How to turn data into stories

Do you ever need to turn to multiple people or different systems to get data? Hi, I'm Cole from Storytelling with Data. And in today's mini workshop, that is the challenge that we're going to address as well as how to overcome it.

I should mention that we are broadcasting for the very first time from our new production studio at Storytelling with Data headquarters in Milwaukee, Wisconsin. [snorts] And it's been a bit of time since we've done an open to everyone live event like this. So, we weren't really sure what to expect.

The team and I were thrilled to see all of the excitement building around this. And I will say as we've been waiting to get started, I've been having so much fun monitoring the comments and seeing all of the places around the world that we have people tuning in from. Today we had more than 10,000 people register for this event.

And I just love the fact that so many people want to learn with us because we very much enjoy learning with you. I thought to do something fun that we would start out by recognizing the 10,000th registration and that was Johnny Weathersby who's joining us today from I assume sunny San Diego, California. Hi Johnny.

Uh we'll be following up with you to have you select some fun storytelling with data swag from our shop. I'm also going to stick in the mail for you signed copies of all three of our books. Speaking of books, I know folks are excited for the many more that we've promised to give away.

Hundred in fact. Stay tuned. We'll uh share the winners of those a little bit later in the hour.

For those tuning in live, you have the opportunity to participate throughout the session today. You'll do that by sharing your ideas in the chat window. I encourage you to put any questions you have there as well.

The team is monitoring that and we have some dedicated time for questions a little bit later in our hour as well. With that, let's jump in. Imagine that I am the HR business partner for my company's sales organization.

I've been invited to an upcoming leadership offsite and asked to give an update on the sales manager population from a people perspective. I decide to focus on the aspects that are relevant to headcount hiring, promotions, transfers and attrition. Now I am not the data person in this particular scenario.

So I turn to the people who are and it turns out it's different people because the data lives in different systems. And so I know that I'm eventually going to need to pull this all together into something that looks cohesive. So to try to make that a little bit easier, I provide a template to my colleagues.

This dictates things like fonts and colors. I even thought the size of the graph would be important, so I put a placeholder in for that. Take a moment and look at what my colleagues shared.

headcount, hires and promotions, internal transfers into and out of sales manager positions, and finally sales manager exits from the company. I think this is a good juncture to invite some interaction as we think about how we could pull these graphs together. I'm going to prime those tuning in live to get your chat ready.

And I want to know what you think about this. What is your reaction to this slide? If you had to describe how it makes you feel in a single word, what word would you use?

Let me know via chat. This is a common approach. By the way, we have four graphs.

So, let's simply put them together on a single slide. Sometimes we're even constrained to this when somebody tells us, "Put it all on one slide. Give me a comprehensive view.

" This unfortunately often leads to some suboptimal design decisions and a communication that might be dense with data but doesn't actually satisfy anyone. Now, I can see out of the corner of my eye the chat window is going crazy, busy, confusing, lots of clutter, boring, overwhelmed. These are not the sorts of reactions that we want to be prompting in our audience.

So, I'm going to suggest that there is a better way to communicate this data. Desperate data is a common challenge, but just because we start there does not mean we are destined to end with disperate data. Today, I'd like to invite you to accompany me on a journey.

Going to take that desperate data and start by turning it into some good graphs. We will take a couple straightforward steps to transition those good graphs into something great. and [snorts] then we are going to take a great leap forward and turn those great graphs into a stellar story.

Let's get started with good graphs. Two tips here. First is to cut the clutter.

Second is to make the details consistent. Let's start off with a conversation on clutter. I'll bring back one of those graphs I flashed in front of you a moment ago and ask you to help me decide what clutter we can eliminate from this graph.

And now I think of clutter simply as elements that are present in our visual communications that don't need to be. When you imagine that every single element we put on a graph or a slide, it creates density. It brings a burden cognitively to our audience.

Want to make sure all of those elements earn their place. Uh again, I see chat going crazy with things that people want to eliminate from this graph. Grid lines, labels, the vertical axis, uh shorten the names on the xaxis.

I think that is a fantastic idea. Uh the chart border. Yeah, a lot of people commenting on grid lines.

I also see a question posed from Martin. What matters? We'll get there.

Bear with us. But first, let's do some decluttering. Let's start with the easy ones.

I'll take away the graph border and the grid lines. It's always amazing to me how much those two steps alone do in terms of making my data stand out more. Next, I'm going to clean up my Xaxis labels.

