EP 8: Data Science in Today’s IT Landscape w/ Shawn Rogers

ABOUT THIS EPISODE

The relationship among data science, strategic IT roadmaps, and the world of enterprise architecture can be complex to say the least. 

That’s why we asked Shawn Rogers, VP Analytics Strategy at TIBCO Software, about what it means for EAs and the C-suite to use data for the IT landscape. 

In Episode 8 of #UnleashIT, we interview Shawn about all things data science.

We discussed why data science is the sexiest job, signs that you need a data scientist, data organization and archiving, data organization and archiving, and how EA is driving digital transformation.

To hear this interview and many more like it, subscribe to the Unleash IT Podcast on Apple Podcasts, Spotify, or our website.

Unifying all of your data, making it practical to utilize and putting the right data in the right place at the right time. That's super important and we see a lot of companies with needs there. Welcome to unleash it, a podcast where we discuss the experiences and ideas behind what's working in enterprise architecture and digital transformation within the IT landscape. Unlock Your Business has digital capabilities. Transform your enterprise architecture. Unleash it. Let's get into the show. Hi Everyone, and thanks for joining us for today's edition of unleash I t we have a very special guest today, Mr Sean Rodgers, who's the VP analytic strategy at Tipco Shu. I'm welcome to the show. It's great to be here. Thank you for having me. Yeah, so why don't you first let's start off with you just telling a little bit about your backgrounds, because you have a fascinating career, and then just a little bit about what you're working on now for TIPCO. Fantastic. I started in our industry in the S in the publishing business and I had a magazine called Dm Review magazine that covered the technologies that you and I are going to talk about. So the anchor goes way back that I'm even comfortable explaining. So I've been here a long time. But the cool thing about it is I've got to watch the same thing. You have this maturation of how we use data, how it's become more are available more, I think, critical to how people differentiate their companies. And so, whether it was in the early days of data management and data warehousing or bi or data visualization, and now we're in that wonderful, cool world of data science and AI, all that time data has been the constant. So my career goes back that direction. I spent some time as an industry analyst and now I'm the vice president of analytic strategy at TIPCO which is a great place for me to be because tip COO's a wonderfully data centric company. So let's talk about some database is a bit around forever and remember...

...when we first got into client server computing, you know, SQL, all this stuff that was coming on. But it's taken some major lease now, especially with the you know, innovative technologies that are coming out, like Iot, would have been some of the major critical changes you've seen maybe like the last five years, you know, with data and how we're organizing it and dealing with it. You know, it's neat that you started the question with database, right, because, yeah, that's that's what we used to look at, right, it is what database is it in? Is it is it in this one or that one? How's the data organized? And, as you well know, and everybody listening here you know, we went through this unbelievable phase almost fifteen, twenty years ago, what we call big data, right, which is when everybody started looking at different types of data. And, you know, to answer your question, I think we've landed on the next iteration, which you know, it's all the day data, any data, right, and companies need and want to leverage any or all data from any place in their organization in a multitude of ways, and I think that that's been one of the most cool shifts. You mentioned Iote and Iot data is like this cool edge data that doesn't necessarily begin its life where we would expect it to. It it's attached to a censor or a process, and this idea of being able to bring that into or bring analytics to it, and I see examples of both, by the way. Or companies are focused on bringing all their Iot data cross the network into a database to use it in a more traditional way. And I see customers who use our technologies to put actual algorithms out on the IOT devices and to turn you know, insight at the farthest edge of their business. So yeah, I think I in retrospect, looking back about how I got into this industry, I think I picked a good one. You know, it's good. It's good, a really fun ride and I don't see an end to it anytime soon. Now, in fact, we continually exponentially increase the amount of data we're collecting and storing and when, I think when the industry is a whole, one...

