Taylor McBride: Hello everyone and welcome to our fourth Erudit Shockwave Talk. My name is Taylor McBride and I am Erudit's Head of Sales. My mission here is to raise awareness on how our AI powered software can help to raise awareness about employee engagement in order to prevent burnout and turnover and even increased satisfaction. Pretty cool job at Erudit. We love learning and having an opportunity to learn alongside the industry professionals like yourselves make learning much more enjoyable. So today I get to be your host and introduce our guest speaker. But before I introduce the guest speaker, a couple reminders. One time is precious. We're gonna try to keep this within an hour. Two, make sure to chat us any questions you have at any time. I'll be reading them, I'll be incorporating them into our discussion. So please keep us on our toes and ask us questions as we go.

Taylor McBride: Last, this is being recorded so we can share this with people who wanted to come but couldn't make it. And actually last, last thing is throughout this talk, please feel free to invite colleagues or friends or whoever to join the conversation. If they missed the first few minutes, we can always send out a video to him. Okay, so let's get to it. It's time to welcome our fantastic speaker. It's a pleasure to welcome Christina. And Christina is a data whisperer who has developed her career in tech most recently at Google and Ways, no slouch for sure. And most recently or previously before that she was at Nielsen and SA Institute. She also holds an adjunct faculty position at i e Business school. And she's also a podcast host from EM 360 where she talks about the latest data tech insights and trends. She does a lot. So we're super thankful for having someone as passionate about data and emerging technologies as Christina, thank you so much for being with us today.

Christina Stathopoulos: Thank you so much for having me on. And you did a really good job pronouncing my last name, by the way, which most people butcher.

Taylor McBride: Well, thank you. It was not an easy one, but glad glad you're here. Yeah. First, let's talk a little bit more about it. The four is yours. Christina, why don't you start to take us through a, a few of your insights here.

Christina Stathopoulos: Okay, sure. So first of all, welcome to everyone tuning in today. Feel free to get involved in the chat, share your own ideas, your experiences, and feel free to ask questions. We'll be addressing them throughout as well as having an a q and A at the end. But as Taylor has already kindly introduced me, my name is Christina stk. I am a very active voice within the data community. I'm passionate about all things data and tech. And when it comes to data, I think there's nothing more important than what I call the last mile of analytics, which really comes down to proper data visualization and data storytelling. You want to make sure that you deliver results and that your, your data findings and your recommendations are presented in an impactful way to drive change within your organization. And there's no better way to do this then transforming your data into a picture or visual.

Christina Stathopoulos: So let's get started. So we have these massive amounts of information and data available right at our fingertips today. How do we cope with it? So there's this quote that I love. I took it from the book Mega Change, and it says, as you can see here, where for most of history, we have suffered from a shortage of information. Tomorrow we will struggle with a surface already today, the chief difficulty is not so much obtaining information, but finding the relevant bits, the relevant data. So we've always struggled with not having enough information or data. We never had enough information to really understand our environment or our business or our body, whatever it may be. But for the first time ever, the exact opposite is true. We have too much information, we have too much data. And a key for businesses today then is making sense of all the data at hand.

Christina Stathopoulos: And proper data visualization is one of the best ways to do this. When a data visualization is done correctly, you can show a lot of information in very little space. So large amounts of data can be easily digestible when it's presented visually in a graph or a picture instead of in a table of numbers. So moving on, why is this? It's because humans are visual creatures. As you can see the statistics here, 90% of all information transmitted to our brains is visual. And also people remember 80% of what they see, but only 20% of what they hear. So good data visualization, it actually calls to our natural abilities for pattern recognition. We see patterns much clearer in pictures than in something like a table of numbers. And then as well, we absorb imagery 60,000 times faster than words. Images make a beeline for the heart. They have the most impact on us. So data visualization is perfect for this. When it's done correctly, we can pull out these key messages from a visualization immediately, and it's thanks to those natural abilities for pattern recognition.

Taylor McBride: Okay, that is super, super interesting to me. So quick question for you. Do you believe that the way that data is going to be presented should be taken into consideration as we're actually collecting that data? And if so, what would that change in the way that we collect and process data if we have like the end in mind?

