>> Live from Boston, Massachusetts, it's the CUBE covering IBM Chief Data Officer Summit. Brought to you by IBM. >> Welcome back to the CUBE's live coverage of the IBM CDO Strategy Summit here in Boston, Massachusetts. I'm your host, Rebecca Knight, along with my co-host Dave Vellante. We're joined by Mark Lack. He is the Strategy Analytics and Business Intelligence Manager at Mueller Inc. Thanks so much for joining us, Mark. >> Thank you for the invite. >> So why don't you tell our viewers a little bit about Mueller and about what you do there. >> Sure, Mueller Inc. is based in the southwest. Ballinger, Texas, to be specific. And, I don't expect anybody, unless they Google it right now, would be able to find that city. But that's where our corporate headquarters and our main manufacturing plant has been. And, we are a company that manufactures and retails steel building products. So, if you think of a warehouse, or a backyard building or even a metal roof, or even I was looking downtown, or downstairs, earlier today, this building is made out of big steel girders. We take those and form them into a product that a customer can use for storage or for living or for any of whatever their use happens to be. Typically, it might be agricultural, but you also find it in very, very large buildings. Mueller is a retailer that happens to manufacture its products. Now, that's a very important distinction, because the company, up until about 15, 20 years ago, viewed itself as a manufacturer that just happened to retail its products. And so when you take the change in the emphasis, your business changes. The way you approach your customers, the way you approach your products, the way you market yourself, is completely different from one side to the other. We've been in business since 1930s, been around for a very long time. It's a family owned business that has it's culture and it's success rooted in West Texas. We have 40 locations all over the southwest. We're headquartered in Ballinger, Texas. We're as far east as Oak Grove, Louisiana and as far west with locations as Albuquerque, New Mexico. >> So you do cognitive analytics for Mueller, so tell our viewers a little bit about what you do there. >> Sure. Mueller has always been on the forefront of technology. Not for technology's sake, but really for effectiveness and efficiency's sake. So Mueller did business process reengineering when it was common for much larger organizations to do. But Mueller took it under as the reality for us to manage our business in the future. We need to have the professional tools to be able to do this. So we set on in our industry using technology in novel ways that our competition just doesn't do. So with the implementation of technology, what you have is a lot of data that comes along. And so we've been very effective using it for our balance scorecard to report metrics and keep the organization on track with that. Giving information back to various parts of the organization and then also creating an analytics platform and program that allows us to really dive deep into the organization and the data and everything that's being thrown off from modern technology. So cognitive analytics. This is something, as you hear about in technology today is, from the robots to artificial intelligence. Cognitive analytics, I think is for us a better way of looking at it of augmented intelligence. We have all of this data, we have these wonderful systems that help give us information to give us the answers we need on our business processes. We have some predictive analytics that help us to identify the challenges going ahead. What we don't have is the deep dive into using these technologies of cognitive to take all of this big data and find answers to situations that it would take a hundred people a hundred years to find out to be able to mine through. So the cognitive analytics is our new direction of analytics, and to be honest with you it's really the natural progression from our traditional analytic system. So as I said before, we have the regular analytics, we have the predictive analytics. As we get into cognitive, this is the next generation of how do we take this data that we have, that's coming at a volume and a velocity and a variety that is so difficult to look at as it is in a spreadsheet, and offload this onto system that can help us to interpret, give us some answers that we can then judge and then make decisions from. >> So, as you said, you have a lot of data. You got customer data, you got supply chain data, you got product data, you got sales data, retail location data. What's the data architecture look like? I mean, some data is more important than other data. How did you approach this opportunity? >> So, a few years ago I went to the first World of Watson, which was in New York. There was about a thousand attendees and Ginni Rometty had had this great presentation and it was very inspiring and she asked, "What will you do with Watson?" And at the time I had no idea what we were going to do with Watson, and so I sat on the plane on the way back and I thought through what are the business case scenarios that we can use to use artificial intelligence in a steel building company in Ballinger, Texas. Don't forget the irony of that part. As we're going to to go back to start using cognitive. So I thought through this and I went to our owner and we had many, many conversations on cognitive. You had the jeopardy, the Watson championship and you started thinking about all of these systems. But the real question was how could we take a new technology and apply it to our existing business to make a difference? And I'm getting to the answer to your question on how it got structured. So we went down the path of investigating Watson, and we've realized that the cognitive is part of our future. And so we plan on leveraging cognitive in many ways. We'd like to see it sales effectiveness, operations effectiveness, transportation effectiveness. There are all sorts of great ideas that we have. One of the challenges we have, and the reason I'm here at the CDO Summit, is when we start to look at our data, the question is are we cognitive ready? And I'll be honest to you, we are for today for a sliver of what cognitive capability is. As you've always heard the numbers 80% of your data is in unstructured format. So we have lots and lots of unstructured data. We have a lot of structured data. When it comes to