>> Announcer: Live, from Orlando Florida It's theCUBE! Covering Pentaho World 2017. Brought to you by Hitachi Ventara. >> Welcome back to theCUBE's live coverage of Pentaho World brought to you by Hitachi Ventara I'm your host Rebecca Knight along with my co-host Dave Vellante, we're joined by Brendan Aldrich he is the chief data officer at Ivy Tech which is Indiana's community college system Thanks so much for joining us. >> Thank you very much I appreciate it. >> And congratulations because I know that you've just won the Pentaho Excellence Award for the Social Impact category. At Ivy Tech you are you using the power of data to combat one of the toughest problems in education higher education drop out rate so tell us a little bit about what you're doing and how you're using data. >> Certainly, well Ivy Tech has been really one of the more innovative players in the higher education space when it comes to how we're utilizing data. Both from the work that data engineering and our chief technology officer has done to the work we're doing now from my area to make that data very useful and very usable for the organization. And we're tackling it on multiple fronts. We're using data in order to help more quickly identify students that have already completed the requirements to graduate. Or if they are close to or have already potentially completed the requirements to graduate on another major other than their declared major and starting those conversations with the students. >> And what about the drop out too so you are obviously also looking at students that are at risk. >> We've been engaged in a project called Project Early Success where we work in the first two weeks of a 16 week term to identify which students we believe are at risk for failure. And then we spend the next two weeks, weeks 3 and 4 of the term coordinating hundreds of faculty staff and administrators to reach out and try to talk to those students and see if we can move them back on track. The first term that we did that we saw a great success with, we, by mid-term were showing a 3.3 percentage point drop in our number of D's and F's being reported. For an organization our size, that meant over 3000 students, more student, who were passing their courses at mid-term as compared to failing them, compared to the year before. >> Scope of the organization? Student size? >> Ivy Tech, we are Indiana state wide community college system so we have 19 campuses, almost 9000 employees and we educate around 160 000 students per year. >> Wow. So just getting back to that college drop out, so professors are putting in the data about who's going to class, who's not going to class >> Brendan: That's right. >> The grades that their getting. And then that's all being fed in and you're finding out who the at risk people are, and it's really just reaching out to them and it's saying "Hey, what's going on?" >> Absolutely. And in fact a lot of the work was done with our engineering team to actually identify data that related to the behaviors of the students. So it's not just their attendance it's not just previous performance in similar classes. But it's really finding those data elements that relate to behaviors of the students that we believe are going to put them on a less successful track. >> Brendan I wonder if we can talk about the role of the Chief Data Officer. When we talk to CDO's in for profit organizations they always say we start with an understanding of how data can help with our monetization strategies. Now let's translate that for a community college. Is that a reasonable starting point if I frame it as how data adds value to the organization is that where you started and take us through sort of the journey of your role. >> Absolutely. Well first of all Chief Data Officers in higher education are still fairly rare. At the time Ivy Tech hired me in December of 2015 I was only the 9th Chief Data Officer working at any college or university in the country. And the first that had been appointed at a two year college. So whereas a public institution like ours is not necessarily as driven by profitability students success is something that's very high on our priority list and being sure that we were able to make data very available to everyone in the organization that was working with our students so that they could use that data to more directly target the areas that they could help the student best. Now there can be profitability components as a public institution we do receive funds from the state, performance funding for students who successfully graduate. In some ways we've been able to use data to help our registrars identify those students more quickly. Which certainly gives us a lot of opportunity not only to help the students on their own educational goals and careers but to be able to increase the amount of performance funding that Ivy Tech receives from the state as well. >> So that you brought to the other point CDO's tell us is data access, making that data accessible. And then there's a trust component too. It's got to be reliable and it's hard with all this data and all this data growth is how are you addressing kind of those challenges? >> One of the things that's really unique about how we're approaching data at Ivy Tech is this idea of a data democracy. It's more than self-service business intelligence or self-service analytics. Because instead of just providing access we wanted to make sure that once our employees had access, that the data was intuitive. That it was relevant to their responsibilities. That it was interactive. So that as their needs and challenges and questions evolved they could continue to use data to answer those questions without having to go back to a central IT team or a central research team. So the data democracy is a really unique aspect of ours that was important to us and I think at the moment we have about 4000 of our employees trained and running on our platform today. >> So everybody wants to be data driven these days your job is to actually affect that data driven initiative. Culturally, people say they're data driven but they don't necessarily act that way. They still act on gut feel and this is the way we've always done it. How have you been able to affect the cultural transformation? >> Well it's important to