And diagonal labels, you know, they maybe aren't the end of the world, but they also aren't great. They look sloppy. They create this jagged line at the bottom of our graph.

But worse than that, studies have shown diagonal text is about 50% slower to read than horizontal text. So, as I see a lot of people bringing up in the comments, we can simply shorten those to [clears throat] the abbreviation. also creates some nice clean structure along the bottom of our graph.

Next, and this is maybe more of a pet peeve than anything, but it just gets under my skin when the white space between the bars is bigger than the bars themselves. Can feel sort of visually jarring. So, I'm going to thicken up those bars.

And actually, one opportunity this affords me is if I want to keep those data labels, bear with me for a moment because I will get rid of them. Uh, but I can pull them into the ends of the bars. This is a really cool trick because this reduces the perceived density of what I'm showing without actually reducing any of the information.

But as many people are commenting, we don't need both our yaxis and every single data point labeled. So I can choose one or the other of those. And typically when you're making that decision, what you want to think about is how important are the specific numerical values.

If they're critical, then you can leave them there and omit the axis. If on the other hand, you'd rather people focus on the general shape of the data or comparisons across different data sets, then often times you don't want to clutter the graph with that and can instead preserve the axis. That's what I'm going to do in this case.

As some additional cleanup, I'm going to orient my titles at upper leftmost. This creates some nice visual framing for my graph. Also, it means that people hit how to read the data before they get to the graph, which is a nice thing.

When it comes to reading this, I can do some things to make that slightly easier. Make my yaxis title a little shorter and piffier. I'm also going to add some bolding to my title so it's a little more scannable.

I'm going to make just one more change at this juncture, which is to lighten things up by turning this data over time into a line graph. Let's take a look at where we started. Now, each of these changes on its own totally minor, but when you layer them together, these individual small changes that are easy to make happen have great impact.

Let's go back to that original view and ask for you to help me spot some inconsistencies. What inconsistencies do you see? Where are things different?

Where they could be the same? Those tuning in live can let me know via the chat window. This is another aspect that can make our graphs unnecessarily harder to interpret.

It also displays a lack of attention to detail. Taking a few minutes to make anything that can be consistent across similar views the same makes things easier for our audience. see what you're highlighting in chat.

The xaxis colors I see coming up a lot of time. Capitalization. Yes, it's different uh in a number of places there where it doesn't need to be.

Uh yeah, lots of great ideas coming up here. Font size and case, uh yaxis name, the legend in different spots. Yes, all of these things.

And again, each individual one minor, but together they create a less than spectacular feeling when it comes to how our audience interprets our work, which is not what we want. Let's just look at a few of these. I'll highlight them sequentially.

There a lot of inconsistencies. So, every single graph title is approached differently. Even though they all use my predefined font, they're different sizes.

There's a different case structure approach. even the words that people chose across the different titles uh varies. We can do a lot to simplify that because when things could be the same but aren't, it causes our audience to question why that is, which is not where we want them spending their brain power.

We want them spending their brain power to understand what we want them to see and understand and what to do with that. So, we can bring some consistency to our graph titles. While we do that, let's bring some consistency to our yaxis titles as well as many people have suggested.

All of these are some component of the sales manager population. So we can make those titles similar or the same across the various graphs. Months of the year though they run consistently from January to December across the graphs, they're approached in a slightly different way across both of them.

So you can just bring consistency there [snorts] as well as in the legend placement. So where the legend is present, it is in a different spot on every single graph. When it comes to legend placement, I'm a fan of labeling data directly when you can.

When you need a legend though, think about also orienting that at the upper left uh potentially under the graph title again so your audience hits how to interpret the data before they get to the specifics. Going back to the original Oh, colors. [gasps] Uh lots of colors are used here.

They were the ones I prescribed. Uh but they're not used very thoughtfully. We'll address that.

So going back to the original and taking these two steps together, we can move from cluttered, inconsistent, visually disperate data to clean and consistent good graphs. However, we do not want to stop here. When we only take away, people can feel like we've stripped things out and not added back value in its place.

And it turns out there are two simple steps we can take to those good graphs to make them great. We can focus attention sparingly and use words wisely. Let's take a look at how we can achieve that.

