 Live from Cambridge, Massachusetts, it's theCUBE at the MIT Chief Data Officer and Information Quality Symposium with hosts Dave Vellante and Paul Gillin. Welcome back everybody. This is Dave Vellante with Jeff Kelly. We're here live at MIT Information Quality Symposium in Cambridge, Massachusetts at the Tank Center. It's sort of this hidden building behind Memorial Drive. I remember last year trying to find it, it was kind of a difficult building to find. Mark, you ended up halfway across Cambridge the other day. But we're here and we've been going two days straight. This is our second year at the MIT IQ. It's really now sort of merging with the Chief Data Officer Forum and is taking on a focus of the Chief Data Officer. That's a role that's emerged within regulated businesses, particularly financial services, government, and healthcare. It's touching and bleeding into some of the commercial businesses, although certainly not as rapidly. But those three that I mentioned in other regulated businesses are really adopting that CDO role quite actively. And of course it's all around data and data governance and data analytics. And we're going to touch on that particularly with an HR angle here. Karen O. Leonard is here. She's the Vice President of Benchmarking and Analytics Research at Deloitte. Everybody knows Deloitte, a world-class organization, one of the top three or four out there. So Karen, thanks very much for coming on theCUBE. Thanks for inviting me. Yeah, so why don't we start with your role and your specific focus of your organization within Deloitte? Sure. Well, we provide research tools and services to help HR organizations improve their organizational performance. And so we have a number of different practice areas. Benchmarking and Analytics is one of the key practice areas that I lead. And so I'm looking at how to help HR leaders make better decisions, better talent decisions with their data. So it's really talent management is the focus as opposed to sort of core HR. I just had a baby. That stuff is sort of table stakes, but maybe a little HR 101 here. I mean, there seem to be two worlds, right? There's the sort of bread and butter of, you know, I need health insurance and I have my family plan, whatever we can all relate to. And then there's the piece that, you know, that organizational performance experts worry about, which is how to drive performance. Are you involved in both? Where is the area of focus for Deloitte and your customers? Yeah, well, you know, there are a lot of different ways that organizations can and are using their talent data. So it could be like along the benefits angle, understanding if we make a change in benefits, how will that influence our turnover? You know, will people want to leave if we change our compensation and benefit structure? How will that change our turnover and how we try to retain people? Or on the leadership side, how can we understand what are the characteristics of successful leaders and then identify people with those characteristics and start growing with them, develop their skills and, you know, fill those skills gaps? Recruiting is a huge area, probably the biggest area within HR right now where analytics is used. Because organizations, you know, are finding really very hard to find talent. How do we understand or predict who is going to be a high performer and how can we identify those candidates and bring them into our organization? And so what are you being asked to do by clients, essentially build analytics solutions that can drive performance? I wonder if you can talk about what activities you guys are performing? Well, right now in HR, you know, HR is lagging behind other functions in terms of its use of analytics. So the things I just mentioned about, you know, predicting turnover or the consequences of a benefits change, those are about 14% of organizations are engaged in those kinds of advance or predictive analytics. You know, 86% of HR organizations are still trying to make sense of the data that they have today. And using that data to make better decisions. So we call that, you know, being in one of the reporting phases, either an operational reporting phase or advanced reporting phase, but not doing it drawing many meaningful insights from the data. So what we're looking mostly at helping HR organizations to do is try to get started with analytics. HR leaders are under tremendous pressure from, say, their sales and marketing and finance folks to up their game with respect to using data to make better decisions. And so a lot of what we do is help them get started. You know, what are some of the foundational elements that they need to make better decisions to draw insights from their data? So data quality is a huge issue that is a foundational issue within HR. And I think across all functions, how to clean your data, integrate it. What skills do you need on your team? HR organizations are just now starting to build analytics teams. So what skills do they need? How do they develop those skills? How do they hire for those skills? So really, you know, most HR organizations are getting started. For the ones that are already there, you know, the 14%, we're helping them to find more value and draw more insights from the efforts that they have already underway. So thinking about starting point, I think about ERP and maybe manufacturing. I think about the diversity of the data sets in that world is enormous. Everybody is different. Is there more commonality in HR or is it diverse like other businesses? Well, HR is using a number of different systems. So they might be using an applicant tracking system that has their recruiting data and pre-hire assessments. They are using an LMS, a learning management system that contains all the data on employee training and development. They're using a performance management system that has the ratings of performance and different competency ratings on their employees. They're using, you know, a different compensation system that stores data on, you know, what the salaries and benefits and compensation are. So they have to draw all those data sources together and then blend that with, you know, financial data from their finance group, from maybe customer data, with sales data, with external data. So actually the problem, one of the key problems within HR is the number of different systems that they're using that all contain data that they need to apply to a business problem and then