>> live from San Francisco, California It's the Q covering the IBM Chief Data Officer Summit brought to you by IBM. >> We're back. You're watching the Cube, the leader in life Tech coverage. My name is Dave Volant Day, and we're covering the IBM Chief Data officer event hashtag IBM CDO is the 10th year that IBM has been running This event on the New Cube has been covering this for the last I'd say four or five years. Beth rottenness here. She's the distinguished engineer and principal data scientist. Cognitive within GTS Large Service's organization within IBM. Bet thanks so much for coming on the Cube. >> Absolutely. Thank you for having me. >> So you're very welcome. So really interesting sort of title. I'm inferring a lot. Um, and you're sexually transforming lives through data and analytics. Talk about your role a little bit. >> So my role is to infuse workforce transformation with cognitive. I typically we go from I think you've heard the ladder to a I. But as we move up that ladder and we can actually >> apply artificial intelligence and NLP, which is a lot of what I'm doing, >> it is it's instrumental in being able to see human beings in a lot more dimensions. So when we classify humans by a particular job role skill set, we often don't know that they have a passion for things like coding or anything else. And so we're really doing a lot more where we're getting deeper and being able to match your supply and demand in house as well as know when we have a demand for someone. And this person almost meets that demand. Based on all the different dimensionality that weaken dio, >> we can >> put them into this specific training class and then allow them to go through that training class so that we can upgrade the entire upscale and reschedule the entire work force. >> So one of the challenges you're working on is trying to operationalize machine intelligence and obviously starts with that training and skill level so well, it's not easy company the size of of I B M E. You're starting the GTS group, which probably has an affinity, at least conceptually, for transformation. That's what you guys do for your clients. So how's that going? You know, where are you in that journey? >> I think that we're in the journey and we're doing really well. I think that a lot of our people and the people who are actually working on the ground, we're talking to our clients every single day. So people on the helped us, they're talking to clients and customers. They understand what that client is doing. They understand the means, the troops, the mores, the language of the customer, of the organization of the customer, in the client, giving those people skills to understand what they can do better. To help solve our client's problems is really what it's all about. So understanding how we can take all of the unstructured data, all of like the opportunity for understanding what skills those people have on the ground and then being able to match that to the problems that our clients and customers are having. So it's a great opportunity. I think, that I've been in GTS my entire career and being an I t. I think that you understand this is where you store or create or, you know, manage all of the data in an entire enterprise organization, being able to enable and empower the people to be ableto upscale and Reese kill themselves so that they can get access to that so that we can do better for our clients and customers. >> So when you think about operations, folks, you got decades of skills that have built up you. D. B A is, you got network engineers, you got storage administrators. You know the VM add men's, you know, Unix. Add men's, I mean and a lot of those jobs. Air transforming clearly people don't want to invest is much in heavy lifting and infrastructure deployment, right? They want to go up the stack, if you will. So my question is, as you identify opportunities for transformation, I presume it's a lot of the existing workforce that you're transforming. You're not like saying, Okay, guys, you're out. What is gonna go retrain or bring in new people? Gonna retrain existing folks? How's that going? What's their appetite for that? Are they eagerly kind of lining up for this? You could describe that dynamic. >> I think the bits on the ground, they're very hungry. Everyone is so, so, so hungry because they understand what's coming on. They listen to the messages, they're ready. We were also in flexing. I'm sure you've heard of the new collar program were influencing a lot of youth as early professional hires. I have 2 16 year olds in the 17 year old on my team as interns from a P Tech program in Boulder, and getting that mix in that diversity is really all what it's about. We need that diversity of thought. We need that understanding of how we can start to do these things and how people can start to reach for new ways to work. >> All right, so I love this top of the cube we've we've covered, you know, diversity, women in tech. But so let's talk about that a little bit. You just made a statement that you need that diversity. Why is it so important other than it's the right thing to do? What's the what's the business effect of bringing diversity to the table? >> I think that would. We're searching for information truth if you want. If you want to go there, you need a wide variance of thought, the white of your variance, the more standard you're me, and it's actually a mathematical theory. Um, so this is This is something that is part of