Christina Stathopoulos: Yeah, this is a really good question and I'll speak from my experience, but I do not know if there's, I don't know if this is the right answer, but what I would say is from my experience, the teams in charge of collecting and formatting the, the, the raw data are usually quite different from the downstream data teams that are visualizing this data and that are, that are presenting it to these key business stakeholders. So in an ideal world, yes, we would have a clear communication stream between these teams, but in reality we do not. So a lot of data is collected to begin with and then, and, and really better to collect more than to collect less. And then the data, the, the data is filtered for better business for for different d business purposes, depending on the team. And then it, as it moves more downstream and closer to the business stakeholders, you're filtering and filtering more, being able to a as you asked, being able to really have this data visualization in mind when you're collecting the data. In my opinion, it's very difficult because of this disconnect between all of the teams from the, from the point of collecting it to the point of data visualization. There's usually multiple teams or people involved. So, and ideally, yes, it would be great to communicate that and have data visualization in mind from the beginning, but realistically it's a lot more difficult to implement than you might expect.

Christina Stathopoulos: So moving on from this, let's see an example of data visualization inaction. And I'm going to show you a really simple exercise. I want you to tell me in the chat here how many fives are pictured here, and you have to answer as quickly as possible only showing this for a few seconds. Let's, let's move on to the next slide.

Christina Stathopoulos: So what about now? How many fives are pictured here? And you can share in the chat. So the answer should be 10, right? I think it's much easier when we bring design into the mix. In this case it's visualization via colors to show that important data. This is a very simple example, but this really is what data visualization is all about. So moving on from this, let's take a step back. What exactly is good data visualization at its core? Of course, data visualization is the graphical representation of information. And like I mentioned earlier, huge amounts of data can be easily digested when they're represented visually. And really, data visualization is found at the intersection of three very different domains or skill blocks. And as we've put them here, it's data design and function. So first of all, the data piece is really obvious. You need good quality data to begin with, but now you want to transform it into these clear messages.

Christina Stathopoulos: You want it to be purposeful and insightful so that you can influence your audience and your stakeholders with the data. Now next, we've got function. So how can we give meaning to your data? How can we give it a purpose by finding a core message hidden within? So what point, try to think about what point are you trying to get across? What is the final action or the feeling that you want to invoke in your audience? So look for the right angle to present your data. And then finally, we've got design. So using your data and the message that you want to convey, how can you build this into an impactful visualization using proper design theory, design principles and as well best practices. So you want to keep all three factors balanced when you are building your data visualizations. You wanna stay true to your data, you want to give it a valid function or a clear message, and you want to comply with good design principles.

Taylor McBride: Okay, so I absolutely love that little game where we, where we find the numbers, there's massive amounts of numbers there, and then you put a few in the back, you bring a few forward and then becomes much easier to do the task, right? So essentially as we're looking through this people data, we're trying to find the important things and ignore some of the other things. So how do you, or I guess, what's your criteria? What's your process for separating that wheat from the chaff or finding the good data and eliminating the bad data or unimportant data?

Christina Stathopoulos: Yeah, and, and I will highlight here first of all, that you're going to be throwing a lot of data out. Like a lot of the data is going to be irrelevant when you settle on the, the final message that you're trying to convey. The people, analyst, the data scientists and so on are tasked with sifting through all of this data to find the relevant bits. That's really the, the core of their role. And not, I'll say not all of the correlations or the trends within the data are obvious from the start. It really takes some digging to find them to find something interesting. And you want to make sure that you're narrowing it down as much as possible to the core message that you want to convey. And one thing that I always have in mind, and I teach this in, in data visualization classes that I, that I have, but there's this quote by Pablo Picasso, and he said that my picture is a sum of destructions.

Christina Stathopoulos: And I would say that data visualization is the same thing. You start with all of this data and then you're like destroying it. You're throwing out, you're filtering down until you focus on the core message. So it's actually good that you're throwing out data, you need to focus on really the, the purpose of what you're trying to achieve with this. So how do you separate out the data? So first of all, you do some exploratory data analysis, of course. So understanding what you have available and the patterns within. And I would say that I think it definitely helps to have business domain knowledge for the data that you're working with. So it gives you a better eye for spotting special patterns like we were speaking about, like you mentioned, people, people, analytics and hr. So someone with experience working with people, data and working within that team. The more experience they have and the more they understand the business, the better they are going to be at spotting special trends, special correlations and relationships within the data.

Taylor McBride: Okay, awesome.