the analytics around our structured data, we're pretty good, but when you start talking about unstructured data, how do we now take this to add to our structured data and then have a more complete picture of the problem that we're searching? So what I'm hoping to gain here at the CDO Summit is talking to some of these world-class leaders in data operations and data management to help understand what their pain points were. Learn from them so I can take that back and help to architect what our needs are so that we can take advantage of this entire cognitive future that's... >> So you're precognitive. So cognitive ready, let's unpack that a little bit. That means, that what you've got a level of confidence in the data quality? You've got an understanding of how to secure it, govern it, who gets access to it? What does that mean, being cognitive ready? >> So it's going to to be all of those. All of the above. First is, do you have the data? And we all have data, whether it's in spreedsheet on our systems, whether it's in our mobile phone, whether it's on our websites, whether it's in our EIP systems, and I can keep going on >> You got data. >> We have data, but the question is, do we have access to the data? And if you talk to some people, well sure, we have access to the data. Just tell me what data you want and I'll get you access. Okay, well, that is one answer to a much larger problem, because that's only going to give you what your asking for. What the cognitive future is promising for us is we may not know the questions to ask. I think that's the difference between traditional analytics and then the cognitive analytics. One of the benefits of cognitive will be the fact that cognitive will give answers to questions that we're never asked. And so now that this happens, what do we do with it? You know, when we start thinking about having attacking a problem, you know, being data ready, having the data there, that's part of the problem. And I think most companies say we're pretty good with our data. But with the 80% that we don't have access to, the real question is, are we missing that crucial piece of information that prevents us from making the right decision at the right time? And so our approach, and what I'm going to go back with, is understanding the data architecture that those who have gone before me that I can pick up and bring back to my organization and help us to implement that in a way that will make it cognitive ready for the future. You know, it's not just the access to the data; it's having the data. And I had lunch a few years ago with Steve Mills who was a senior executive for IBM, and one of the people at lunch was bold enough to ask him, "How do we know what data to capture?" And he said, very bluntly, "All of it." Now this was about five years ago. So, back then, you're shaking your heads saying, "We don't have storage capabilities. "We don't have the ability to store all these data." But he had already seen the future, and what he was telling us right then was all of it is going to be valuable. So where we are today, we think we know what data's valuable. But cognitive's going to help us to understand what other data might me valuable as well. >> So I'm interested in your job from the perspective of the organizational change. And you work for, as you said, a small family-owned company. Smallish of family-owned company. And we've heard a lot of today about the business transformation, the technology involved, and how that has really changed dramatically over the last decade. But then, there's also this other piece which is the social and cultural change within these organizations. Can you describe your experience in terms of how your colleagues interpret your world? >> You're asking me those questions 'cause you can see the bruises from whatever I have to accomplish. (laughter) You know, within an organization, one of the benefits of working that I found at Muller, and it's a family organization, is that those who work there, and I've been there for 18 years, and I'm still considered a newcomer to the organization right after 18 years. But we're not there unless we have a strong commitment to the organization and to the culture of the company. So, while we may not always agree as to what the future needs to hold, okay? We all understand we need to do what's best for this company for its long term survival. At the end of the day, that's what we're there to do. So culturally, when you first come up with saying you're going to do artificial intelligence, you know, you got a lot of head-scratching, especially in West Texas. I have a hard time explaining even to those around me what it is that I do. But, once you start telling the story that we have data, we have lots of data, and that there might be information in that data that we don't know now but in the future we may have, and so, it's important for us to capture that data and store it. Whether or not we know that there's immediate value, we know there's some value, okay? And if we can take that leap that there's going to be some value, and we're here with the help of the organization faces, we know that there are challenges to every organization. We're a still building company in Ballinger, Texas. Now I know I keep saying that, but what if a company like Uber comes up with metal building and all of a sudden, we have new challenges that we never thought we'd face? Many organizations that have been up, industries that have been in upheaval from these changes in either technology access or a new idea that splits the difference. We want to make sure we can stay ahead, and so when we start talking about that from a culture, we're here for the long term value of the company. We're committed to this organization, so what it do we need to do? And so, you know, the term "out of the box thinking" is something that sometimes we have to do. That doesn't mean it's easy. It doesn't mean that we all immediately say, "Aha! This is what we're going to do." It takes convincing. It takes a lot of conversation, and it takes a lot of political capital to show that what it is that we're going to do is going to make sense and use a lot of good examples. >> Well, and you come to tongue-in-cheek about people rolling their eyes about AI and so forth, but any manufacturer who sees 3D printing and the way it's evolved goes "Wow!" And then the data that you can capture from that, so, I wanted to ask you, when you talk