remember that if you can make the right data available to the people who are ready to use it, that's a transformational opportunity. For us, before we began on this project less than 2% of our employee base actually had the ability to create a report. Everyone else had to make requests wait for data to be made available it could take time and maybe that data wasn't available by the time they actually needed it. So if you think about that, moving from a place where less than 2% of our employees had access to data to a point where we're approaching 50% of our employees now having really good access to data we didn't want just a few silver bullets we feel that every one of our employees has the potential, if they have the right data available to test their ideas with data and come up with brand new, innovative ideas. So we could have thousands of silver bullets coming to rise throughout our organization. >> So give us some examples, I mean we've talked a little bit about how the data is transforming the student experience and student success rate but how, what are some of your grand ideas about how faculty and how employees can use data to test ideas and make their lives easier and make Ivy Tech more successful. >> Oh absolutely. And even if you think about Project Early Success and the idea that we were helping to identify students that we believe may be struggling behaviorally in being successful in their courses. Now if you can take that as an attribute and you can surface it through our system to the employees that are using it which includes our faculty. Our faculty members now have the ability to see very quickly which of their students may be struggling and have the chance to intervene with those students as well on a regular basis. So it's not just one phone call at the beginning of the term. It's not just Project Early Success but now what we're talking about as Project Student Success how do we continue to use that kind of information to engage the student over the entire course of the term to ensure that we're not just changing their trajectory a little bit in the beginning but that we're following that journey with them over the course of their educational goal. >> Can you talk about the regime in your organization? The reporting structure, to whom do you report is there a CIO- >> Brendan: There is. >> What's the relationship there? >> There is a CIO who I report to the Chief Technology Officer and I both report to the CIO and we had a recent change in our leadership within the organization as well. Back a year ago this last July we have a new president of the state wide organization Dr. Sue Ellspermann who was formerly our lieutenant governor for the state of Indiana. >> So that's interesting that you report to the CIO. Most Chief Data Officers, we find, I wonder if you can comment don't report to the CIO there's sort of a parallel organization for a variety of reasons. People generally believe that well, it maybe one day was the CIO's job it's sort of the CIO's job morphed into kind of keeping the lights on and the infrastructure going, but what do you see amongst your colleagues with that regard? >> You know what's important for me and I think that if you look at every organization across the country there is this data knowledge gap. This idea that you've got your IT and engineering staff that knows everything there is about how to build, support, augment and de-commission these systems but generally have not been as involved in what the data means inside those systems or what decisions are being made off that data. On the other half of that gap you've got all of the rest of your organization the people that are using data who know what it means and who are making decisions from it but generally don't know enough about how to think about structuring that data so that they could get the engineering teams to build them new tools. This is really the place where a Chief Data Officer in my mind comes to sit. Because my goal is to build those bridges between the organization so that we can help engineering learn more about what we're doing as an organization with data and then use that information to build tools that will drive the rest of the organization closer to those goals through data. >> Now you're not a bank so you've got, I'm imagining a pretty small team. >> Brendan: We do. >> So maybe you can talk about that and how you manage with such a small team. >> You know it's interesting most organizations when you think about a build versus buy scenario you think about well I don't have a lot of people I don't have a lot of bandwiths, maybe we need to buy. Now Ivy Tech went through that process and every one of the RP's that came back were too expensive We couldn't afford to do it. So as a team we had to sit down and think about how do we really rethink the way that we approach this in order to still accomplish what we need out of data and out of our data warehouse and analytic systems. Part of what I'll be speaking at the conference today is some of those entrenched data practices that we had to overcome or rethink and rewrite in order to get to where we are today. >> Well Brendan it's been so much fun having you on theCUBE, thanks so much. >> Well thank you, I appreciate it. >> I'm Rebecca Knight for Dave Vellante you are watching theCUBE, we will have more from Pentaho World in just a little bit. (electronic music)