Christina Stathopoulos: So let's move on from here. So data visualization really is, it's, it's vital for any industry, any organization, any department. And it's because data has become our lifeblood, but we need to give it shape and we need to give it a meaning. And I want to be very clear that the purpose of data visualization is not to make data more beautiful. I want to be very clear about that. It's not to make it pretty, it's really to provide insight into these complex data sets by trying to find a way to communicate the key messages and aspects in more intuitive and meaningful ways for your audience. And I would say it's typically used to do one or multiple of these three blocks that you see here. So it's either used to inform, to educate and or to persuade.

Christina Stathopoulos: So I'm here with adit today and I wanted to make sure that I tie all of this into the field of Human Resources and People Analytics. Everyone is trying to tell their story, but what story can Human Resources and people, analytics teams tell? So we're gonna go through the what, the how and the why of data visualization specifically for these types of teams. So first of all, let's start with the what. What data can be sourced for data informed HR decisions? So I think you should always try to consider both external and internal data that could be valuable. And this is applicable for hr, but also really any department, any company. Consider what you can use internally plus externally. So in this case focused on this, this ar. So external data can really be anything like financial, economic data, which is particularly important today given how chaotic the economy seems to be as well.

Christina Stathopoulos: It's always good to have in mind competitive market data, understand your competitors and the competitive landscape. And then other external factors keep in mind something that could be relevant to your business. So maybe it's product price swings in the market or the weather, whatever it may be. So that's external. And then we have internal, internally, Human Resources should have access to a lot of people data. So hiring, firing and layoff decisions, compensation, tenure in the company, the employee performance reviews, the vacation days used and when they're being used, trainings being taken, et cetera, et cetera. So all of these things around the people within the organization as well as the reporting structure and the hierarchy, understanding how, how really the company is structured. And then on a wider scale they should hopefully have access to broader company data, like company performance metrics. You could do calculations like revenue per employee and how that changes over time. Now this list that I've showed you on the screen, it's, it's no way inclusive. There's lots more data out there, but this is really to help you get the wheels turning.

Taylor McBride: Okay, super interesting. So I have a quick question and please, audiences you have questions, feel free to send them in. But my question is, in your experience, are Human Resources departments generally up to date in the importance of data and visualization? Are, are we at a moment of maybe like evangelization on the relevance and importance of data or is that already there?

Christina Stathopoulos: I would say, I think, I think we're at a moment of realization for a lot of different departments and industries and so on, and Human Resources departments are definitely included. One thing I've, I've noticed personally is this, this title, this role of people analyst? I don't remember having heard that years ago. And now I'm hearing more and more about Human Resources teams having their dedicated people analyst as they should really every team has a, an importance to really has, has a reason to have an analyst on the team. It's incredibly valuable to have one or multiple of course, depending on your size. So I think we are at a moment of realization and I'm also seeing it like reflected in the market with more startups, more solutions being launched to help companies, but especially in this last mile of analytics, like help really along the whole, actually along the whole data chain, but especially in the last mile of analytics, helping companies make the most out of their data and have it be accessible and presentable for business stakeholders. I don't know if you have any comments on this as well, tey, because I know you work very closely with a lot of, of HR companies.

Taylor McBride: Yeah, yeah. I mean, I talk to Human Resources people all day, every day. I go to different trade shows for like HR tech for example, which I'm sure some of you guys have gone to and there's, you know, there's thousands of people there. So I, I get a lot of these sorts of conversations and I definitely have seen an increase in things like people analysts and people analytic departments, even at large companies, which is fantastic. But I think traditionally what I've heard from a lot of these people, I mean I've worked in the HR technology space for a little while now. I've heard a lot of Human Resources people feel like either they don't have a seat at the table or they have a seat at the table, but there at this decision making or leadership table with, you know, marketing or sales or engineering or what all these departments that have mountains and mountains of quantitative data. And a lot of times these people feel like they, you know, come to the table with less data and it, it doesn't allow them to make as many arguments or persuasions to their team about why they need resources because they feel like their data is a little bit lacking or they just don't have the same amounts. I mean, is that something you've seen in the past and is that changing along with the scene in general?