to your colleagues and people are afraid that robots are going to take over the world and so forth, but what are the things that when you think about augmented intelligence that, you know, where do the machines leave off and the humans pick up? What kinds of things do humans do in your world that machines don't do that well? >> So, you know, if I go back and think about analytics, for example, there's a lot of time collecting data, storing data, translating data, creating contract to retrieve that data, putting that data into a beautiful report and then handing it out. Think of all that time that it takes to get there, right? A lot of people who are in analytics think that they're adding value by doing it. But to be honest with you, they're not. There's no value in the construct. And so, what the value is in the interpretation of that data. So what do computers do well and what do we do well? We do well at interpreting what those findings tell us. If we can offload those transactions back to a machine that can set the data for us, automatically construct the data, put it into a situation for us that can then allow us to then interpret the results? Then we're spending the majority of our time adding value by interpreting and making changes with the company versus spending that same time going back and constructing something that may or may not be something that may add value. So we spend 80% of our time creating data for a report. The report, now we have to test the report to determine, can I communicate this the right way? You have machine learning now and you have tools that will then take this data and say, "Oh, this is numerical data. "This looks like general ledger data. This is the type of way this data should be displayed." So I don't have to think of a graph. It suggests one for me. So what it does is then allow me to interpret the results, not worry about the construct. >> So you can focus on the things that humans do well. But the other thing I want to talk to you about is the talent issue. I mean you guys, you've mentioned before that you're based in West Texas and you are working on a real vanguard in your industry. As I said, you were someone who is thinking about whether or not Uber is going to say, "Let's make steel buildings." I mean, is that a problem that you're facing, that your company is facing? >> Well, there is no joke, right, that the fact of the future's going to have a man and a dog. And the man's job is to feed the dog, and the dog's job is to bite the man if he tries to touch any of the machinery, right? So, I don't think that we're there. The jobs aren't going to be eliminated to where people are not able to add value. But finding a talent, back to your question, is the expectation that we have of talent, it is scarce. Finding people that have the skills to now interpret the data, so you can find people that have a lot of time that can do any of those steps in between. But now, what's happened is, you want people to add value, not create constructs that don't add the value. So the type of talent that you look for are people who can interpret this information to give us the better answers that we need for the organization to thrive. And that's really where I see the talent shifting is on more forward-looking, outcome-based, value-based decision making, not as much on the development of items that could be offloaded to a machine. >> Yeah, I mean, interpretation, creativity, ideation. I mean, machines have always replaced humans. We've talked about this on The Cube before, but the first time in human history, machines are replacing humans in cognitive functions. I mean, you gave an example of the workflow of developing a report, which... >> Kenney Company can relate to, yeah. >> But yeah, 10 years ago, that was like super valuable. Today it's like, "Let's automate that." >> Well, but the challenge I think where people have is where do they add value? What is the problem that we're trying to solve? It's where do we add value. If we add value creating the construct, you aren't going to be employed, because something else is going to do that. >> But if you add value on focusing on the output and being able to interpret that output in a way that adds value to your company, you'll be employed forever. So, you know, people that can solve problems, take the information, make decisions, make suggestions that are going to make the company better, will always be employed. But it's the people who think they add value flipping a switch or programming a lever, now, they think their value's very important there, but I think what we have to do and it behooves us, is to translate those jobs into where do you add value? Where is the most important thing you need to be doing for the success of this company? And that I think is really the future. >> Are you... We haven't asked any IoT questions today. I want to ask you, are you sort of digitizing, instrumenting for your customers the end products of what you guys produce, and how was that creating data? >> You know, we haven't, we talked about it. We don't have products that, we're not selling things that are machinery that might break down and give us information, and so, we're building final products that are there, that people will then do different things with. So, IoT hasn't worked for us from a product standpoint, but we are looking at our various machinery and making sure that we have understanding as to those events that are causing a break down. One of the challenges we have in our industry is if we have a line that manufactures apart, if it goes down, okay, now it shuts everything down. So we have a duplicate, which can get very expensive. We have duplicates of everything, and how many duplicates do you need to have to make sure you have duplicates of the duplicates? So if we can start to look at the state of this coming from our machinery, and use that as a predictor, then we can use that, and so you have sort of an IoT thing there by looking at the data that's there. But is it feeding back into our normal reporting systems? It's not necessarily like it is from a smartphone are enabled like that. >> No, but it's anticipating a potential outage. >> Sure. >> And avoiding that. Yeah, great. >> Well Mark, thanks so much for coming on The Cube. It was wonderful conversation. >> Thank you. >> I'm Rebecca Knight with Dave Vellante. We will have more from the CDO Summit just after this. (upbeat music)