 I'm Jenny Schultz. I'm the senior director of data governance for KPMG. I am not client facing. I am actually internal to KPMG. So we're now eating our own cooking or drinking our own champagne if you will and doing data for ourselves instead of just for our clients. I've been to EDW and spoken there a few times but again I'm thrilled to be here at DJIQ. I flew in from DC yesterday so long flight and it's a little bit late for me but we're going to get through it. And I know I'm standing between you and tasty beverages so I will make sure to end on time. Alright so if you don't know who KPMG is we do have a global presence. We are colloquially unknown as one of the big four accounting firms. Again we have a large global presence. I'm here to talk to you about what we're doing in the United States. Again I lead the data governance program. I started in April of 2020 right at the height of COVID. It's been fascinating meeting stakeholders through video screens. I have not met one team member, one of the 20 team members that I have on my data governance team in person. It's been interesting but I think we've figured out a way to make it work. So before that before coming to KPMG I stood up the data governance program at Freddie Mac. I was successful there and thought okay let's do this on a larger scale. So here I am. Alright so that's KPMG. Let's talk about my department. We are data strategy and operations. We were born in 2019. My boss is Jody Morton. She's the chief data officer for the U.S. firm and actually just recently named the global chief data officer. She has two jobs which is awesome for her. So we're still relatively new right internal to KPMG. Like I said I started in April of 2020 standing up the data governance firm and I've been at the data literacy game at KPMG about nine months or so. So I want to talk to you about kind of what we've learned so far. You know what we think the robust components of a day literacy program are. Hopefully you'll gain some nuggets if you're thinking about starting a program or if you're in the middle of one. But after so after 2019 you know my boss started she was on a hiring spree right for the first you know make sure year or two. I feel like we're still on a hiring spree because people are still leaving right. There's so much movement in the data space or just in companies in general. And we got the leadership team together. She got the leadership team together and created our vision and our mission. We needed kind of a North Star. What are we working toward right with our in this department. So our vision is empowered people using the right data at the right time to produce insightful outcomes in a data driven culture. And that word culture is really important. Those of you who have been in data programs started data programs know that it is not mostly a data problem right. It is a people culture and change management problem. So that's what my data leadership team is focused on along with our mission right to enable trusted data for KPMG through market leading expertise and continuous innovation. So that's who we are. This is a one pager I call it the at a glance slide of our 100 plus page data strategy. And so starting from the bottom up just to give you some insight into kind of the full data program at KPMG. So we are at the bottom right the chief data officer in function. We are implementing the data strategy building the data supply chain which I'll talk to you about a bit more in a second and all of the governance standards processes operating models tools data assets you name it. We are there building these things or enhancing them so that we leverage data as an asset. At KPMG we've been talking about data protection for a long, long time which is awesome. It's a foot in the door. We haven't talked about value creation from data. So that's what we're working on next is kind of that next iteration. These eight areas are kind of our focus areas as part of our data strategy. So enhancing data literacy which is why I'm here today implementing data governance, clarifying confidentiality rules are a lot out there. Providing data quality accelerators, making sure we have that trusted data, creating a metadata source of truth. That's also in my team, establishing master data, driving third party content. We buy a lot of data at KPMG. So it's not just data from our clients. It's not just the data we produce to run the firm. We also buy a lot of data at KPMG. So we need to manage it. And then our foundational assets. So again, those centralized storage mechanisms that we maintain for the firm. So above that, those are our focus area. Above that is a high level view of our data supply chain, right? I'm sure you all have seen something similar or have something similar at your organization. So acquiring the data, whether you buy it, get it from a client, create internally, bring it into a centralized storage mechanism, distribute it to those data consumers who need it to either do some report, create a dashboard, put some sort of, you know, analytics together, predictive models, whatever it might be. We are building that data supply chain. We're standardizing it, making it consistent, efficient, so that we can, again, get the data in the hands of the people who need it to do their jobs. And why do we exist? I mean, you know, it's probably motherhood and apple pie at this point. But we want to increase speed to market. I mentioned we are one of the big four, there's, you could also somewhat argue there's a big six out there too, if you include other