our truth. We know that diversity of thoughts we've been working. I run and sponsor the LGBT Q Plus group. I do women's groups in the B A R G's and then we also are looking at neuro diversity and really understanding what we can bring in as far as like, a highly diverse workforce. Put them all together, give them the skills to succeed. Make sure that they understand that the client is absolutely the first person that they're looking at in the first person that they're using Those skills on enable them to automate, enable them to stop doing those repeatable tasks. And there's so much application of a I that we can now make accessible so that people understand how to do this at every single level. >> So it's a much wider scope of an observation space. You're sort of purposefully organizing. So you eliminate some of that sampling bias and then getting to the truth. As you say, >> I think that in order to come up with ethical and explainable, aye, aye, there's definitely a way to do this. We know how to do it. It's just hard, and I think that a lot of people want to reach for machine learning or neural nets that spit out the feature without really understanding the context of the data. But a piece of data is an artifact of a human behavior, so you have to trace it all the way back. What process? What person who put it there? Why did they put it there? What was that? When we when we look at really simple things and say, Why are all these tickets classified in this one way? It's because when you observe the human operator, they're choosing the very first thing human behaviors put data in places or human behaviors create machines to put data in places. All of this can be understood if we look at it in a little bit of a different way. >> I thought I had was. So IBM is Business is not about selling ads, so there's no one sent to future appropriate our data to sell advertising. However, if we think about IBM as an internal organism, there's certain incentive structures. There's there's budgets, there's resource is, and so there's always incentives to game the system. And so it sounds like you're trying to identify ways in which you can do the right thing right thing for the business right for people and try to take some of those nuances out of the equation. Is that >> so? From an automation perspectively build digital management system. So all the executives can go in a room and not argue about whose numbers are correct, and they can actually get down to the business of doing business. From the bottoms up perspective, we're enabling the workforce to understand how to do that automation and how to have not only the basic tenets of data management but incorporate that into a digital management system with tertiary and secondary and triangulation and correlations so that we have the evidence and we can show data providence for everything that we're doing. And we have this capability today we're enabling it and operational izing. It really involves a cultural transformation, which is where people like me come in. >> So in terms of culture, so incentives drive behavior, how have you thought through and what are you doing in terms of applying new types of incentives? And how's that working? >> So when we start to measure skills were not just looking at hard skills. We're looking at soft skills, people who are good collaborators, people who have grit, people who are good leaders, people who understand how to do things over and over and over again in a successful manner. So when you start measuring your successful people, you start incentivizing the behaviors that you want to see. And when you start measuring people who can collaborate globally in global economies that that is our business as IBM, that is who we want to see. And that's how we're incentivizing the behaviors that we want to. D'oh. >> So when I look at your background here, obviously you're you're a natural fit for this kind of transformation. So you were You have an anthropology background language. Your data scientist, you do modeling. >> I always say I'm a squishy human data scientist, but I got to work with a huge group of people to create the data science profession with an IBM and get that accredited through open group. And that's something we're very passionate about is to give people a career past so that they know where their next step is. And it's all about moving to growth and sustainable growth by making sure that the workforce knows how value they are by IBM and how valuable they are by our clients. What does >> success look like to you? >> I think success is closer than we think. I think that success is when we have everybody understanding everybody, understanding what it's like to pick up the phone and answer a customer service call from our client and customer and be able to empathize and sympathize and fix the problem. We have 350,000 human beings. We know somebody in some circle that can help fix a client's problem. I think success looks like being able to get that information to the right people at the right time and give people a path so that they know that they're on the boat together, all rowing together in order to make our clients successful. >> That's great. I love the story. Thanks so much for coming on the hearing. You're very welcome. Keep it right there, but we'll be back with our next guest is a day. Violante. We're live from Fisherman's. More for the IBM CDO Chief Data officer event. Right back sticker The cube dot net is where the