Let's look at one of these stripped down, decluttered versions of a graph and talk about how we could focus attention on one of these lines. Now, I've started out by intentionally pushing everything to the background, making it gray. This gives us the ability to achieve visual contrast.

And sparing visual contrast is going to be how we signal to our audience where we want them to look. See what's coming in via chat. I see a number of people talking about talking about color as a way to differentiate.

We absolutely can. Color used sparingly is one of our most powerful tools for directing attention. But there are other things we can do to create contrast as well.

Let's assume, for example, that we want to direct our audience's attention specifically to the higher line. This is the one that starts off the highest. It has clear peaks and valleys over the course of time and it also ends the highest.

What besides color could we do to that line? See, Johan says intensity uh line types comes up. We could think about making where we want people to look a dashed or dotted line.

See a couple of other votes for that line thickness. Ah, from Johnny Weathersby, our lucky 10,000th registration. Thanks, Johnny.

See a number of people suggesting that we make that line red. Ah, Neo suggests words or phrases. Those are definitely going to come into play.

There are a lot of ways that we can show our audience where we want them to look through sparing contrast. Let's take a look at some of those. Color is probably the most obvious one.

One thing I will mention with color is it's unique as a design aspect in its ability to impart tone or feeling on the things that we put in color. So notice here that blue feels positive, friendly, nice. Whereas if I simply take that same line and I make it red, now it feels like danger or aggressive, something bad might be happening.

So, we want to consider how we can use color and the tone that it can impart to reinforce what we want to get across and to make consistent things consistent visually through the similar use of color. We'll see that play out in a moment. Terms of other ways to direct attention to that hire's line.

As a number of people noted, we can make it thick, make the other lines thinner, or a combination of those things. Intensity is something else we can play with. We can make the higher line darker than all the rest.

Now, before I flip to the next one, which is position, we can't move the line around. If we have other graph types, sometimes that's possible to resort how we're showing the data. But here, the line is where it is because of the data it's plotting.

But we can ensure that it doesn't cross behind other data series. So if you direct your attention to October and the space around it, we see another one of those lines crossing in front of it. So we can just bring the higher line visually forward so that we don't have that issue.

Dotted lines stand out very much when other things are not dotted. I'm a big fan of dotted lines to express uncertainty. It's a goal, a target, uh an estimate of some point.

We could remove all of the other data. I'm sure that came up somewhere in chat, but I didn't see it specifically. This is always something we want to ask ourselves.

By the way, do we need all of the data that we're showing? When you consider eliminating data, however, be thoughtful of what context that you lose when you do so and make sure that that's an appropriate trade-off. On the flip side of this, we could show just the other data series and then have the hires line appear.

And that simple animation of it not being present and then becoming so garers attention. And a live presentation that can be a really useful thing to do. I saw comments for data markers and data labels.

Yes, if we put them everywhere, we might end up with a cluttered mess. But we can actually incite our audience to make specific comparisons when we are sparing and considerate about which data labels we include. For example, if I highlight just these peaks, we can start to say words about this graph.

We might say something like hires tend to happen most in the first month of each quarter. Those words are important. If that's what we want our audience to know, we should put them on the page or on the graph.

There was actually one prominent study recently that showed when you title your graph like this with the primary takeaway, people are more likely to remember that takeaway. The priming power of words is really, [sighs and gasps] really useful. And when we pair that with the sparing visual contrast, then we've done some really nice things for our audience, which is we've made it clear where to look.

And through our words, we've made it clear what to see. So you can imagine how we might do this for each of the other graphs. Rather than do that, however, I want to show you how we can take things a big step forward through story.

Uh, but before we get there, I want to share a few additional ways that everyone can learn with storytelling with data. We have just launched our 2024 public workshop schedule. Today you're seeing a sampling of strategies that enable us to communicate effectively with data.

You can learn even more in these sessions and we have a variety of them to meet your individual needs. Uh from the short punchy storytelling with slides focused on planning presentations and designing stellar slides. We have our storytelling with data classic workshop that dives deeper into content similar to what we're covering here.

uh making effective graphs, weaving them into action, inspiring stories, uh but goes quite a bit more in depth given the longer time. We also have a one-day master class that combines all of that great learning plus more on how you can deliver a stellar presentation. Those are in person and we have sessions planned in April in London and in September in Seattle.