companies like Accenture, for example. So we want to increase speed to market, provide the best products and services to our clients, while protecting the firm, and also improving operations, and increasing efficiency, and aligning our risk management to our risk exposure. You know, I heard Scott say this morning, you can't treat all data the same way, right? It's expensive. And we're, we're no different, right? You should treat, there's kind of a sweet spot, you don't want to over control your data or under control it, you should find that sweet spot. All right, so what is data literacy? I don't need to define it. Gartner already has. So we use Gartner's definition of data literacy at KPMG. It's pretty simple, makes sense. This is how we talk about data literacy at KPMG. They do a great job, Gartner's a great job defining all kinds of terms. It's really about, you know, how can we enable faster, better decision making, you know, get the most value out of our data, really understand data literacy helps us understand and analyze what's happening, either internally to KPMG or with our clients in the market, whatever it might be. And hopefully, you know, data literacy will help us predict what's going to happen as well. Return on investment for data literacy. Data is not free, right? Data is not free. And so you've got to invest in data literacy as well. Or else you're not going to get the returns that you expect. So the investments need to need to include attention, time, funding and infrastructure. So attention. Leaders have to pay attention. Employees have to pay attention. Our professionals need to know it's okay to spend time on literacy and learning and upskilling. The time, again, leaders need to say and actually walk the talk and say, yep, we agree, this is something you should be spending your time on. And we're going to fund it. And we're going to help you with the tools and capabilities that you need to do that job. And we're going to help you make data available so that folks can actually, again, play around. You hear me talk about more about that in a minute. Play around with data, you know, meet our clients' needs. And the returns are hopefully engaged employees, right? If employees are learning and growing, they're going to be happy. They're going to provide better services and products to our clients. Our clients are going to be happy. We're going to retain our clients. Hopefully we'll grow our client base. We'll manage our risk better. And hopefully we'll, again, make better decisions faster, which leads to lower costs in our operations. Alright, this is what we consider in the US firm, the components of a robust data literacy program. These are in no way in any sort of order. It's just kind of a blueprint. We used to do many of these things in parallel. And I'll dive into each one of these individually. So let's talk about leadership vision. I've already talked about this. And before I actually get into leadership vision, we can't do this alone. So I mentioned I have a team of 20. That's not my literacy team. That's my total team. My literacy team is three. So we are over here on the enterprise side, right? We're the centralized model here. But we rely on our champions, our practitioners in our other practice lines, we have advisory audit and tax service lines, right, that are going out and selling to customers and meeting clients needs. We rely on those folks to tell us what they need, right, to do their jobs. What do they need to learn? What do they need to be upskilling? So you'll see some things over on the local side that we're doing and some things on the enterprise side. I don't have time to drain every single bullet on every slide. And I think these are all on the on the site. So feel free to have at it on your own. But I think this one's pretty self explanatory, right? The tone is always set from the top. People don't pay attention unless your leaders are saying, Yes, this is important. I support you. You have the time. You have my support. You have the funding. Yes, you can step away from that client engagement for a week to attend something, right? The tone has to be set from the top. And we helped you that through the CDO office. We Iran a data council. I'm sure like many of you do, we leverage the data leaders and all of the areas to help, you know, spread that message. A North Star framework. So what does it mean to be data literate, right? If you're a tax professional, right, you're putting together tax returns for companies in the oil industry, right? You're going to have very different data literacy needs than, say, a partner in the audit practice, right? Very different. So we created a framework. I'll show it to you next. It's a data literacy maturity model. And it helps us articulate what are the levels of data literacy throughout the firm because not everybody needs to be at every level. It depends on your job. And again, just like data is fit for purpose, literacy is kind of the same way, right? We also have detailed competencies. We have some already developed. We are working in concert with those champions that I mentioned to create more competencies at all the levels. So again, we all have a common way of talking about data literacy. So this is a high level view of our data literacy maturity model goes from conversational, literate, competent, fluent to multilingual. The first two, conversational and literate, are components that we see everyone at the firm, every professional should have, right? You should be able to understand main concepts, talk