...of the biggest challenges is what data is important and what we do with the rest of the data. Where is it all important? What are your thoughts there? Yeah, well, you know, there's a lot of different strategies for what data to keep and how long to keep it. Some of it's driven by, you know, compliance and regulatory things. She kind of got to follow the rules with particular types of data. But you know, I read a lot, I'm sure you do as well, and I read a lot less articles about, you know, archival strategies. These days, the cost of keeping data has really allowed us to attack data in a different way. So I think most companies have and this is my experience when I visit our customers. They got more data than they know what to do with. So you know, and you and I before our call were reminiscing about the the magazine that I had, and back in those days we used to write about how decisions were being made on twenty percent of enterprise information. The other eighty had to be ignored because it was too costly and too difficult to wrangle. We don't do that anymore. No, now we're wrangling the data that we want. We're looking at data that we've never looked at before. You know, the first book I wrote was on social data, which was almost like a new sort of data. Social data has been something that's only been around for a while and I wrote about how to use it and leverage it alongside of other data that you were using in the enterprise to make decisions. So yeah, I think the data landscape is just become incredibly shifted. And I'll add one last piece to the answer. I think when I was in analyst I wrote a lot about the hybrid data ecosystem and the hybrid aspect of data ecosystems was also driven by new personas. You know, whether it was a DVA, which wasn't a new persona, but business users were certainly new personas customers wanting our data to do things with it, and partners. Was a new sort of paradigm. And then along walked into our enterprise environments these folks called data scientists and they were brand new users of our data and they wanted to do they didn't want it, they didn't want you to transform or...

...change it. They wanted granular data and they wanted to get at the root of everything, and that caused some really interesting hybrid challenges. Are On data, but I it's a great question, but yeah, the database to Iot to, you know, data streaming, all of those things are where we're at right now. So it's kind of cool. Yeah, so let's talk a little bit about data science. To be when I look around, everyone says how hard they are to find right and what you do find, then it's hard to keep them because they get recruited every place else. It's obviously a very sexy feel to be going into right now, or at least a field that's high end demand. Harbard called it sexy, so you're not far off. Yeah, there are their article about data scientists a few years ago they called it the sexiest job out there. So, yeah, it's an incredible market that they have. I give a lot of public talks and when I talk about data science I'll urge the crowd. I said if there's anyone here today that is looking for a raise, get on linkedin and put the word data science into your bio and your phone will start ringing. So that's I agree with you. So, for for your organizations that are considering bringing in a data science, you know function, how do you start going about doing that? I mean, what's the trigger that makes you go, okay, we really need to get a handle on this, we're going to need a data scientist in house. I mean, what are those trigger points to get to the point where that companies are more successful, you know, in managing their data with data science? Yeah, that's good question. You know, I see it a lot. There's this convergence. If we look at traditional business intelligence platforms or data visualization platforms. Part of what occurred when they got really good and the data visualization stuff got really good, as it kind of democratize the access. So back in the earliest days of Bi it was only a handful of people at a company that have access to the VII platform. Now it's very democratized and very open across most enterprises. And so if your company's being successful with insights that are databased right on data, you mature...

...over time and part of that match duration is is, as you move forward from looking at what used to happen in your business looking backwards at your data, you get to a point where you want to start looking at what's happening now and predicting what might happen in the future. And when you cross that bridge that's a data scientists. Data scientists will help you use algorithms to understand these massive amounts of data and also to do things like predictive analytics to start to predict against your supply chain, or you want to become more sophisticated around, say, risk decisions. Those types of things are usually the catalysts that will get up an insurance company to say hey, this is where I have to go. Or and healthcare, personalized healthcare information, is some place where you see it awful lot of traction getting. So I to answer your question about you know what, what precipitates it or where do you start? It's usually because you've matured beyond the Bi platform and you're trying to converge these things and typically call it hyper converged analytics. If you can bring together data science, Bi and streaming or real time all in the same place, man, that is an incredible value proposition and a lot of companies are heading in that direction. And in to get there you often, almost always need some data science expertise in your business. So that's usually what starts at for most folks. And you mentioned the democratization of data right. So everyone reads data every all your business lines need data, whether you're in finance, marketing and sales certainly live on and breathe data. Product Management, rap operations, all data and bringing a data scientists, how do you how do you maintain that open dialog? Had Add, to make sure that you're being as efficients as possible with the data. Do you have across an organization across that very different landscape? So, to be clear, my experience has shown me that most of the companies are struggling with efficiency around data science and so and there's a good...

