>>Yeah, >>yeah. Hello, everyone. And welcome to the cultivating a data slowing culture. Jack, my name is Paula Johnson. I'm thought Spots head of community, and I am so excited to be your host heared at beyond. One of my favorite things about beyond is connecting with everyone and just feeling that buzz and energy from you all. So please don't be shy and engage in the chat. I'll be there shortly. We all know that when it comes to being fluent in a language, it's all about how do you take data in the sense and turn it into action? We've seen that in the hands of employees. Once they have access to this information, they are more engaged in their role. They're more productive, and most importantly, they're making better decisions. I think all of us want a little bit more of that, don't we? In today's track, you'll hear from expert partners and our customers and best practices that you could start applying to build that data. Fluent culture in your organization that we're seeing is powering the digital transformation across all industries will also discuss the role that the analysts of the future plays when it comes to this cultural shift and how important it is for diversity in data that helps us prevent bias at scale. To start us off our first session of the day is cultivating a data fluent culture, the essence and essentials. Our first speaker, CEO and founder of the Data Lodge, Valerie Logan. Valerie, Thank you for joining us today of passings over to you Now. >>Excellent. Thank you so much while it's so great to be here with the thought spot family. And there is nothing I would love to talk more about than data literacy and data fluency. And I >>just want to take a >>second and acknowledge I love how thought spot refers to this as data fluency and because I really see data literacy and fluency at, you know, either end of the same spectrum. And to mark that to commemorate that I have decorated the Scrabble board for today's occasion with fluency and literacy intersecting right at the center of the board. So with that, let's go ahead and get started and talking about how do you cultivate a data fluent culture? So in today's session, I am thrilled to be able to talk through Ah, few dynamics around what's >>going >>on in the market around this area. Who are the pioneers and what are they doing to drive data fluent culture? And what can you do about it? What are the best practices that you can apply to start this? This momentum and it's really a movement. So how do you want to play a part in this movement? So the market in the myths, um you know, it's 2020. We have had what I would call an unexpected awakening for the topic of data literacy and fluency. So let's just take a little trip down memory lane. So the last few years, data literacy and data fluency have been emerging as part of the chief data officer Agenda Analytics leaders have been looking at data culture, um, and the up skilling of the workforce as a key cornerstone to how do you create Ah, modern data and analytic strategy. But often this has been viewed as kind of just training or visualization or, um, a lot of focus on the upscaling side of data literacy. So there's >>been >>some great developments over the past few years with I was leading research at Gartner on this topic. There's other work around assessments and training Resource is. But if I'm if I'm really honest, they a lot of this has been somewhat viewed as academic and maybe a bit abstract. Enter the year 2020 where data literacy just got riel and it really can no longer be ignored. And the co vid pandemic has made this personal for all of us, not only in our work roles but in our personal lives, with our friends and families trying to make critical life decisions. So what I'd ask you to do is just to appreciate that this topic is no longer just a work thing. It is personal, and I think that's one of the ways you start to really crack. The culture code is how do you make this relevant to everyone in their personal lives? And unfortunately, cove it did that, and it has brought it to the forefront. But the challenge is how do you balance how do analytics leaders balance the need to up skill the workforce in the culture, with all of these competing needs around modernizing the platform and, um, driving trusted data and data governance? So that's what we'll be exploring is how to do this in parallel. So the very first thing that we need to do is start with the definition and I'd like to share with you how I framed data literacy for any industry across the globe. Which is first of all to appreciate that data literacy as a foundation capability has really been elevated now as >>an >>equivalent to people process and technology. And, you know, if you've been around a while, you know that classic trinity of people process and technology, It's the way that we have thought about how do you change an organization but with the digitization of our work, our lives, our society, you know anything from how do we consume information? How do we serve customers? Um, you know, we're walking sensors with our smartphones are worlds are digital now, and so data has been elevated as an equivalent Vector two people process and technology. And this is really why the role of the chief data officer in the analytics leader has been elevated to a C suite role. And it's also why data literacy and fluency is a workforce competency, not just for the specialist eso You know, I'm an old math major quant. So I've always kind of appreciated the role of data, but now it's prevalent to all right in work in life. So this >>is a >>mindset shift. And in addition to the mindset shift, let's look at what really makes up the elements of what does it mean to be data literate. So I like to call it the ability to read, write and communicate with data in context in both work in life and that it has two pieces. It has a vocabulary, so the vocabulary includes three basic sets of terms. So it includes data terms, obviously, so data sources, data attributes, data quality. There are analysis methods