Christina Stathopoulos: I, I mean, I think, so I think more and more just really not even talking hr, but across all sorts of departments and organizations, you're realizing that like data is not just for, you know, logistics, optimizing your logistics or it's not for these specific use cases that it was before. It can be applied anywhere. And yes, Human Resources maybe is a newer, a newer and growing field, but I think it's becoming more, we're noticing it becoming more important, more important with the appearance of more people, analysts, like you said, or entire people analytics teams, that's reflection of how important it has become. And everyone's realizing as well this notion of like not having as much data as other departments. So I think having those teams there and starting to build out your, your data is going to help. And then as well this, this idea of data democratization, so being able to share more between and across teams, which also in turn helps with cross collaboration. So having the teams looking at similar data also helps them when it comes time to work together.

Christina Stathopoulos: So let's go back to the presentation. So I went through the, the what this data, using all of this data, but what do we do? What's, what's next? How do we take advantage of all of this data? So first of all, partner, you need to partner with either your people analyst as Taylor and I we're, we're just speaking about if you have those within your organization or if you don't, then you should partner cross-functionally with a data science team so you can try to bring your data to life through proper data visualization. Now, in parallel, I would also say that you should also try to inspire your Human Resources team to engage and interact with your company data through these easy to understand visualizations as they're being developed. And this, in order for your Human Resources, your entire Human Resources team to be using this data properly, it's very likely going to require some upskilling of the team to ensure that everyone is data literate. So empowering your team to feel confident working with data is key. We cannot assume that this is a natural ability, but with proper training, really anyone and everyone on a team can be em empowered to get the most out of data and they should be empowered to do this. But there does does need to be some training done so that they're not misusing the data.

Taylor McBride: Okay. So I, when we're talking about this subject of, you know, upskilling the team, there's certain teams that have background on this, right? They have people, analytics people, or there's other teams that have access, you know, to another data analyst within the company that they can borrow them for for whatever reason. But when you don't have that, when you don't have the people analysts or you don't have people you can borrow, you know, what kind of steps can you take? I take for myself or can I take for my Human Resources team so they can all be upskilled? Like what, what kind of path can we take there?

Christina Stathopoulos: Yeah, this is a good and tricky question, especially if you're not being given like the, the bandwidth or the the headcount to tackle this. But first of all, like I mentioned before with data literacy, I think that the entire team needs to be given these basic data literacy upskill trainings. And these, the, when I talk about data literacy, I'm not talking about empowering the team to all be downloading raw data and building the visualizations themselves. I'm actually talking about something data literacy more in the, in the sense of being able to interpret and work with the data correctly, feeling confident working with the data and making decisions based on that data. So a training that really gives them confidence in general to work with data and, and understand the basics. Now from there, of course you've still got to have people who can build the data visualizations like we were talking about and the reporting that in the end is going to empower the rest of the team to, to look through it and make decisions.

Christina Stathopoulos: 2If you don't have, if you're not being given the HE headcount, if you don't have a people analyst or you're not being, being given the resources from another cross-functional team, what I would recommend if you can, is to see if anyone in the Human Resources organization it's interested in people analytics currently, if they want to maybe do some extra upskilling go on their own like learning journey, one or two people could do it together to be upskilled to the point where they can be building these data visualizations and really helping the rest of the organization get better use of their data. So I would recommend something like that. And yeah, I mean I would say that's, that's really your, your best choice, but then these people that are going to be upskilled, it's like they can work a hybrid role so they can stay in their job before, but you're going to have to of course give them more bandwidth. You're gonna have to lower their, you know, their workload down to 70% for example. So they can focus 30% of their time on upskilling themselves and starting to provide data for the company. And then hopefully if they enjoy it and they're doing well, you can transition even more and more and possibly modify that headcount of where they were their positioned before and modify it into a people analyst if that's the, the route that they wanna take.

Taylor McBride: Right. Ideally with this scenario, if these people are providing valuable data in their journey to, to get into this, maybe we can then justify the full-time role. That too.

Christina Stathopoulos: Yes, exactly. Hopefully so. Yeah,

Taylor McBride: And you can have that data all the time. So I had a couple questions come in. One, is it, you know, it actually kind of refers to a couple slides back, but talks about, you know, it can be so hard to focus on important data and not overwhelming audience and it can be really hard to throw away or ignore certain data because you think other things are more important. So how do you get over that feeling of, of feeling like you're maybe throwing away important information or missing out on something that you're not seeing?