about them, you know, especially at a high level with clients. And then the competent and fluent are, I'm working on an analytics project at a customer site. In each practice area, advisory, tax and audit, they're going to customize this to their needs. Because again, it just depends on what you're working on as to how much day literacy is needed. And the multilingual is, okay, I can take a skill like metadata management and translate it in every cross every industry. And I can explain it to others who don't understand what metadata is. So there are levels and there are competencies under each of these. And you'll see we've also split it out between data at the top and digital at the bottom, because they go hand in hand, right? We're moving to our digital world and data is needed to support that. Learner profiling, how do we know what our professionals need, right? There are a couple different ways we do that. We just piloted a data literacy assessment. There are many of them out there on the market. It sparked a lot of conversation about data literacy. But it helped us understand and help to the learners, the professionals, understand where they were. I thought I was really data literate, but I didn't score as well. And then some people who thought they didn't know anything scored higher than they thought. There's a lot of conversation, a lot of debate, but it helped us understand and it helped them understand what do they need to work on. And it sped up the conversation with their bosses or their supervisors about, hey, I want to focus on X or I need more attention on Y, right? And this year's upcoming training cycle. And at the Enterprise, my department also runs, we have a business intelligence and analytics community of practice. And we also publish templates, accelerators, you know, anything that we can do to help drive that self-service data and analytics. We do that as much as we can. But again, we really need to understand one of the components is understanding what the learner needs, how they take in information, and are they using it. So experiential range, back to so now talking about using your new found data literacy skills. You've got to practice, right? It's one thing to sit in a classroom and blah, blah, blah, like I'm doing to you now. It's another to actually put it into practice. Like let's learn together, let's create something, let's, you know, work on a client engagement to, you know, put together, help solve a problem, whatever it might be. And so, in a, locally, our advisory practice is actually doing just that. They have a list of non-profit organizations and folks who learn new skills or have, take a new certification with Alteryx, for example, get to collaborate with a non-profit organization and provide them services that they may not otherwise get, right, and do it for free. So it helps the client, it helps the professional, it's a win-win. And so we are franchising good ideas, right? That's our job at the Enterprise is when we hear about these things, we help promote, we help get more people involved so that others can, again, take advantage of that win-win situation. We also provide, I mentioned at the Enterprise, we're building that data supply chain, right? So the tools and capabilities to be able to get the data in the hands of those who need it. Same with experiential learning. We've got sandboxes, again, communities of practice. We have a Python user group so that people can get together and just talk about the tips and tricks writing Python. So it just, if you, if you make it experiential and people can, again, put into practice what they learn and figure out things together, it makes it so much more real. Mender-supported training. There's no way me and my three people could develop all the training that we would need in the data literacy space for 33,000 people in the United States firm. So, outsource. There are a ton of good vendor programs out there through universities, through other organizations who are not even some nonprofits, right? Who put together certifications. They're a ton out there. We leverage them at the Enterprise. We help get the best pricing, right? Figure out what the needs are across the organization so that we have a strategy for our vendors and our vendors aren't picking us off one by one. So we help do that. And then we supplement. There are some things that are particular or, you know, to KPMG. So like, for example, the data strategy one pager. I give an entire class on the data strategy at KPMG. Why is it needed? Why do I exist? Why do I have a job? Why are you here? Because otherwise if people don't understand the foundation, they'll never get to this level, right? Collaboration. No successful data literacy program is successful without working across business lines. So except we're at the center, we have to understand what's happening in our practice areas, audit tax and advisory. Again, what are the needs of the learner? We have a champions group or practitioners group. We meet at least monthly, if not more often to share best practices. You know, what learnings do you need? What worked? What didn't work? Right? What should we not do going forward? Right? It helps us keep the pulse about what's happening in the company. And then we can pivot if we need to or we can figure out what else do we need to implement? Do we need another learning group? Hey, we have some people who want to practice another skill with another tool. Great. Let's get those people together and actually we can learn together. And we don't get it all right