And then finally, for those who want to learn even more in a longer uh format, we have our 8week online course and there are cohorts of that starting in January and again in the fall. I will mention for those tuning in live or watching this video later, you can use the code good to great. Uh, that's g o d t og gre a t at registration for any of these sessions for 10% off.

You'll find all of the details at storytellingwithdata. com/workshops. I'll also mention we are going to be rolling out an official scholarship program for all of our 2024 sessions.

So stay tuned for that. In the meantime, folks can register with that good to great code for 10% off. Also want to highlight our custom sessions.

If you want to organize or suggest learning for your team or organization, we offer private and custom versions of various sessions ranging from shorter inspiring keynote presentations and skill-building webinars to longer form workshops where we collect examples from your team ahead of time and use those to illustrate and practice the lessons covered. More info about these offerings can be found at storytellingwithdata. com/custom-workshops.

I'll also mention that for organizations who want to learn with us but are facing constraints. We also have a special program called reach. This is applicationbased.

So you can apply to bring lowercost sessions to your team. You can find information on that at storytellingwithdata. com/reach.

All [snorts] right, I'm almost done with the advertisement part of our session, I promise. Just want to draw your attention to all of the other resources that we make available. And like this mini workshop today, a great deal of the content that we produce is free and open to everyone from videos, our blog articles, podcast episodes.

We also offer ways to practice and exchange feedback in our online storytelling with data community. That's also where we start to get in some ways you can support us as well through premium subscription there or as I mentioned by booking a workshop for yourself or your organization uh reading our books. Actually, on that front, I'll mention that we have a couple of fun projects underway that are taking shape in this space, including something that those out there who both work with data and have children in their lives will appreciate.

Stay tuned for more on that front. I should also mention when it comes to books, we are giving away a hundred copies of my newest book, Storytelling with You, Plan, Create, and Deliver a Stellar Presentation. And we'll go to the slide where you can see who those lucky 100 recipients are.

I'll pause here for a moment. Uh congratulations to everyone who will have a book coming to them after the session here today. We'll say for everyone else, you can get storytelling with you or pick up any of our books at your favorite retailer.

Before I get back to our content, I want to just give a quick reminder that we have time set aside today for viewer questions. So for those tuning in live, please share your questions on any topic related to our content today or really anything related to making effective graphs and giving powerful presentations would be welcome. You can share those in the comment or chat window.

First, let's finish this up with a stellar story. And to make stellar stories, we want to do two things. First, weave multiple graphs together and secondly drive people to do something specific with the data that we share, enticing them [clears throat] to act.

Let's take a look at what this can look like. I'm Cole and I'm here today with a sales manager update and I want to encourage us to rethink how we hire sales managers in the organization. Just to set the stage, I'm going to be looking at our sales manager population and the aspects that contribute to headcount over time.

Look at this for the last calendar year aggregated together. So just directing your attention down to the bottom, that xaxis, I'm going to start with the beginning of year headcount. Then I will add in the additions to headcount, hires, promotions, transfers in to sales manager positions.

And then we'll take away the deductions to headcount transfers out of sales manager positions and exits from that population. This is going to basically be like a visual math problem where eventually it will yield the endofear headcount. We started the year with 317 sales managers.

The biggest addition by far was through hiring. uh hiring actually accounted for 2thirds of the growth to this population over the course of the past year. We did also have promotions and transfers in.

These count accounted together for that remaining third of increase to our sales manager population. We also had some deductions transfers out and I'll just bring attention to the fact that we had more transfers out than we had promotions and transfers in combined. We also had a great deal of exits over the course of the past year.

You'll note that we had more exits than we hired. So, taking all of this together means we're actually slightly down year-over-year on headcount from where we began. While at the same time, the overall sales organization has grown, meaning we have an increased need for sales managers.

Now, you might simply think, well, that means we should hire more. But I'm going to suggest a different approach. First, I want to share some additional detail on how these metrics play out over the course of the year because I think this can help guide our forward-looking strategy.

So, let's focus focus first on the additions to headcount. Going to go to a different structure here. We're still looking at the number of managers on the y ais, but now we have months over the course of the year from January to December on our x-axis.

67% were hires. Uh in other words, every two out of every three new sales managers came into the organization from the outside this past year. I'll just highlight the fact that we have the greatest number of hires starting the first month of each quarter.

That's largely due how we set our targets and sales incentives. You'll note that there are relatively fewer hires when it comes to the first month of the quarter in October and that is due to our annual promotion cycle that takes place then. So let's jump next to looking at that trend.