Christina Stathopoulos: Well, first of all, I wouldn't say it's throwing away. I mean, I hope you're not throwing away in the sense of like literally deleting and trash leaving it behind forever because when, when I was talking about more like filtering through the data, so we're not eliminating it forever, I would hope not, but you do have to like concentrate on the message that you want to get across. And to do that you need to focus on the data that you need. So how do I get over this like overwhelming feeling and worrying that I'm, that I'm missing out on something else? Well, first of all, you're, you're, yes, you're ignoring some data this time, but you might, you might use it next time. So there's always a next time. But when I'm working through these projects and what I do is I always have in mind the, the final audience, so the final stakeholders, you have to imagine that if you go to like a, a business audience and you come in with all of these different data points and graphs and, and if you just throw it all at them, they are going to be overwhelmed.

Christina Stathopoulos: You're not going to come out of this with any of your goals achieved. You're, you're likely not going to inspire them, you're not going to invoke some sort of change in them because they're going to come out of it so overwhelmed. So really I've, I've always got top of mind, like my audience, how can I zoom in on this to make it as clear as possible, but also with the objective that, you know, the, the clear message that I'm trying to get across. So pretty much that's how I do it is by having the audience in mind trying to put myself in their shoes. If I consider all this data that I've got now, are they going to understand it? No. Then how can I cut it down? How can I do different filters or, or break it down into other pieces so that they can understand it.

Taylor McBride: Yeah, I mean I think that's something all departments struggle with is sometimes realizing that less is more, right? And if you were to capture every piece of data, you might just be overwhelming people. So focus on what's important and don't worry about some of the other stuff because the less can be more in, in a more powerful message. Another, another question that came in from Ryan. Are there common data use cases you've seen Human Resources struggle with.

Christina Stathopoulos: Struggle with? So actually let's, let's hold off on this one because on the next slide I'm going to talk about Human Resources use cases and then we can discuss, okay, cool. So we've been through the what and the how, but let's talk about the why. So why are we doing all of this? Some use cases, we've got all of our data, we've built maybe these nice visualizations to understand it and we've hopefully got our Human Resources team ready to use all of this valuable information, but why, what exactly can they do with it? So just to throw a couple of examples out there for you. First of all, of course to make better people decisions, make faster, more objective decisions around your people. So things like headcount planning and data informed recruitment of these new team members. And then as well you can improve collaboration, but in the sense of improving collaboration with other departments in order to create more efficient HR resources, HR trainings and development programs for all of the employees.

Christina Stathopoulos: So it's really by understanding your people more, you can better foresee their needs and you can respond accordingly through the trainings and resources that you offer. And then as well you can predict workforce needs. So by understanding your employees and also the micro plus macro trends, you can improve things like attrition rates, you can anticipate employee turnover, really just making, making you better prepared and agile. And then overall as well, you can just better understand employees, better understand your people, you can use your company data to spot risks and opportunities rapidly within the workforce. And then finally, I think this last one might be one of the most important, and it's that you can use your data and your visualizations to win leadership buy-in. So when you present your recommendations to leadership, if you back your recommendations with data a k a facts, then it's very hard to argue against them.

Taylor McBride: Oh yeah. Okay. Fantastic. So I mean, we've talked a little bit about the benefits of data visualization, but what are some of the like common limitations that these teams will come up against and how can they address those?

Christina Stathopoulos: Yeah, and this kind of kind of links to the, the previous question as well about the struggles with use cases too. So it's perfect. So I would say first of all that I'm clearly a a data visualization fan. A data fan. So I think the pros always outweigh the cons, but there are definitely limitations and risks that you have to have in mind. One thing that could be, if you're, and we kind of touched on this, but this could be if your team is not prepared to use data visualization properly. So if they are not completely data literate, then you risk data and visualizations being interpreted incorrectly and then the wrong assumptions can be made. So that's why data literacy is so important for everyone. It's not just for the analytics, the data science team, everyone needs this core of data literacy. And actually I run trainings, I've done trainings within companies where we do this upskilling of data literacy.

Christina Stathopoulos: And it really is because of this, if you want to empower people to make decisions on data, you need to make sure that they're doing it correctly and they're doing it confidently. So that's one risk. And then another limitation or or risk really is around, I would say bias and the one sidedness of data visualization, and I can explain this. So we are human after all and we all have our own biases. And these can be especially propagated through the use of data. So when we're talking about data visualization in particular, the creator of these data cuts and the visualizations themselves, they may be considering things in a one one-sided way because they're created by a certain person or a specific team. So it's possibly missing something important. And then the rest of the downstream audience will not have a chance to see what they're missing.