the first time. We're definitely experimenting, right? With a firm as large as we are, we try things and we either fail fast and move on or, you know, we'll figure it out. Metrics. So not only do we need to understand, again, what are the learner's learning? So what degrees do they have? What degrees do they need or certifications? Are they using them? Right? So we have a database that catalogs what degrees, what certifications. I know some of the other probably big firms you all work at probably have similar similar databases through your HR department. So we have that. We understand, again, are they using these? Are they actually taking what they're learning to a client site or internally, right? We have a lot of data internally that we use to run the business. Are we leveraging our knowledge and our skill sets? Or are we not? Do we need more upskilling to make a difference? And then also being able to understand, this is kind of a future state, but something we want to aspire to is what are people querying? We have a learning site. It's called Focus. We have a learning site internally. What are people querying? What do they want to learn? If we had, we understood that and like how many people are querying, you know, Power BI. Gosh, it would be great to know that. So we're working on that. Change management, no good program. Again, I said before, it goes without having a good change management program. People, culture, change management. Again, how do we know when we need to pivot? How do we know how we're doing? We're constantly evolving and changing our data literacy program, our needs, what we're focused on. So while these are considered, we think are the core components of what works for us, what we need. Some days we spend a lot of time on this. Some days on measuring, sometimes we spend more, oops, spend more time on change, on experiential learning. It just kind of depends on the day, what the needs are. You know, everything is changing so fast. I mean, a year and a half ago, were we talking about ESG data, for example, right? We are spending a ton of time in that space. So it just kind of depends. Another thing I'll mention is rewards and recognition. You know, not everybody is motivated by money, recognition, you know, the value they create, just kind of depends, right? But some people are. And for those people who like that, hey, you know, let's celebrate that someone achieved this goal or got this certification. We're actually working with HR right now to talk about a bonus when you finish a certain level of alturic certification. So we're, we got to use every avenue possible to get people to want to learn, upskill, be day-literate and all speak the same language when it comes to data. All right, so things that you must do when it comes to day-literacy. You've already heard me say this, leadership, right? Making sure the tone is set from the top. People, the leaders are walking the talk when it comes to day-literacy. They're funding it. Again, at least I have three people. My previous organization, I had zero. So to me, it's a step up. And they want to make, got to make it easy, right? It's got to make it fast and easy for people to get the learning that they need. Local interventions, I mentioned before, we would not be successful without our practitioner, our champions in the other practice lines, you know, really understanding what the learners need, what they're doing, are they putting there what they're learning into practice? Are we all talking about data the same way? And sharing those success stories of, hey, I made a difference because I was able to help, you know, this nonprofit or this client do X with this tool or capability. Organizational system, something else my team works hard at is trying to break down barriers for things that have to get done internally. Again, with a large firm, very large firm, there are processes on processes on top of processes. And so we help get to the bottom of, hey, I need procurement to write a contract for this university to have this training course or this literacy curriculum embedded. So we help make that happen. And again, it's easier for us to do that at the center versus having each practice line call HR or procurement say, hey, help me do this, help me do this. So we are changing the way we work internally as well. And again, pay attention to rewards and recognition, you know, it's a big deal, right? The people are learning and, you know, getting certifications and degrees and actually putting into practice. So you want to recognize that. Communicate. We have many ways of communicating. You heard me mention our champion group. I run three separate types of data governance working groups, a data council, we publish a newsletter monthly about what's happening in our data program. We've got, again, those user groups, Python is one example, communities of practice, we use all of them to market whatever's happening in my department, including data literacy. So use all of them. We also the other, the service lines, the practice lines also have their own newsletters. We get in there too, right? So we're, I don't think we're at the point where we're bombarding yet because people are still hungry for the data information. I actually just got an email today. I was doing one of those data strategy talks a couple of weeks ago and I just got an email today, hey, I know you said at the end of your talk that I could, I could email you about where I can find some more data related training. I work in corporate communications. Could you point