have done it. You have to learn from it. But, the concept of the second surgery really is about, can we go back and re-craft what we have with proper context. It's not about technology and tools. It's all about the data.
Good points. Yeah. So, the fact that you were in a second surgery situation isn't the problem. In fact, I think it's common for any new technology that comes out. We saw this with business intelligence and data warehousing. What we're seeing now with data science is that the environment you put in place, at first is very technology centric because you're still trying to learn about the technology. So, the air isn't doing that. The error is believing that that's going to carry you through to operationalization of scale.
That's right. I mean, the engine is, it's great. The platform is there, but you have got to give the right context and you sometimes don't know what that context is until you do the first round of surgeries and you go, okay, now I know I've learned something. Let's go back and, and let's clean it up a little.
Yeah. I moved from being a technology exercise and just proving that it can work, a proof of concept, so to speak, to now, you're part of the business. You are generating business value and business relevance.
And for us as CIO, as we all know, big investments, you don't want to have to go back and say, I screwed up. I can't use it. Beauty of this one is you can use it. Yeah. You can reuse it and reuse it.
Yup. Perfect. So on this journey, so now that we've sort of wrapped this thing up, we've come through the proof of value. We've built all this stuff, you know, we've got the models built in, the data Lake is humming and we've got a bunch of great stuff happening. What were the aha moments for you in this process?
Oh, there's one that comes to mind that's just amazing. So I think we talked about the journey and part of the journey is we all got in a room across functional team, HR, finance, marketing, everyone talked about their tons of data sources, 10, 12, 30, 50 whatever. But when it came right down to it, we only needed three data sources to really bring almost 90% accuracy to this particular business problem. And that's amazing.
Yeah, I really, I, I do think that's a real important aha moment is that you read time and time again and I talk to customers time and time again about they're just putting all this data into a data-lake and if you don't know what problem you're trying to solve, then you don't know what data is most valuable. Understanding that those are the three most important data sources. And again, we actually will spec those data sources we're going to pop up as we go down to the future use cases. How does that affect your investment?
Well, the investment is, we know those are the three most important data sources. So we know where to laser focus and leverage the assets that are most valuable. We've all data sets are not equal in value. Right. And we just found the three most valuable ones to give us 90% accuracy. That's, that's the mining for gold. That's the gold we found and we're going to reuse it and reuse it and reuse it. Um, as can and augmented. Yes. But we know those are the three pillars of the most valuable, um, uh, data sets that we need to start with.
Okay. Excellent. Yeah. So many IT projects have this in this disbelief that these things are big bang projects and we probably get that because of the ERP things we went through and how we had to spend, you know, $30, $40 million on software and tech, set 10 X add on services and millions of people died on the path there and such. So that, and we come into this world today and especially data science and we've actually seen some very large players in the data science space who've also embraced that big bang approach. I won't mention the company with the three letters, but this approach is very different. It's not a big bang approach.
So first of all, CEOs hate big bang. We just are not fond of it because it takes a long time. By the time you get to the end, even if you get all the requirements right, it's so long in the process that the requirements have changed. So when we talk about this particular journey and the approach, it's sprints of value. It's small datasets. It's a subset of all of the data in the universe that you need, but it's, it's dependable enough to deliver that value as a CIO. That's awesome to the business in quick sprints. And then you go onto the next one, no more big bang. You actually are bringing value almost within a quarter or half a year versus ERP. SIS, Projects of the past are big projects in the past where years before you saw the value.
I'm already envisioning hashtag sprints of value, right? I think that's a great way to think about it. And as you think about these sprints of value, so we've done the first use case here. We've created propensity models, we've gotten the 90%, we're rolling it out now into our operational systems. What's next?