and concepts and terms. You know, it could be anything from, ah, bar Chart Thio, an advanced machine learning algorithm to the value drivers, right? The business acumen. What problems are resolving. So if you really break it down, it's those three sets of terms that make up the vocabulary. But it's not just the terms. It's also what we do with those terms and the skills and the skills. I like to refer to those as the acronym T T E a How do you think? How do you engage with others and how do you act or apply with data constructively? So hopefully that gives you a good basis for how we think about data literacy. And of course, the stronger you get in data literacy drives you towards higher degrees of data fluency. So I like to say we need to make this personal. And when we think about the different roles that we have in life and the different backgrounds that we bring, we think about the diversity and the inclusion of all people and all backgrounds. Diversity, to me is in addition to diversity of our gender identification, diversity of our racial backgrounds and histories. Diversity is also what is what is our work experience in our life experience. So one of the things I really like to do is to use this quote when talking about data literacy, which is we don't see things as they are. We see them as we are. So what we do is we create permission to say, you know what? It's okay that maybe you have some fear about this topic, or you may have some vulnerability around using, um you know, interactive dashboards. Um, you know, it's all about how we each come to this topic and how we support each other. So what I'd like to dio is just describe how we do that and the way that I like to teach that is this idea that we we foster data literacy by acknowledging that really, you learn this language, you learn this through embracing it, like learning a second language. So just take a second and think about you know what languages you speak right? And maybe maybe it's one. Maybe it's too often there's, you know, multiple. But you can embrace data literacy and fluency like it's a language, and somehow that creates permission for people to just say, you know, it's OK that I don't necessarily speak this language, but but I can try. So the way that we like to break this down and I call this SL information as a second language built off of the SL construct of English as a second language and it starts with that basic vocabulary, right? Every language has a vocabulary, and what I mentioned earlier in the definition is this idea that there are three basic sets of terms, value information and analysis. And everybody, when they're learning things like Stow have like a little pneumonic, right? So this is called the V A model, and you can take this and you can apply it to any use case. And you can welcome others into the conversation and say, You know, I really understand the V and the I, but I'm not a Kwan. I don't understand the A. So even just having this basic little triangle called the Via Model starts to create a frame for a shared conversation. But it's not just the vocabulary. It's also about the die elects. So if you are in a hospital, you talk about patient outcomes. If you are in insurance, you talk about underwriting and claims related outcomes. So the beauty of this language is there is a core construct for a vocabulary. But then it gets contextualized, and the beauty of that is, even if you're a classic business person that don't you don't think you're a data and analytics person. You bring something to the party. You bring something to this language, which is you understand the value drivers, so hopefully that's a good basis for you. But it's not just the language. It's also the constructs. How do you think? How do you interact and how do you add value? So here's a little double click of the T E. A acronym to show you it's Are you aware of context? So when you're watching the news, which could be interesting these days, are you actually stepping back and taking pause and saying E wonder what the source of that ISS? I wonder what the assumptions are or when you're in interacting with others. What is your degree of the ability? Thio? Tele Data story, Right? Do you have comfort and confidence interacting with others and then on the applying? This is at the end of the day, this is all about helping people make decisions. So when you're making a decision, are you being conscientious of the ethics right, the ethics or the potential bias in what you're looking at and what you're potentially doing? So I hope this provides you a nice frame. Just if you take nothing else away, take away the V A model as a way to think about a use case and application of data that there's different dialects. So when you're interacting with somebody, think of what dialect are they speaking? And then these three basic skill sets that were helping the workforce to up skill on. But the last thing is, um, you know, there's there's different levels of proficiency, and this is the point of literacy versus fluency. Depending on your role. Not everyone needs to speak data at the same level. So what we're trying to do is get everyone, at least to a shared level of conversational data, right? A basic level of foundation literacy. But based on your role, you will develop different degrees of fluency. The last point of treating this as a language is the idea that we don't just learn language through training. We learn language through interaction and experience. So I would encourage you. Just think about all what are all the different ways you can learn language and apply those to your relationship with data. Hopefully, that makes sense. Um, >>there's a >>few myths out there around this topic of data literacy, and I just want to do a little myth busting real quickly just so you can be on the lookout for these. So first of all data literacy is not about just about training. Training and assessments are certainly a cornerstone, however, when you think about developing a language, yeah, you can use a Rosetta Stone or one of