integrating all that data together. So I wonder what can HR professionals learn maybe from other parts of the organization that are using analytics? One example that strikes me is you hear a lot about customer analytics and telco providers, for example, trying to identify which are their most valuable customers and sometimes treating those customers differently, you don't want to lose your most valuable customer. Is that something you can apply similarly in HR analytics? You don't want to lose your most valuable employees? Exactly, yeah. So employee engagement is a huge area for analytics and understanding what's engaging employees, what are the drivers, what are some of the levers you can pull to increase engagement. You know, I saw some numbers yesterday about, you know, if you improve your employee engagement scores, say by, you know, one percent or two percentage points, you can improve your store profitability or your company, you know, reduce your company loss and turnover by, you know, magnitudes on order of that. And so that is a huge area. As far as learning from other groups, HR organizations are looking to other groups like finance and operations and sales and marketing to get some of the talent they need. Because like I said, they're just starting their analytics team. They don't have the talent. And so they can borrow talent, hire talent from those groups and learn from their processes and apply those same kinds of methods that say marketing is doing with identifying the best customers to, you know, how do we identify high-performing employees, successful managers, and then use those characteristics to recruit and retain those targeted areas. Well, so talk about when you go into a new client, a new HR organization. I mean, what's the typical, what are some of the challenges that may be a long-time HR professional basis? I mean, this is not just unique to HR, but I'm guessing the idea of data analytics is not necessarily come second nature to a lot of HR professionals. What are some of the biggest challenges in kind of transitioning existing HR professionals to take a more data-driven approach? And how do you go about actually not just training them, but getting them to kind of believe in the analytics and that this is a new way of doing things that's actually going to improve the way they do their job? Well, like I said, a lot of HR people are sort of being pushed, pulled, prodded, coaxed into the analytics world, you know, sometimes from their colleagues in finance and marketing and sales and operations. And so, you know, one of the biggest hurdles for them is just trying to understand, because they're not data savvy people, a lot of them anyway don't have data in their DNA. What can we even do with analytics? You know, where do we get started? We have a lot of data, but they don't have the use cases, you know, to help them understand where could we even start with applying, you know. And we always recommend start with the business problem. You know, talk to your business leaders. What are their pain points? Is it, I'm having problems recruiting talent, or I have really high, new, higher turnover, or, you know, our leadership pipeline looks very weak. So identify, you know, work with your business leaders to really understand, you know, what challenges they're facing, and then let's see what data is needed to help solve those challenges. Yeah, I mean, it strikes me and encourage me if I'm wrong, but, you know, HR professionals, you know, they're people, persons, for lack of a better term. You know, we here at Wikibon and theCUBE, often we talk to a lot of IT professionals and sometimes they're not people, people, people, persons. They're more focused on the tech. So the kind of, the challenge seems to be a little bit reversed. These are really people who are very good at engaging with others, communicating, but maybe that data part of the business is not something, as you said, that comes second nature to them. Right, yes, and I think the blend, the other thing is really important to understand, you know, have data people and people, people, and so the mature HR teams that we work with have a blend of both on their teams. So they'll have people on their teams who understand HR, and they'll also have technical people who understand statistics, who have backgrounds in IT, who have backgrounds in database and can extract and manipulate data, who, you know, can build statistical models. So it really is a complex mix of skills to lend to a business problem. Let's say your business leader wants to reduce new higher attrition, or, you know, higher better quality candidates. That, you know, to solve that problem, you need a group of people with a blend of expertise, people with expertise in HR and recruiting, as well as people with expertise in statistics and database and IT, people with strong consulting skills to, you know, be able to understand that business problem and the business issue. So it really is a complex mix of skills to lend to a problem in order to, you know, apply analytics to help solving it. Karen, can you describe the maturity model, maybe take those 14%, specifically in the data and analytics world? You know, to help us draw the bell curve in terms of what that looks like and how you communicate to clients in terms of where the industry is, because benchmarking is part of what you do and where you want them to go or where they want to go. Sure. Well, you know, we start our maturity model at level one is just operational reporting. And these are organizations that, you know, are just HR organizations that are just looking at operational measures. You know, what's our turnover? How many managers have completed our sexual harassment training? You know, so it's very operationally driven, compliance-based driven and very reactive. And a little bit over half of HR organizations are still at that first level. Then we moved to level two, which we call advanced reporting. And this is where HR organizations are starting to do a little bit more audience analysis, understanding their audiences, their different audiences' needs for data. So they start segmenting their data, they start customizing it, they start looking at trends, they start looking at benchmarks. And a little bit over 30%, say 35%, 36% of organizations are at that level. Then we get into two levels that really deal with analytics and insights. So level three, we call advanced analytics, and that's where