Well, the beauty of this methodology is that we already know what's next, or at least we have the population of potentials next. We picked this one because it was the highest valued, but we have a list of 11 that we prioritized all of it for the most part. And so we know the population, we know what the subset of the population is. We're going to go back and look at that list again, probably do a smaller version of, Hey, let's see what's next, right? What we think is going to be highest value next for Jonathan and the CMO team. And we've created a repeatable operational process. So it's not about projects anymore. It's how the data scientists sit on Jonathan's team can pick up and just start going. And that's what's next. We now move on to potentially another friendly HR, finance, uh, the revenue officer and do something similar with them to get them their roadmap of data, data work.
So as a CIO, you've created this, almost this problem for yourself that now you're the most popular kid in the block, right?
Oh we are cool. We are so cool
I was going to say CIO is cool again, but I don't think CEOs are ever cool. So now you can finally say, CIOs are cool. You're the most popular kid on the block and you're going to have, as you mentioned, finance and HR and manufacturing, other groups come knocking on your door to the CIO is, are listening to this. What do you recommend that they should do? How do you manage this, all of a sudden this, this avalanche of people who want to go through this hashtag sprints of value.
Well, first thing is, it must be controlled and governed and have some type of a framework because the one thing that we all realize through our process is we all must come together as a cross functional group. If we all go do our own silo work with our own silo data sources, none of this would've shown up. So you have to have a front door and efficient front door, but a front door and a governance methodology to say, Hey, let's all come together and talk about what we think we need. Where the data all has to reside in one place and it has to be trustworthy data. And as the CIO and somebody like yourself, we're the ones that are agnostic to what people are going to work on. We want to help them get their business value out of it, but we should govern it with a consistent and a um, um, a fair and objective way.
Yes, very transparent. We'll know what's going on. A single place to come and make your case. The decisions are based on value, not personalities or technology. And what I think is really interesting about that approach, Renée, is it that sets the stage for an organization like Hitachi Vantara to exploit the economic value of data, right? If we believe, if we know that data as an economic asset is unlike anything else we've ever seen, right? It never depletes, never wears out. And the same datasets, if curated, we can use a cross on several use cases at a marginal cost. It starts to approach zero, right? So there's this governance council almost becomes this economic value of data reservoir or spokesperson or you know, champion that's really helping the organization to understand where and how to exploit that data, the value of that data.
Right. Well, I think that the thing you said earlier, it doesn't expire. It's reusable and it seems like a common, sensical thing. But when you had zettabytes of data and saying it's reusable, it's kind of an overwhelming idea, right? But if you have an asset, a digital data asset, and it's like a little Lego, it's a little square red Lego, but it can fit in a whole bunch of different places. You don't need hundreds of those little Legos. You just need one. And every time you reuse it, it delivers even more value. Over time, which is great.
Yeah, it's great. It's a different way to think about data and you, the CIO, is literally the owner of that, that unique economic value. But you know, you may not be the coolest kid on the block, but you may be the most powerful kid on the block.
Wow, Don't let that go to my head.
So let's, let's wrap this up by talking about the dataops journey.
So what is it about the DataOps journey, if you're giving guidance to the CIO out there, what are some of the key points that they need to understand and embrace as they go on their own individualized DataOps journey?
Well, I think there's probably three things. So first, don't get discouraged and admit that yes, you built it, they didn't come. It's okay. Right? But it is something that you can go back and fix. So, the second surgery concept, right. Okay. So, that's one. The second is you have to have a friendly, like we talked about that early on. If you didn't have a friendly, if I didn't have a friendly in Jonathan Martin is our CMO, I would still be looking at piles of data and hearing a lot of noise about, put more data in there, put more data and nothing would show up. So, you have to have somebody that is going to be a business partner to just continue to stay laser focused, which he did. He stayed laser focused to the one thing he was trying to solve. We didn't get distracted by the purple squirrel over here or the whatever there. We just stayed focused. And when our teams kind of wandered off the path, we brought them back. So that's a really important thing. A friendly, and then the third one is really, I just kind of talked about it as the framework. You have to have some consistent framework and don't deviate. I mean, you don't know what you don't know. Right? And the only thing is going to figure out what you know is by continuing to work the path in a consistent manner.