me in the right direction? Perfect. So I want to keep, keep evangelizing at the enterprise, but we need, we need our practice lines to help evangelizing as well as communicating. We leverage all channels as much as we can. So what have we learned so far? So this is what we've learned. And your organization may be different. But again, talk about the why. Again, that's why I always start with a data strategy. Why are we here? What are we working toward trust and growth with our clients? You know, working better internally, you know, providing value, whatever it is, communicate the why, leverage your leaders. I use my, my boss, the chief data officer, all the time to help communicate that message. Require training. And on a tight timeline. I don't recommend that. One of our practice lines did that. It did not work out so well. You know, learning should be fun, right? Learning should be something you do together. You know, in, in those groups that I mentioned, it should be, hey, we're going to try this really new cool thing and we're going to be successful together. And if you make it required, especially in a tight timeframe, people just they're like, yeah, no, I'm all set. You now you've ruined it. And now I'm not going to do that. There's a lot of pushback. So if you want to upskill, you got to make it interesting and fun. Learning to be a team sport, right? I mentioned that already. Let's learn together, learning together, you know, talking in groups about Python tips and tricks, how to get the best results. It just helps people put make it real and people like helping others. We have a we have a really helpful culture at KPMG. If I asked, I mean, I'm new. I ask a question every day because there's so much I don't know. I'm the new kid on the block, even being there for a little over a year and a half. I ask questions all the time. And so it people feel good when they help others, right? And so it's kind of so win win. Evaluations and assessments, I do recommend data literacy assessments or some sort of evaluation, right? To understand where you are or where your professionals are as a group, as roles, as people. It sparked a lot of debate, right? People were like, hey, this test is too easy. Now this test is too hard. Like we heard it all. So that was our pilot. We're going to go to around two in 2022 and see where we get. But yeah, it's do it. It may it is a little painful to hear all the feedback, but that's what you want. You people were talking about data literacy for the first time. It was great. All right. And then collaborate. I talked about it earlier. Working, you know, work across units. One example, again, the data literacy maturity model where we've developed some companies kind of on the left-hand side of that model, we're working on more the right-hand side now. And we're doing it with our champions, with our practitioners. It is painful to agonize over every single word in those competencies. But if you don't do it together, they don't feel a sense of buy-in, right? And then when you go to kind of say, okay, here it is. Let's, you know, let's roll this out. It's like we didn't agree to that, right? So it's painful. It takes longer. But you get better results in the end. And the same thing goes for data governance. You all who are doing it, right? You've written a data governance standard, do it together. So if you don't do it together and you just say, here's the policy, people are like, no, I'm not doing that. All right, so that's data literacy at KPMG so far. Kind of what we've learned to date, how we're implementing our program, we're still building, we will always be building, right? The world of data and technology is constantly changing. I've heard that about 50 times today. So we will continue to evolve this program. Maybe you'll see me here next year with another set of lessons learned. What we've done. But with that, oh, that's interesting. I will turn it over to you all. Do you have any questions? Yes. About the data literacy assessment. Yeah. You know, optimally with the turning model assessment, I've seen a lot of those, where can I find a data literacy assessment? There, I'll give you an example. There's a vendor called question mark who does data literacy assessments. And they have all kinds of types of tests out there that you can give to professionals. That's who we use to do our pilot and it's probably who we're going to use going forward. So yeah, question mark is one vendor that we've used. We didn't create it ourselves. I'm all about outsourcing, right? I got three people. Anybody and anybody, everyone and anyone who's willing to take it. It could be at a partner level all the way down to I'm just a college hire who just started. Just want to understand where do people, people need to know where they see themselves and we need to know where they are. Yeah, good. Thanks for the questions. Yes. And it's definitions and you know, like for example, in tax, you know, what is the GDP ratio or what are the fee ratios? But what is the value you are? Yeah. My team teaches at a broader level, right? Centralized so what is data governance? All the data governance and management terms, technologies, capabilities, the services we provide, we do a lot of marketing about that. To understand the data that the tax professionals use, that's never going to be my expertise. So I will teach, you know, the terms and tax. That'll be up to the tax professionals to figure out in the tax practice, for example. I call myself, from a data governance perspective, I call myself the what