>>from around the globe. It's the Cube with digital coverage of AWS reinvent 2020 Special coverage sponsored by AWS Global Partner Network. Right. Welcome back to the cubes. Live coverage of AWS reinvent 2020 were virtual this year. We're not in person. We have to do it remote but the Cuba's virtual And I'm John for your host here with Cube Virtual next guest, Sarah Cooper, who is the general manager of the i o T Solutions with a W s. Sarah. Great to see you. Eso you last year in person. In real life, now we're remote. But thanks for coming on. Thank you. >>Thanks, John. Always good to be on the Cube and great to see you again. I don't know how many years it's been from our initial meeting, but it's been a few. >>Well, we gotta we gotta cube search engine. You were on in 2016, but we saw each other last year on when we're riffing on the i o t. News. A lot of great stuff. I mean, from Speed Racer all the way down through all the industrial stuff. Even more this year. But two things that jumped out at me this year. War is the carrier keynote and also the BlackBerry kind of automotive thing again speaks to kind of two megatrends. Obviously, automotive will get to a second, but the carrier announcement was really interesting. You guys did this thing and I was so impressed with the cold chain, uh, product. It was the connected cold chain. It was called, Um, this is where the carrier, which is known for air conditioning This is critical I o t devices that stays with the vaccines involved. Take a minute to explain what the cold chain connected cold chain project waas. >>Yeah, absolutely. So. So we worked closely and are working closely with Carrier on on a product called Links Now Cold chain. Um, as Dave Gitlin, the CEO of Carrier, described in Andy's keynote eyes about moving perishable goods, things that need certain temperature ranges from point A to point B and that usually it sounds simple. Uh, that's not quite so simple. It's usually you know, least you know, 5 to 25 hops, sometimes as much as 40. Andi zehr these air partial goods This is food. This is medicines. This is vaccines. Very hot topic at the moment. And today you know you're moving between ships and those big tractor trailers, and you've got warehouses with refrigeration units and you've got retail grocery stores with refrigeration units thes air, all different data sources that are owned by different. You know, members of that supply chain that value chain and to end. And so what links does is it pulls the data from all of the curier equipment and then pulls that data and looks across all of this information, using things like machine learning to draw inference and relationship and then be allows us to be able to make smart recommendations on things like routes. Or, if you know, a particular produce might need to stop before its original event to make sure it's got long shelf life. It allows us basically to provide that transparency and toe end, which is so difficult because of the number of players. And it's in part due to curious breath of products. And then, you know, with AWS, we're bringing the digital technology side. We got the i o t. The M l. A lot of big data processing pieces, eh? So we're really excited about that. I have to say It's one of the easiest projects to hire for when you talk about making sure that we're able to reduce food waste from the current 30 to 40% or that we're working on making sure that vaccines are efficacious by the time that they get a vaccination site, engineers sign up pretty quickly. >>You know the cliche. You know, mission driven companies. They're always kind of like people love the work for mission driven companies. In this case, you have a project and group that literally is changing the world. If you think about just the life savings on the on the on the vaccine side, that's obvious. We all can relate to that now with covert on full display. But just in terms of energy consumption, on food, ways to perishables if you get the costs involved to society, hunger around the world. Uh, just >>food is >>just wasted, and there are people starving, right? So when you start looking at this as an instrumentation problem, right, it gets really interesting. So you mentioned supply chain value chain. This is I o t potentially, even Blockchain again. This is a key change. The world area. You guys have a multi year deal with Carrier, So validation. What does that mean? Specifically, you guys gonna provide cloud services? Um, what's that all mean? >>Yeah. So we were bringing our engineering talent as this carrier. This is a code development, so we're actually jointly developing together. They bring a lot of the domain expertise they bring, you know, years and years of experience in refrigeration, Um, and in, you know, track and trace of these products. And we bring engineers who have vast experience at scale in these kinds of inference, challenges and and data management and data quality. And so it's really kind of bringing the best of both worlds. And you see this happening more and more. I think in general, where you've got a company like AWS that has strong digital expertise and a history of product innovation, working with customers that are very innovative themselves, but typically have been innovative in in, you know, traditional hardware products and the two worlds coming together to make sure that we can really solve some of the big challenges that are facing our society today. And, um, again, you know, it's great to wake up in the morning and get to work on a project that has that kind of impact. >>Well, before we move on to the whole BlackBerry automotive thing, which is another whole fascinating thing share something that people might not know about this carrier project. That's important. Um, whether it's something anecdotal, something that you know, Um, that's important. What, what what's what's What else is there that's game changing that you think is important to point out? >>Yeah, you know, I don't know that when we first started working with Carrier on on scoping this project that I had really thought through all the different players that are touched by cold chain. Um, certainly we've got a number of them within Amazon with our our fulfillment technologies and our grocery stores. That that's logical. Um, you think about the shippers and people who are out, you know, um, farming. And you know, I mean, crabmeat is something that moves in these big refrigerated containers, but actually there's there are transportation companies. There's drivers of these big rigs that need to make sure that they're being that they have fuel consumption management. You've got customers, you know, really kind of throughout that piece, freight forwarders. And so really the breath of the people that are touched, not just you and I is consumers of of perishable goods and fruits and produce on DNA medicines, but also really, that full end to end ecosystem on that's That's both the exciting part from A from a business standpoint, but also the exciting part from the technology stand. >>Well, it's great work, and I applaud you for it's one of those things where foodways isn't just a supply chain impacts the rest of the world because