We had 47 promoted over the course of the last year to sales manager. 38 of those happened during our single annual promotion cycle. Now, I'll just mention when it comes to promotions, couple great things about them is we already know that folks getting promoted into sales managers are a culture fit with the organization in general and with the sales organization in particular.

But perhaps even more important than that, those promoted from within tend to stay in role and at the organization longer than external hires. On the flip side of promotions and some of the challenges there with the single annual cycle, we hear complaints every year from the existing leadership team. It's just all consuming in that time period.

[snorts] And also anyone who's just shy of being ready to promote has to wait an entire additional year before they become eligible again. And we actually lose many people during that time period. Before we move to transfers out and exits, take a look at transfers in.

So similar to hires, we tend to see more of those in the first month of each quarter. Similar to promotions, we get the benefit of culture fit and again longer time in role and at the company than we see with external hires. Now, you might be able to anticipate where I'm going to be going with this, which is if we take some of that energy that we've historically spent on hiring and use it instead to increase our promotions and transfers, I think it could be a win across several fronts.

Before we talk more about that, let's take a look at the deductions from a headcount. Those come in the form of transfers out of the organization and exits. So again, let's look at those over the course of time.

So I'm back to that view with uh sales managers on our y-axis and months of the year on our x-axis. We had 111 transfers out. I'm going to go ahead and layer on exits as well because these follow a similar trend over the course of the year.

January is when many of the hires from the prior year become eligible to transfer and as we can see many of them do. Also digging into exits in January and February we can see that many of these first pursued internal transfer but were unsuccessful. And again, many of these were more recent hires, meaning maybe they see it as a foot in the door, but aren't thinking about sticking around.

We also see a spike in both transfers and exits in November. Anyone know why that might be? That's right.

These are people who maybe thought they were ready for a promotion but didn't get it. And so we see exits coming through there. Just back to the big picture and taking all of this into account, it may be time to consider altering our people strategy when it comes to sales managers.

If you consider the effort it takes to bring in this many hires, we think that shifting some of that energy towards increasing promotions and transfers into the organization will help us reduce those red bars of sales managers transferring out and leaving the organization. Just to take that and put it into words on a slide, I'm recommending that we increase our efforts to hire from within the organization. That can take a number of different forms.

We could fasttrack internal transfers into sales manager positions, making that a fast and easy process. I highly recommend that we add a second promotion cycle in April, both to spread out the burden on the current team as well as give people line of sight to that next potential promotion. We could also consider introducing an approval process for offcycle promotions.

And I'm sure that you have ideas as well. Let's discuss and determine where we go from here. That was a great experience and in fact after going through all of that it is possible to get the key pieces into a single slide summary.

I wouldn't present this but it can be used as a follow-up to remind people what was covered or for those who missed the update. You may find it difficult to recognize now that this is where we started. We improved things quite a bit.

As I mentioned, just because we began with disperate data did not mean we were destined to end there. We started by turning it into good graphs by cutting the clutter and making the details consistent. We made those good graphs great by focusing attention sparingly and using words wisely.

And then we took a giant leap forward into a stellar story, weaving multiple graphs together and driving people to act. So the next time you find yourself facing desperate data, reflect on the lessons we've covered here. Use them to make great graphs and move beyond just showing data.

Make those great graphs a pivotal point in an overarching story. It's time next to turn our attention to questions. If you haven't already, I invite those watching live to please ask your questions in the chat window.

Randy has been here behind the scenes so far making sure things run smoothly. Randy, do you have any viewer questions for me? Sure thing.

So, uh, first we had a question way back that I made note of when you were talking about the different books and that question was from Panda who asked, "What's the difference among the three different books? " Fantastic question. So, I'll start with the original, that's the white book, Storytelling with Data.

If you're working with data on a dayto-day basis and need guidance for how you can turn that into effective graphs in terms of what type of graph to use, more examples on how you might declutter and focus and weave things together into a story. This is a great place to start. Uh basically goes deeper into a lot of things that we talked about today and with many more examples.

You'll find even more examples in Let's Practice. So, this book is structured chapter and lessonwise along the same lessons as the original book, but it is entirely exercise-based. And so, within each chapter, there are three sets of exercises.