The one thing I want, I'm going to ask are you going to build upon the friendly is critical. And when we work with customers, we find that a lot of organizations want to go to the most powerful person who may not be friendly, right? You're better off picking somebody in the organization. Maybe they're not the most powerful part of the organization. They really are looking to embrace data and analytics to transform their business. They are, are the living, um, version of digital transformation and digital transformation does happen, I think across unit by unit, across the organization. You build success, people see the success and then the governor's council is created because you get this sudden swarm of people coming in.
Well, you have your friendly talking off the back fence to his neighbour or her neighbour saying, "Oh, how'd you get 90% accuracy on this cross sell upsell thing?." Well, this is what we did and then all of a sudden, yes, it kind of becomes a buzz. I think the other thing is that they go to the most powerful person to your point, but they also look to somebody, sometimes a COO, CEO, not because of power but also where they are in the organizational structure. But, when you try and solve their business problem, it is so strategic and broad based. Yes, you're going to struggle still. So again, my advice to the CIO is find a friendly CMO. It's like a discreet line of business friendly so that you aren't trying to solve world hunger as your first project.
And when you do that, the use cases are literal, it's like printing money. It is your pretty money for your customer.
There's a whole bunch.
Right. I know. That's how I fund my Starbucks. That's great. Well, thanks. This has been a great journey. We're going to be interested to talk to you as you move forward as Hitachi Vantara, especially on the governance council because I know a lot of CEOs have questions about, yeah, I can see I can have that first success. How do I scale that? How do I operationalize it? So, I think there'll be more coming conversations about this.
All right, thank you.
Thank you Renée. So, let's talk about what we just heard from Renée Lahti regarding her journey that she went on and the DataOps advantage. Number one is the death of big bang projects. That is the, the idea that CIO projects need to be big bang from a data and analytics perspective. In fact, that approach we've learned isn't the right approach at all. In fact, you're better off to bite off very small bite size, ROI driven use cases that deliver value. The other thing we learned from Renée was the importance of finding a friendly, now people get confused between a sponsor. Somebody who is very high in the organization of CEO or COO who is going to sponsor this versus a friendly someone you're going to work with in the trenches, get your hands dirty to actually do the work. So yes, it's important to have a sponsor, but it's more important to have a business friendly who's willing to get dirty in the mud with you as you try to solve the problem they most important to them using data and analytics. And the third thing we learned was the importance of this digital value enablement framework. A framework that we can use over and over again. Because after we've done the first project and we've had success like we've had here, we're going to start seeing more and more use cases pop up, not only the use cases that came out of our first digital value enablement engagement, but now we're going to have finance, we're going to have HR, we're going to have manufacturing, we're going to have customer service, other organizations. We're going to want to come to us. So we need to have a process, a framework that allows us to take them through the same value engineering process to identify where and how to start and probably put in place a governance council so that we ensure that the organization is building off the same data and analytic assets. Which of course brings me to my favourite topic, the economic value of data. Now it's what's surprising to me is how few CIOs leverage this concept to drive business relevance in the organization. Think about data as an economic asset. It never wears out, never depletes. And the same curated dataset can be used across an unlimited number of use cases at a marginal cost approaching zero. Think about the power of using data and analytics over and over again to shrink time to value, to de-risked projects, to provide more value to the organization. And as I said to Renée and the way out, it isn't about making the CIO cool again cause the CIO probably never was cool. It's about making the CIO relevant to the business, making the CIO the owner of the economic value of this data that at its heart is what's going to drive your organization's digital transformation. So until our next podcast, thanks for listening.
I hope you enjoyed this podcast. If you want to learn more about Hitachi Vantara, track us on Twitter @HitachiVantara, or if you want to follow me, follow me @Schmarzo, I'm the only one on Twitter. Thanks for your time. Until next time, cheers.
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