Christina Stathopoulos: And this is kind of linked to an earlier question that came in about how do you get over this overwhelming feeling that you're missing something and it's going to happen because you have to make these decisions to cut out some of the data, you have to do that, but you also have that risk in the end that you're, you're applying this one sidedness, this bias to the data by making all these decisions and these cuts before it makes it to the final audience. So that's kind of the, the balance that you have to make though.

Taylor McBride: Super interesting. So I guess going a little deeper there, you know, you want your analysts or your people team to be able to eliminate some bias from their visualizations. How can we make sure that the data as it comes in is not biased?

Christina Stathopoulos: Of. You mean like before the visualization or

Taylor McBride: Yes, the, the, the people data that's collected. Because back back to my last point, I guess not last point, but a couple minutes ago when I was talking about, you know, having, having a seat at the table and maybe feeling like you have qualitative data rather than quantitative data. Do you have any recommendations on making sure that the data is clean coming in?

Christina Stathopoulos: eah, I mean there's different, there's different ways of doing this and it, it depends on what point of like the data, I guess the data pipeline we are talking about. But of course trying to get the data as, as quantitative as possible. We want to stay away from like opinionated things. We want to have things broken down into numbers as well when we're talking about like avoiding the bias and things. So I think one way to help with this is to make sure that we're including other people in the decisions that we're making. So like personally, if I'm working on a, on a data project, I understand of course, course who my final business stakeholders are and I try to include them even earlier in my, in my data project process to get their views on the data. Although this is kind, can kind of be tricky because they're not working so close to the data, so you have to interpret it for, for them.

Christina Stathopoulos: But I think just involving other people along the process and not being so solo when you're doing it can help eliminate some of maybe the, the wrong assumptions that you could be making as an analyst. And you have to realize that you, you don't have all of the knowledge of the business and especially if you're an analyst, you're not sitting in the position of a, you know, the whatever, one of the sales managers. So you might need to pull them in to get their opinion on things, be open to doing that and be ready to be to, in this sense, you start acting like a data translator and this middle man, but you wanna pull them in and pull in their knowledge and cross-functionally really to involve others in the decisions that you're making with the data. That would be my, my biggest recommendation.

Taylor McBride: Yeah, especially love the suggestion of working cross-functionally. Yeah. Okay. So Christina, I, I believe that's all the slides we have for now. So I just wanna thank you for sharing all this insights. Really appreciate it. It looks like we have, I don't know, roughly 20 minutes or so for some questions. So as you guys have more and more questions, please bring 'em up and, and we'll start to tackle a, a couple of these and get through 'em. But first question here is similar to Ryan's, what, what are some of the biggest knowledge gaps in general that you feel like Human Resources has?

Christina Stathopoulos: Yeah, I think the, this is, for me the biggest knowledge gap is just data literacy. Like the basics, so, and I that I was talking about before, but the basics of understanding and making sure that you are interpreting the data correctly, that you're interpreting the visualizations correctly. And this, I will note that this shouldn't all fall on the, like Human Resources team, yes they need their basic literacy upskilling, but there's also some responsibility that falls on the plate of the, the people analyst or the data scientists that you're, that you're working with. So it's partly their responsibility as well to make sure that they are designing the visualizations with that Human Resources team in mind. So making sure that they're putting themselves in the shoes of the Human Resources team and they're designing, you know, everything that they're showing on their reports and their visualizations. They're designing it in a way that is very easy to understand, it's intuitive and it can't, it shouldn't be, it shouldn't be easily interpreted incorrectly.

Christina Stathopoulos: And again, this also plays some of that like bias And one sidedness I see a visualization and I think it's great and I interpret it in two seconds, but ask someone else and they might see something completely different. And that's because we, when we see images in general, we all have our own perspectives and interpretations. So trying to always have that audience in mind. And, and I will add here as well, having the audience in mind, one thing I do when I'm building a visualization or a dashboard is before like we launch something, I'll have beta testers from the audience. So let's say that this is the Human Resources team and I'm going to launch a report for them. I'll have some beta testers from the Human Resources team, I'll show them what I'm building, I'll have them involved in the process, I'll get their opinion and feedback and I'll have that in mind before I actually launch it across the entire Human Resources team. And you do this because you want to make sure that you're having the audience in mind and you're, you're designing it for them. You're not designing it for yourself.