First, there's practice with Cole, where I put forth a scenario that you're meant to think through and maybe approach on your own, but then I also show you how I would approach it. It's a way of getting insight into many more examples and corner cases and all the issues that come up when we're grappling with graphing and communicating data. There's another section of exercises called practice on your own which are canned sort of examples but without any prescribed solutions.

These are great for university instructors teaching from the books. uh we have I think over 500 or 600 now identified of instructors around the world teaching for our book from our books. So you can use those for additional homework or group projects.

Also great for the individual who just wants to learn more or for a manager of a team who might want to encourage that. And then the final exercise section within each is practice at work where it takes the concepts and really breaks them down into guidance about how you might take something you're facing in your job and apply the lessons. This is great for somebody who wants a handson way to learn.

And then I will say the one I'm most excited about at this moment in time is the newest storytelling with you which goes beyond the other two books and really gets into the important role that the individual plays when communicating whether it's data or anything. Because you can make a great graph, but if you can't talk about that graph or that data in a way that engages and gets people to want to listen and act, the beautiful graph or slide is going to fail. So I often get asked, what should I do next after I read the first book or take a workshop?

And you know, I want to I want to make even better graphs. But I would say don't worry about better graphs. Graph, good graphs, great graphs, that's good enough.

The next way to really advance yourself is to invest in yourself and how you present yourself, how you present your data, how you talk, how you engage. And so the new book really walks through that. Um, as well as the practical bits of creating and planning.

Uh so it takes you through getting clear on your message, understanding your audience, planning out your content in a low tech way, then goes through the technical aspects of bringing that low tech planning into your tools, going through things like setting up a template in PowerPoint to make things easy and consistent. There's an entire chapter on graphs, uh also chapters on words and images as well. And then the final section really dives into developing yourself.

So, I would say if you're debating which do I get, start with this one. All right. And there was a question of which format are those books in?

And actually, they're all in electronic format. And then storytelling with data. The white book and the yellow book are both available on Audible, read by the author, which is which is always exciting.

All right, we had another question. Uh uh many people are asking what tools do we use to make our graphs and criti also ask how do you animate your charts in those tools? Great questions.

Everything that we've seen today was done directly in PowerPoint. And I will say the majority of what the team and I do is PowerPoint or a combination of Excel and PowerPoint. mainly because these tools are pervasive.

Love the fact that anyone can pick them up and make a graph, right? There's no barrier to entry. Challenge is just that nobody really teaches us how to do this.

So, the kinds of lessons that we focus on across all of our work are those that are tool agnostic that can be achieved in any tool. So, when it comes to tools, I'm a fan of picking one or a couple and getting to know them well so that they don't become limiting when it comes to employing some of the things that we've talked about today when it comes to the individual questions of how did you do that in PowerPoint. So, for what we saw here, it's a lot of the same graph on different slides just formatted differently, which creates that animated feel as I flip through them.

And a great resource for you to turn to on that is the Storytelling with Data YouTube channel uh because we have a ton of tutorials and more coming uh and a lot of shorts as well that will show you what menu uh settings to go through when it comes to some of those formatting changes and how we actually go through and animate in these sorts of settings. So definitely recommend checking out resources there. We will also follow up with everyone who registered for the session today and make sure that we include all of the resources that we talk about here.

All right. In a related question, Diana asks, "What do you do when your audience requests that you continue to show them tables for everything? " The audience who loves tables is often feeling like their question of so what isn't answered.

And when that is the case, it feels like getting more data can be the answer. And so one thing I would recommend trying though, because if you simply say, you know what, tables aren't the right answer, I'm going to give you a graph instead, people will not like that because they tend to be change resistant. So instead of taking anything away, think about adding where you can say, audience, I still have your tables.

We can go through those. But I've done something different today that I think is going to help us have a better conversation or see something new or in a different light. And I've gone ahead and put some of that data in a graph.

And here's what we can see. And here's why this is interesting or important and how it's relevant for you. And what you'll find is over time as you start to develop both your own confidence and your audiences that you are highlighting the important things for them.

It will wean them off of this desire for the tables because again oftenimes people wanting tables it's thinking that more data is going to answer the question which means that their questions aren't getting answered currently. So if you can get more context and understand what they need, how they're making decisions, what inputs would be useful, that will help you curate from that tabular data a story like what we saw today. Also just look for instances where you are likely to be successful.

Uh so maybe starting a new project, you might try this instead of going against the grain of something that has already been living in a table. Just a few thoughts. All right, Brad asks, "Is data storytelling the same as data visualization?