those techniques, but that only gets you. So far. It's conversations you have. It's immersion. Eso keep in mind. It's not just about training. There are many ways to develop language. Secondly, data literacy is not just about internal structure, data and statistics. There are so many different types of data sets, audio, video, text, um, and so many different methods for synthesizing that content. So keep in mind, this isn't just about kind of classic data and methods. The third is visualization and storytelling are such a beautiful way to bring data literacy toe life. But it's not on Lee about visualization and storytelling, right? So there are different techniques. There are different methods on. We'll talk in a minute about health. Top Spot is embedding a lot of the data literacy capabilities into the environment. So it's not just about visualization and storytelling, and it's certainly not about making everybody a junior data scientist. The key is to identify, you know, if you are a call center representative. If you are a Knop orations manager, if you are the CEO, what is the appropriate profile of literacy and fluency for you? The last point and hopefully you get this by now is thistle is not just a work skill. And I think this is one of the best, um, services that we can provide to our employees is when you train an employee and help them up. Skill their data fluency. You're actually up Skilling, the household and their friends and their family because you're teaching them and then they can continue to teach. So at the >>end of >>the day, when we talk about what are the needs and drivers like, where's the return and what are the main objectives of, you know, having a C suite embrace state illiteracy as, ah program? There are primarily four key themes that come up that I hear all the time that I work with clients on Number one is This is how you help accelerate the shift to a data informed, insight driven culture. Or I actually like how thought spot refers to signals, right? So it's not even just insights. It's How do you distill all this noise right and and respond to the signals. But to do that collectively and culturally. Secondly, this is about unlocking what I call radical collaboration so well, while these terms often, sometimes they're viewed as, oh, we need to up skill the full population. This is as much about unlocking how data scientists, data engineers and business analysts collaborate. Right there is there is work to be done there, an opportunity there. The third is yes, we need to do this in the context of up Skilling for digital dexterity. So what I mean by that is data literacy and fluency is in the context of whole Siris of other up Skilling objectives. So becoming more agile understanding, process, automation, understanding, um, the broader ability, you know, ai and in Internet of things sensors, right? So this is part of a portfolio of up skilling. But at the end of the day, it comes down to comfort and confidence. If people are not comfortable with decision making in their role at their level in their those moments that matter, you won't get the kind of engagement. So this is also about fostering comfort and confidence. The last thing is, you know, you have so much data and analytics talent in your organization, and what we want to do is we want to maximize that talent. We really want to reduce dependency on reports and hey, can you can you put that together for me and really enable not just self service but democratizing that access and creating that freedom of access, but also freed up capacity. So if you're looking to build the case for a program, these air the primary four drivers that you can identify clear r A y and I call r o, I, I refer to are oh, I two ways return on investment and also risk of ignoring eso. You gotta be careful. You ignore these. They're going to come back to haunt you later. Eso Hopefully this helps you build the case. So let's take a look at what is a data literacy program. So it's one thing to say, Yeah, that sounds good, but how do you collectively and systemically start to enable this culture change? So, in pioneering data literacy programs, I like to call a data literacy program a commitment. Okay, this is an intentional commitment to up skill, the workforce in the culture, and there's really three pieces to that. The first is it has to be scoped to say we are about enabling the full potential of all associates. And sometimes some of my clients are extending that beyond the virtual walls of their organization to say S I'm working with a U. S. Federal agency. They're talking about data literacy for citizens, right, extending it outside the wall. So it's really about all your constituents on day and associates. Secondly, it is about fostering shared language and the modern data literacy abilities. The third is putting a real focus on what are the moments that matter. So with any kind of heavy change program, there's always a risk that it can. It can get very vague. So here's some examples of the moments that you're really trying to identify in the moments that matter. We do that through three things. I'll just paint those real quick. One is engagement. How do you engage with the leaders? How do you develop community and how do you drive communications? Secondly, we do that through development. We do that through language development, explicitly self paced learning and then of course, broader professional development and training. The third area enablement. This one is often overlooked in any kind of data literacy program. And this is where Thought spot is driving innovation left and right. This is about augmentation of the experience. So if we expect data literacy and data fluency to be developed Onley through training and not augmenting the experience in the environment, we will miss a huge opportunity. So thought spot one. The announcement yesterday with search assist. This is a beautiful example of how we are augmenting guided data literacy, right to support unending user in asking data rich questions and to not