Taylor McBride: Okay. Yeah. Great thoughts. So this question is from Jordy and it's all along, but bear with me cause I think it's interesting you mentioned that anyone can train to get better at data visualization, but experience shows that it's actually quite difficult to find someone that communicates clearly in general much less communication data early, clearly. Excuse me. If you were looking to hire a designer for data visualization, what strengths and skills would you be looking for?

Christina Stathopoulos: So this is a really good question and it is true that it's very difficult to find good communicators and especially you in this case, you want a good communicator who's good with data. So you're adding like this ne this extra level, right? Yeah, it's definitely difficult to find someone, you can do as much training as you want, but I guess there is some sort of like natural ability that they've got to have. But I still think almost everybody has it in them if they, if they really believe. But what's strengths and skills? So one of the most important is, is definitely communication obviously cuz data visualization is communication at its core. So someone who is good at communication, at communicating with people and then particularly someone who is good at communicating technical topics to different sorts of audiences. So someone who's good at taking something technical and communicating it to other technical maybe engineers. And then being able to take that same data, that same technical message and communicate it to the business side. This is what I would call like a data translator. In this case it's quite difficult, but if you can find someone who's good at it, it's, I don't know, it's invaluable for a company to have this. So I think something around this communication is incredibly important.

Christina Stathopoulos: Of course you need like the hard skills so someone who's able to, to work with the data, filter through it, clean it, model it, whatever it may be, and then build the visualization. So you might need whatever hard skills, whatever tool you're using within your company. But that can really be be learned as long as you take the time to learn the new tool. And then, I'm trying to think if there's anything, I mean as well, the design part is something that you can study quite a lot. There's lots of resources out there so you can learn like the gestalt principles. Edward Tuft is a really, is a, is a data visualization pioneer. There's different books and things that you can read from these people and videos. So I think the design element is something that you can learn if you take the time to study it.

Taylor McBride: Okay. It's not just pie charts and bar graphs, there's, there's other elements.

Christina Stathopoulos: Oh there's design principles that you should have in mind. Yes,

Taylor McBride: Of course. That actually reminds me of, I don't know the saying or I'm sure everyone's heard a million times, but like a true sign of understanding is being able to take a complex subject and then explain it to a child, right, I think

Christina Stathopoulos: Or that, yeah. Yeah, that's a good question that you can ask in interviews. By the way. It's good that you, that you said that. So take some subject like how would you describe machine learning to a baby and see how they answered that. Don't ask me that question

Taylor McBride: By the way. Okay, perfect. Quick question here from Ryan Anderson. Are there common data use cases you've seen Human Resources struggle with? We, we kind of touched on this a couple slides back, is there anything you wanted to elaborate on that? There's a few more questions. If you feel like you've covered all, you could.

Christina Stathopoulos: Yeah, I mean I think it, it hap I think there's all sorts of things that they're struggling with and I don't know as well, Taylor, if you, I know you've worked with HR companies, all sorts of companies. Have you noticed a pattern on your side that's like something recurring? Cuz you've definitely had a lot more like broader experience across companies. So I don't know if you've

Taylor McBride: Seen Yeah, I, I mean I, I feel like I see a lot of people who feel like the, the biggest gap they have is their data can be a little old. They're looking at turnover data, they're looking at surveys from a quarter ago, they're basing decisions off of exit interviews. And I think sometimes it, it makes people too late feel like they are behind and they're being reactive rather than proactive. And so being able to be early and on top of those sorts of things, I think sometimes has been a gap that I've seen on on. Would you agree with that or, or

Christina Stathopoulos: No? Yeah, no, no. Then that's, that happens actually, not just in HR, but probably anywhere that you want more real time data. But many times that's not, for some reason it's not available for you. You shouldn't be making decisions now based on what was happening a quarter ago. You should ideally be looking at data as close to today as possible. And then yeah, looking at exit interviews, it's a little too late when you're, when you're just, you should, you should be able to try as much as possible to foresee these things and understand, understand the turnover better.

Taylor McBride: Yeah, definitely. Yep. One quick question here. What open source data visualization tools do you recommend?