" No, it's not. Uh, nomclature is an interesting thing because words get thrown around and come to mean different things over time. For me, data visualization is simply taking data, taking numbers, and turning them into pictures.

We can visualize data for many different purposes. We can do it in a business setting where we're after efficacy and the speed of transfer of information. Uh we can also do data visualization that is more artistic or interesting from an aesthetic point of view.

[snorts] Uh neither of those are wrong or right. They're just data visualization for different purposes. Data storytelling is not just the data.

That's where you are bringing in components of story. Uh so when we teach about storytelling in our work, we're really getting into it. What's the plot?

Uh where is their tension in terms of what matters to the audience that either isn't being satisfied in some way or something that could go wrong? How do we build that tension over the course of our data story, reaching a peak of climax and then having a falling action and a resolution? So really bringing structures of story into how we communicate because when we do that well, we can use it really powerfully to engage and get people to stick with us and get them to care, which is incredibly powerful.

But I will say data storytelling is one of those buzz phrases that gets thrown around when people maybe just mean put some words on a graph. Uh that's a step towards it, but there's so much more we can do. Couple of folks have asked how do you use branded or familiar colors in a graph and what do you do when you're restricted in which colors you can you can use or as as Liz says um what about you know when your audience wants to use red yellow and green and pushes back at the changes to more accessible colors and I will add something that Sophia added which is and what about us colorblind folks what do you do about them yes color as we've seen is an incredibly powerful tool in our designer toolkit, particularly when we use it sparingly.

Um, [snorts] so when there are brand colors that you can fold into how you're communicating with data, I recommend doing that can bring a nice cohesive look and feel to things. Just recognize because you have a ton of different brand colors does not mean you need to put all of them in your graph. So picking one or a couple distinct prominent brand colors and using gray elsewhere can often work for that.

When it comes to the stoplight question of the audience who wants the red, yellow, green, we do get into some colorblind issues there which might be one argument that would be useful for your audience is about 10% of western population experiences some form of color blindness which most typically is difficulty in distinguishing between shades of red and shades of green. I'd argue also that mostly when we use those color palettes, we're not interested in all of it. we're interested just in what's going well or just in what isn't going well.

So you could even think of highlighting those things sequentially uh instead of all at once. So the challenge is when everything is different, nothing stands out. And so that can be fine if you're using it to explore the data, but once you've already done that, you have something specific you want to communicate and somewhere specific you want people to pay attention to, then we want to use our color more sparingly in order to drive that.

I think we have time for one final question. All right, this last question is from the user handle an SS. Sounds very mysterious, but the question is a great one.

It says, "What is the best way to convince leadership that we need to incorporate storytelling in our comm communications? Could we say it was uh a better way to provoke thought or better position uh position us to make meaningful decisions and recommendations? " What are your thoughts on that?

This is a fantastic question and yes, all of those things. uh you know it's hard to point to ROI when it comes to investing in these skills, but I think the way that we see them play out is when it's done and you're finding that people are having better discussions. They're making smarter decisions because they're no longer asking questions about the data or asking for more data or trying to understand the graph.

they're able to quickly get to how does this new information I now have matter for the business matter for the important conversations and decisions that we're having and making and so the more you can build situations like that and point to their success. So I would say try out the things that we've talked today and that you'll read about in the books and see on YouTube and elsewhere. Try out the ones that you think are going to be the most useful in your work and try them out in instances where you are likely to be successful.

Uh where the risks aren't crazy big and people will be accepting because then you can start to build momentum. Uh [snorts] because the best thing is when people start coming to you because of your fantastic work. I've seen what you do when you're communicating with data.

can you teach my team to do that or can you do that for me as well? And that's how you get really great grassroots momentum with this stuff. So, it won't be successful every time.

Don't get discouraged. Keep trying. Look for places where things are successful and build on that.

So, we're out of time. We took the whole hour and I love it. I love the excitement.

I love being able to see chat flow through out of the corner of my eyes. So I just want to say a big thank you to everybody tuning in today. Uh this recording will be available.

It will live in YouTube so you'll be able to rewatch and point colleagues to it. Also just mention if you enjoyed this session, please let us know in the comments. Uh because I think if you do, we may very well do more of them.

And with that again, thank you for tuning in today. I wish you great graphs and stellar presentations.