expect them to have to know all the forms and features is no different than how a GPS does not tell you. Latitude, longitude, a GPS tells you, Turn left, turn right. So the ability to augment that the way that thought spot does is so powerful. And one of my clients calls it data literacy by design. So how are we in designing that into the environment? And at the end of the day, the last and fourth lever of how you drive a program is you've gotta have someone orchestrating this change. So there is a is an art and a science to data literacy program development. So a couple of examples of pioneers So one pioneer nationwide building society, um, incredible work on how they are leveraging thought spot In particular, Thio have conversations with data. They are creating frictionless voyages with data, and they're using the spot I Q tool to recommend personalized insight. Right? This is an example of that enablement that I was just explaining. Second example, Red hat red hat. They like to describe this as going farther faster than with a small group of experts. They also refer to it as supporting data conversations again with that idea of language. So what's the difference between pioneers and procrastinators? Because what I'm seeing in the market right now is we've got these frontline pioneers who are driving these programs. But then there's kind of a d i Y do it yourself mentality going on. So I just wanted to share what I'm observing as this contrast. So procrastinators are kind of thinking I have no idea where they even start with us, whereas pioneers air saying, you know what, this is absolutely central. Let's figure it out procrastinators are saying. You know what? This probably isn't the right time for this program. Other things are more important and pioneers air like you know what? We don't have an option fast forward a year from now. Do we really think this is gonna organically change? This is pervasive to everything we dio procrastinators. They're saying I don't even know who to put in charge for this. And pioneers there saying this needs a lead. This needs someone focusing on it and a network of influencers. And then finally, procrastinators, They're generally going, you know, we're just gonna wing this and we'll just we'll stand up in academy. We'll put some courses together and pioneers air saying, You know what? We need to work smart. We need a launch, We need a leverage and we need to scale. So I hope that this has inspired you that, you know, there really are many ways to go forward, as FDR said, and only one way of standing still. So not taking an action is a choice. And there were, you know, it does have impact. So a couple of just quick things to wrap up one is how do you get started with the data literacy program, so I recommend seven steps. Who's your sponsor and who is the lead craft? Your case for change. Make it explicit. Developed that narrative craft a blueprint that's scalable but that has an initial plan where data literacy is part of not separate. Run some pilot workshops. These can be so fun and you can tackle the fear and vulnerability concern with really going after, Like how? How do we speak data across different diverse parts of the team. Thes are so fun. And what I find is when I teach people how to run a workshop like this, they absolutely want to repeat it and they get demand for more and more workshops launch pragmatically, right? We don't have any time or energy for big, expansive programs. Identify some quick winds, ignite the grassroots movement, low cost. There are many ways to do that. Engage the influencers right, ignite this bottom up movement and find ways to welcome all to the party. And then finally, you gotta think about scale right over time. This is a partnership with learning and development partnership with HR. This becomes the fabric of how do you onboard people. How do you sustain people? How do you develop? So the last thing I wanted to just caution you on is there's a few kind of big mistakes in this area. One is you have to be clear on what you're solving for, right? What does this really mean? What does it look like? What are the needs and drivers? Where is this being done? Well, today, to be very clear on what you're solving for secondly, language matters, right? If if that has not been clear, language is the common thread and it is the basis for literacy and fluency. Third, going it alone. If you try to tackle this and try to wing it. Google searching data literacy You will spend your time and energy, which is as precious of a currency as your money on efforts that, um, take more time. And there is a lot to be leveraged through through various partnerships and leverage of your vendor providers like thought spot. Last thing. A quick story. Um, over 100 years ago, Ford Motor Company think about think about who the worker population was in the plants. They were immigrants coming from all different countries having different native languages. What was happening in the environment in the plants is they were experiencing significant safety issues and efficiency issues. The root issue was lack of a shared language. I truly believe that we're at the same moment where we're lacking a shared language around data. So what Ford did was they created the Ford English school and they started to nurture that shared language. And I believe that that's exactly what we're doing now, right? So I couldn't I couldn't leave this picture, though, and not acknowledge. Not a lot of diversity in that room. So I know we would have more diversity now if we brought everyone together. But I just hope that this story resonates with you as the power of language as a foundation for growing literacy and fluency >>for joining us. We're actually gonna be jumping into the next section, so grab a quick water break, but don't wander too far. You definitely do not want to miss the second session of today. We're going to be exploring how to scale the impact and how to become a change agent in your organization and become that analysts of the future. So season