...reason for it and you hear these stories like you might. You might be at a our jener price company speaking with them and you'll hear someone in a meeting go, well, the you know, the folks over and finance. They have like this incredible data scientists, you know, and it's like it's almost like a cap, it's like a secret. They don't want everybody to know that Susan exists in the finance department because she's a data scientist. As more business leaders or line of business people know that they are, understand that they want this style of insight. What happened and is happening still, is they get involved in that Unicorn chasing of the data scientists. So they want to hire one for the group, higher one for the group, and then companies find out, back to your efficiency side of your question, that they're not being very efficient. The other problem with data scientists, as they're all kind of data artisans. They all come with their own paint brush and colors, and what I mean by paint brushing colors is some of them walk into your business very knowledgeable with python and that's what they like to use, or they like to use the our language or they want to use an application like the ones that you can get from my company, and so they come armed with their own brushes and paints and there's an issue to get them to collaborate, because they're all talking or painting different scenes, right. And so the next level of maturity is usually when companies will do one of two things. They'll either put a center of analytic excellence in place, which is where the data scientists all kind of come together to work on things, or it will be driven from the Sea Eos Office to either put in places CDEO, a chief data officer or chief analytics officer, and the chief analytics officers tend to generally be a little higher up than cdeos are. I A friend of mine calls a CEO's offense and cdeos defense, and sometimes you need both. But the bottom line is you either mature through executive mandate and sponsorship sea level on down, or the company gets smart and brings these assets together in the center...

...of excellence so that you can leverage all the paints, leverage all the brushes and all the artists in one place, if that makes sense. Yeah, so this typically fall under a cio or are these types of positions going directly to the CEO. There's a lot of argument about as a matter of fact, in the second book I wrote, I have a whole chapter on it and and there's a lot of great debate about what works best. My opinion is a chief analytic officer should roll into the office of the CEO. I think that's the best place to have impact. It's the best place to get stakeholder sponsorship for large, disruptive projects. And back to your question about where do you start? If you've started and you've seen success, you start to look for bigger things. And when you get two big things, it's one thing to apply data science in the finance department and get some great insights and drive your business. It's another thing to disrupt large processes across your organization and an order to do that it always requires the high gas level of sponsorship. So I think it works best into the office of the CEO. However, I will tell you some of our greatest and best customers in the folks that I meet in the field also have it going into the office of the seeio and there's great reasons for that right. So yeah, it goes both ways, but my pick would be cee it's a critical business factor too. When you think about it, data is everything running the organization. Why shouldn't report into the CEO and made? A lot of companies see it as a differentiating business asset these days. So you know it's you and I been in this market a long time and and we've seen some of the funny wars that have gone on. I remember for years how software companies were doing battle around things like data quality and MDM and the things that were inherent about leveraging your data in a smart way. And it's all come home again every you know what's that saying? Everything old is new again today, against I said with I sit with customers and they go all so, we have a data quality problem, and you kind of go, you know so, and you know from...

...where I am what we try to help customers with, and I think it's important, is connecting to all your data has become paramount, and again, it plays a huge role there, unifying all of your data, making it practical to utilize and putting the right data in the right place at the right time. That's super important and we see a lot of companies with needs there. And then the analytic side of the predicting against it and you'll lizing it aspects. So all three of those pieces or pillars have become super important for any company that's trying to do cool stuff with data science and data in general. Yeah, and then the next stage is fealy predicted analytics. That comes in after that. Yeah, it's going to happen. So we have a lot of enterprise architects that listen to the show. And what are the concerns that the enterprise architect has to be involved with when it comes to data, because you know, it's not just you know. What do we keep on crime? Where we put on the cloud? Is was a data owner right? Where do we store that? WHO has access to that data? How is that data being used by all the applications I have within my text acc all this stuff. So maybe you can just give to be your thoughts on that and would be great. Yeah, you know, years ago having data kingdoms or fivedoms was a pretty normal thing. I mean it was, oh Geez, you know that whole data set is owned by sales or that sown by this company. You know the disruption that we all saw economically quite some time back in the two thousand and seven to nine period a lot of companies struggled and didn't come out the other end of that downturn in economics. But the ones who did, many of them would say that what helped keep them afloat or gave that extra two or three percent was insight into data and the ability to make decisions that their competitors couldn't. And what it did is, I think it caused a bit of a culture shift. So, getting to your question about what the roles are like, when you ask the question, I thought partner, partner, partner, because you can't have an enterprise why data science or analytic practice without the expertise, the subject matter expertise, the...