you're more efficient. You could distribute food, toe other places where people are hungry and just its overall impact is huge trickle effect. So impact is huge. Okay, now let's talk about the automotive peace. Because last year we had on the Cube folks from BlackBerry and remember them came on like BlackBerry. Isn't that the phone that went extinct by the iPhone? No, no. There's a whole nother io ti automotive thing around. Ivy Ivy? Why intelligent vehicle data platform? You guys just announced a multiyear agreement with them to develop that product combined with some of the I O. T and machine learning. Could you take him in to explain what this relationship is. What does it mean? What does it mean for the industry? >>Yeah, it's It's similar to the carrier relationship. You know we are. We're engineering together. Um, in this instance Q and X, which is a division of BlackBerry, is in 175 million vehicles. I mean, just think about that. They're running under the covers, and they are. They are a safety security layer and a real time operating system. So you know, when you think about all of the products, really end end in Q and X isn't just in automotives. It's in nuclear power plants. It's in manufacturing automation. It's one of those products that that you probably benefit from, but you didn't know it. Um, and in the automotive space, it's the piece that manages the safety certified layers of data coming off of sensors in the car. And so, fundamentally, what we're doing with Ivy is we're up leveling that information today. If you think about a car, you've got 1500 suppliers that are all providing parts into that far, which means that different makes and models have different seats. Sensors to give you wait in the back, you know, seat as an example. And so if do you want to write an application that tries to determine if that weight in the back seat is your dog or not, my dog happens to be bothering me at the moment. Z. >>That's one of the benefits of working at home. You know? >>Absolutely. So we'll use him as an excuse here. But if you want to know if that's a dog on the back seat, um, being able Thio, then figure out the PC electric measurements and the algorithms, um means you have to know what sensors air in that back seat, which means you got to write essentially an application Pir sensor manufacturer for vehicle make and model That doesn't work so fundamentally What Ivy does, is it? It abstracts away the differences between the vendors and then it up levels information by using machine learning and analytics running in the car. To be able to allow a developer to say, you know, a P I. Is there a dog in the car like How simple is that? I don't have to figure out what the weight measurement is. I don't know. I have to know if there's cameras in the car or if there's some other way to know. If the dog I just need to ask, Is there dog in the car? And the A P. I, for my view, will tell you yes, No, or I don't know, you know, because sometimes there isn't the technology to know that. And then the application developer can then use that information to build delightful experiences, things that make your dog behave, hopefully, things that might help protect them on a hot day. Um, you know, in things where you know that if there's a child in the car, you don't play explicit lyrics. If they're fighting in the back seat, you make sure that the cartoons go off until they behave themselves and cartoons come back on. There are lots of in vehicle experiences that can be enabled by this as well as vehicle operations. So, you know, being able to do >>yeah and all that stuff. >>Yeah, Selective recalls making sure that Onley cars that are actually affected need to come in and making sure that that you know, that's that's quantified and that, you know, it is actually safe to drive to the point of recall. All of that could be done on a vehicle by vehicle basis. >>So are you competing with car companies now? >>No, fundamentally, the oe EMS are the Are the companies that that the car manufacturers are those that end up delivering this capability and they own the data. You know, this isn't something where BlackBerry or A W S owns the data the auto manufacturers dio so it's there platforms to make a delightful experience out of, um, we're just helping to make sure that that's as easy as possible and opening up. You know, the potential innovation so that it's, you know, it's certainly their developers internally. But if they want take advantage of the millions of AWS developers now, they could do that. >>Sarah, Great to have you on one of the things. I just want a final questions or final point. Let's get your reaction to Is that it seems to me with the cloud in this post covert scale error when you start to get into edge, um, you know, industrial I o t. You hear things like instrumentation supply chain, these air buzzwords, these air kind of characteristics all kind of in play. But the other observation is partnerships, arm or co engineering. Co development vibe. Is that just unique? Thio what you're doing? Or do you see this as kind of as a template for partnering? Because when you start to get these abstraction layers, the heavy lifting can be under the covers. You have this enablement model. What's your quick take on this? >>Yeah, I think we talk about undifferentiated heavy lifting, a lot of Amazon on defunding mentally. That's different for each industry. And he talked about that. His keynote. And so I think you know you'll see more and more co development and co engineering coming from from companies across when we have big technical challenges and these air complex problems to solve it takes a village >>awesome. Sarah Cooper Thanks for coming on GM of Iot. TIF Solutions A. The best to great success stories. The carrier and Blackberry, one Automotive with Black Braids operating system that powers the safety and for cars and, hopefully, future of application, development and carrier, with the cold connected chain delivering perishable goods, vaccines and food. Changing the game. That's a game changer. Thanks for coming on. >>Thanks, John appreciate. Always good to see you. >>Okay. Cube coverage. Jump shot for your host. Stay with us from or coverage throughout the day and all next couple weeks. Thanks for watching. Yeah. Mhm.