you're starting to build statistical models to understand the relationships between variables. Let's say it's not just we have a turnover problem, our turnover is increasing. That might be a finding at level two. It's why do we have a turnover problem? What's driving turnover and what talent initiatives can we put in place to help mitigate it? And 10% of organizations are there. And then the very top level, we call predictive analytics, and that's 4% of HR organizations today. And that's really taking a step further to see that same turnover example. Who is likely to turn over or quit within the next 12 months? And what can we do to help them stay? And how can we help our organization plan for different levels of predicted attrition? So thinking about the software industry in general, where are they in terms of supporting this maturity model? Can they handle levels three and four? Do they need to do a better job there? What are you looking for from your software partners? Well, most of the big HR systems vendors have capabilities at levels one and two. They're really based around collecting the data and then recording it. And many have fairly sophisticated dashboards and self-service capabilities. So that's at the level two. And then they're starting to get into levels three and four and helping to provide more advanced analytics, predictive analytics. But they're still a long way from providing really what HR people need. There's a lot of smaller vendors out there, smaller solution providers that have solutions for advanced analytics, predictive analytics. Sort of off the shelf. But a lot of it is still done by the statisticians, by the modelers using statistical packages or programming languages to do it. So you, I mean, Deloitte, all good consultants, system integrators are technology agnostic. At the same time, you don't want to throw a bunch of point products at your customers. So how do you deal with that dissonance? Will you actually, if a customer is really driving hard to a level four and maybe the big enterprise doesn't have all that, enterprise software vendor doesn't have all those pieces, will you bring in some of those smaller specialists? Is that something that you generally try to avoid? I wonder if we could talk about that a little bit. In my organization, we don't do consulting engagements. We do research and tools and help advise customers. So we'll advise a customer or a member on what options are out there. We get the question a lot of what's the best tool? What should we use? What's the best platform? It depends. Yeah, it really does depend. There are a lot of factors. But we'll at least lay out the landscape of different options. And then we can help an organization go down that path of choosing one. What are the challenges when it comes to HR and analytics? Do you find a lot of HR organizations think that maybe they can just throw technology at the problem by a new application, by a new data warehouse and that's going to solve the problem? Oh yeah. And a lot of HR people will come to us and say, what tool do I need? When really, they need to start a lot earlier. A tool is not going to get them to level three or level four analytics despite what all the vendors might be out there selling. I was at HR Tech last year and every vendor was selling a predictive analytics solution. But really, the tool comes along as part of the path. It's not the first step in the path. They have a lot more work to do in terms of just looking at their data, their data quality, the skill sets they need. I think the people using the tools, getting the skilled people to use the tools is more important than what tool do I need right away. So Karen, your business model, again I'm fascinated by the benchmarking piece. You'll go into a senior HR executive and they'll say, okay, I want to understand how I benchmark relative to my peers. Is that right? And is that a service that you provide or am I thinking about that? That is part of the service that we provide. We have a lot of different metrics, mainly operational and efficiency-based metrics that HR organizations can benchmark themselves against. And since that's where a lot of HR organizations are now, that's something that they use frequently. And so benchmark against our metrics is certainly something we offer. We can also benchmark organizations against their maturity model. So one of the things we try to help HR organizations do is understand where am I today and then where do I want to be? And it's not I'm at level one today and next year I want to be at level four. Maybe I'm at level one today and over the course of the next year I get to level two and then build out a roadmap. But then we help them understand what do I need to get there, some prescriptive steps of if you're at level one now and we have 15, 20 different factors that go into an organization's maturity level and help them identify what are some of the key levers they can pull to improve their analytics capabilities. Okay, so last question, we're at the CDL forum, the MIT IQ. The dean yesterday said do something different, do something exciting, so I challenged this all. I got my list in the back of my book here. What's exciting you? Any new ideas that came out of this conference? Talk about that a little bit. I think what's excited me is just all the momentum behind analytics and you see here people from different industries, people from different job roles coming together to talk about how analytics can be used to help organizations make better decisions and then addressing some of the challenges. I think there's been a lot in the media about how over-hyped big data is. But seeing people here really working on these issues and challenges and making better business value based on it is really exciting. As well as the talk yesterday where, and this is something I believe in thoroughly, which is you don't really need big data and monumental solutions to make incremental improvements in your business. Sometimes it's small data and it's small steps along the way that can get you much better business value. You have the Genie Ross prescription, right? That's right, that's right. Thanks very much for coming to the CUBE. It was really a pleasure having you. Thank you. Take care. Everybody keep it right there. We'll be right back with our next guest. We're live from the MIT Information Quality Symposium, the Chief Data Officer Forum. This is the CUBE. We'll be right back.