...domain expertise in the partnering of anybody in the Enterprise Architecture involved in your company. And that's why I think these centers of excellence works so while, because these folks have a seat at the table and it's a different seat than they had years and years ago. It's not as service seat anymore it was back in the day. You know, hey, hey, you guys, we need that table or these databases, so make it happen. Now it's a partnership and a lot of really great insightful projects that we see our customers attacking to optimize their business, to optimize supply chain or customer interaction is actually being driven by really smart it people at these tables because they have an all encompassing view of the applications and the software and the data that exists and they're actually better at most people come to the table and say we see some synergies here and we think that we could make better decisions or automated process and so on. So I think it's evolved quite a bit, but it's gone from service to partner to stakeholder in a really short period of time and I think the smartest businesses will look at it and enterprise architecture as a critical requirement to success for these centers of excellence. And if I'm brought in to help a customer understand how to do that and I don't see the right representation for the data, of the databases, the applications the rest of the business, that's usually one of the red flags will raise immediately you don't have the right people in the room. And and enterprise architecture has to be represented but also has to be influential in that process and some of the best companies, the guys that are really really breaking new ground using analytics as a backbone in their applications. It's because they're doing it with this synergy. Does that make sense? It makes total sense. We're seeing the same thing too. You know, the the Aa guys were always seen as very ivory tower, the type of position...

...like it was. You know, it had all these weird names like togaff and you know all these models that they use it and you know, it was more like the EA was just isolated from the rest of the organization and transition that we're seeing right now is that the EA is actually driving a lot of digital transformation, is actually enabling the different lines of businesses to work together with them to add technology and technology applications to better run their businesses. So we're definitely seeing that more and more. We're also seeing a trend that's going from, you know, project based to more product based type of it. Right there's no more just setting up a CRM or EARP system. Now it's more like what can we bills? You know, that's going to act as a solution for particular business problem. So all the sudden, I t nations are becoming software companies and the success was that we see are the ones that are running them like. So, yeah, yeah, no, it's you see those examples everywhere. And and I not only is it building, you know, software or applications for their organizations and for their customers, and that's definitely a new and emboldened sort of thing, but they also have a new sense of agility. Going back back to my stone age days, I used to write articles about how terrible it was that it took six months to change the dimension in a data workhouse, you know, and that didn't happen much anymore. And you've got to have your act together and it brings this new sort of level of agile thinking and speed to the game, and that speed is what drives the ability to scale. And you know, you mentioned solving their own problems. I mean, let's face it, it's two thousand and twenty. It's been a weird gear in a lot of companies are solving problems with this new agility, the ability to build things within their organizations, and a lot of it's coming from the it are, because if you can't pivot right now, you're losing pace a lot faster than you know. Your competitors might be. So, yeah, it's I totally agree with you. I like that approach. It's one that my company is helping our customers with.

We call it a connected experience. You know, you need to have access to things like, you know, real time data. I think I mentioned any or all data at the beginning of the conversation, but a but that's this connected experience to move forward and bring all of these parts and pieces together in an angile way. My company responded this year with, and I don't normally talk about our products, but this one we made free because of covid and it's called gather smart and it helps companies bring their employees back to work. We built it in under six weeks and we used our technology to do exactly what you just said, is to create something from within our company that we knew customers needed, and that's what our customers are doing as well. So, yeah, it's kind of a new world that way, isn't yeah, it's changed. It's change for the better. Yeah. Well, thank you so much for joining us today, Sean. It was really great speaking with the learning more about your thoughts and views and the industry. And, you know, I think our listeners will really have enjoyed this particular episode, so thank you well. Thank you. It was great to be here. I enjoyed the conversation terrific. You've been listening to unleash. I T to ensure that you never miss an episode. Subscribe to the show in your favorite podcast player. If you'd like to learn more about enterprise architecture and tools to help unleash your businesses digital capabilities, visit lean ix dotnet. Thank you so much for listening. Until next time,.

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