Christina Stathopoulos: So there's tons of tools out there actually you can learn pretty much anything you want and a lot of them are free. I use Google Looker Studio, which was formerly Data Studio cuz it's free, as long as you have Gmail, Looker Studio is free and it's a really easy to use tool. Pretty much anybody can do it. So that's a really good one, especially if you're, you're wanting to do something like, it's a quick ramp up. And also that obviously is free. There's no, there's no cost to get started. A lot of companies today are obviously using Power BI from Microsoft. Yeah. And then Tableau, those are probably the two most common. And then you can do like visualization within, if you wanna get into coding, so Python r there's packages, there's libraries that you can do it straight within there, but that of course requires coding.

Christina Stathopoulos: Whereas the other tools that I was speaking about before, Looker Studio, power BI and Tableau are a lot of drag and drop. So anybody can use it. You just have to learn. Yeah, I would say those. And, but there, and there's of course like all of the coding options. So I said R Python, there's D three, which is another very like well known, I think it's built through through JavaScripts, but it's a very flexible language for data visualization. You can build some incredible things, but it's a long ramp up if you're not familiar with, with coding.

Taylor McBride: Yeah, I can only imagine. Learn, learn a whole trade or profession in order to, to work on your data with sounds tough. Janine says in your trading sessions, what are the biggest mistakes designers make when trying to communicate data through design? What are, you know, your top three woopsies?

Christina Stathopoulos: Yeah, this is a really good question actually. I'm trying to think. The biggest mistakes, well for, I mean I think the number one biggest mistake is not having the audience in mind. A lot of times I will see people trying to create like these really elaborate complex data visualizations that they think look so cool, but then you go show it to the audience and the audience is like, what is this? What does this mean? Why is this line here? So I think a lot of times I'm seeing people overcomplicate things that probably would be fine in a, in a bar graph. So that's probably the most common thing I see happening. Another mistake, maybe improper use of color or not the, or not the best use of color that you can. So remember the example that I showed earlier where we, where we highlighted the, the fives with color, that's another, you can do this same thing within visualizations. If you want to really highlight like a certain bar, a certain part of the graph, there's ways to play with color to do that. So maybe not having color in mind could be another mistake.

Christina Stathopoulos: I think those are the two. I'm trying to think if there's anything other like big ones, but I think those are the, the two biggest things that I see. See.

Taylor McBride: Okay, awesome. Loved hearing those. Thank you. If anyone else has any questions, love to hear 'em. Otherwise we can probably wrap up now and, and get our last words in. But I wanted to I guess wrap up real quick by first Christina offering some parting words and then after that I have a couple things to to end and we'll go from there.

Christina Stathopoulos: Okay. Yeah, I did want to close with some words of advice to everybody who stayed tuned in until the end. And the, the words of advice I wanna give you, this is applicable for data visualization, but it's also from a wider perspective. I wanted to give you tips on communication skills because really in the end, data visualization is communication. So to be an effective communicator, you can follow what is called grace's, conversational maxims. I always have these in the back of my mind when I'm designing a data visualization. But also when I'm putting together a presentation, a speech, whatever it is that I need to communicate, you can always have these maxims in the back of your mind. And to give you a bit of context, it comes from linguistics. It's also called the cooperative principle. And the maxims are quantity, quality, manner and form. Or the easiest way to think of it, which is the way that I think of it, is to be brief, be true, be relevant, and be clear. So you should keep those in the back of your mind whenever you're trying to communicate something, whether it's through data visualization or not, this is applicable, but it can really help you get your, your point across.

Taylor McBride: Fantastic, thank you. Yeah, so I mean, to wrap things up on my side, I just wanted to thank everybody for being here. And I wanna especially thank Christina for taking the time to be our fourth shock wave talk speaker. This was super interesting to me. I I really love the conversation and, and we're really grateful that you're able to come and be with us. Special shout out to our team who's, who's running things in the backstage to make sure things run smoothly. Technical. Great job team. I also wanted to, you know, give a little plug for our Shockwave Talks In general, please follow us on LinkedIn or subscribe to our knowledge hub so you can get updates about all different areas of human resources and how we can all upskill in different areas to, to become better departments. And then last but not least, I also want to plug our actual software. If you're entire of employee surveys or you feel like the data that you're getting is maybe not accurate or not complete or you're only hearing from a small portion of your employees, love for you to check out our survey free People analytics tool. We have a group demo coming up in a couple weeks on December 1st at noon Pacific time, 3:00 PM Eastern. And we'd have, we have a little surprise waiting there for anyone who comes. So thank you so much everybody for coming. Thank you Christina and everyone take care. Thank you so much. Thanks. Bye.

‍