team? I'm the what team, I'm not the how team. I will tell you what you gotta do. I can help you figure it out, right? Cause again, my department does offer a lot of tools and services and capabilities so we can help you with the how, but if you don't want to use our how, you don't have to, right? If you want to keep a spreadsheet with all your business glossary terms, I'm not going to make you yet. Maybe in the future, but I might. Not yet. It's all about building goodwill in the beginning. The tools like Ultrix and PowerV, I have extensive community resources and training documents available, but one of the problems we've had is finding where to start somebody out in and how to, what do they follow to get to where they want to be without wasting a bunch of time looking into things that are possible. We recommend that they work with getting around them. Oh, that's probably best answered by one of my colleagues who actually runs. The Power BI community of practice. I was only able to attend a couple of them so far and there is, there's a lot of information to sit through out there. And I think the people on his team are analytics practitioners. So they already had the knowledge to be able to say, okay, we're going to steer people in this direction. I know that's exactly not a concrete answer to you, but since I am not an analytics professional, but I know that they are, they were the ones to pare down the content and say, here's what's important, what you need to know. So they think it's best done by the people who know their stuff, go out and carry sort of things. Yeah. And you can't just set someone loose on a vendor website. Like that's really going to put you nowhere. I saw your hand. So just to give some information for this question. Thank you. One of the things we gave was a partner between the data governance office, the learning and development team, and then BI analytics team. Yep. And we created a program called Data View. I love that. We went out to a big vendor and then we curated courses and started at beginner and intermediate and advanced level that would give people a place to start based on what their skills are. Love that. You just give me a great idea for what's next. Because our BI and analytics practice didn't spin up until the summer. So we will get there. Love that idea. I saw his hand and I'll come to you. Actually to build upon that, I decided it's extremely important to realize that you have many, many different roles in your company. And so don't think of it as a training program. But think of it as a curriculum. What that means is that you have different folks that are going to take different tracks through the training. If they're a technical asset or a technical steward, they're going to have a different potentially training program that they're going to follow or path through your curriculum. Whereas you have a business user, say a sales person, that has to have it for a QVR. They're going to have a dramatically different path than someone that's a product or a technical steward. So it's important to realize it's not one size fits all. And you can hear it to the point of earlier. Definitely can't just have them sort out what they're supposed to do. Almost swore, sorry, it's late today. Yeah, I know. I saw your hand. How do you get people excited about data governance training? Oh gosh, that's tough. How do I get people excited about data governance training? I told you I started during COVID. I'm on a screen a lot. I smile a lot on my screen. Yeah, that's a tough one. So I start, you give your trainings a fun name. So I have one that's called talk data to me, right? So it is, I hate to say it, but it works. People are like, what's that? And then they sign up. I know data governance, again, not a sexy term. But people, there is a thirst for knowledge at KPMG. People want to understand what it is, what it's all about. What am I going to make them do, right? So there's a little bit of a push, but a little bit of a pull for information as well. So I think catchy names, marketing, we do, again, we have a newsletter, we do, and it's, I call it's kind of the carrot and the stick approach. So I will eventually beat people with sticks, but I also have a lot of carrots to offer, right? Because we do provide a lot of tools and capabilities and services in my department. So that helps people come to, come to my training, learn about metadata and how my team can help you, right? So it's a little bit of a carrot and stick approach, too, that works. So when the compliance training, they have so much fun. We hear so often, you know, when you have to be compliant with two people, they run, this is, that's a series. And people are on and doing, when's the next episode? I mean, that's, do you build a story that's so popular of people sleeping? I mean, that's so popular, yeah. Storytelling is always best, right? If you can get people to the class, tell stories. It works every time. That's what they did, yeah. Dude, when's the next episode? We never heard that, when's the next compliance training? Yeah, no one says that. We've turned a lot of our, because we have to do quarterly compliance training, too, and we've turned a lot of them into, they're like, TED Talk style. People really, people, it resonates, right? And so we don't mind it, yeah. No, no, no, no, that's good to know. Did I see a hand over here or are we good? Okay. Enjoy your tasty beverages. Thank you for coming. See you tomorrow, or if you have other questions, I'll be circulating the hallway with my tasty beverage.