Storytelling with Data | Cole Nussbaumer Knaflic | Talks at Google

please join me and welcoming uh Cole newp nli uh storytelling with data um for authors's at um Cole was on our team people analytics and people operations here at Google many many years ago and she led our relationship with sales and she was known throughout the team for her incredible expertise with presenting data visualizing data and making it actually speak to clients and to users um and lots of funny stories about Co one that stands out um is her feelings on pie charts um I put together analysis once that showed a distribution of a specific

population by level or something put it in a beautiful pie chart that I thought was amazing and Cole gave me very brutally honest feedback about how ineffective it was it actually showing the differences in the group so Cole I'm very curious to hear today if you still have those views on pie charts or if maybe they're a little bit more acceptable now I still have a pretty strong view when it comes to pie charts okay well we look forward to hearing a little bit more about that um so the way that this is going to

work is we're going to have 30 minutes of a lesson actually from Cole from her book um and then second we're going to have Q&A moderated by Tina M from people analytics and me Davey Nichols from people analytics as well so please again join me in welcoming Cole to her first stop on her book tour here at Google awesome so it seems so fitting for me for Google to be the first stop after the official publication of my book since this is where so much of it began I started at Google back in 2007 on

the people analytics team and people analytics is an analytical team in the people Ops organization here where the goal is to try to ensure that all the decisions about people employees future employees are datadriven now I had the opportunity of joining when the team was relatively small which meant that I got to work on a ton of cool stuff over the years learning about things like what makes a manager effective how do you build build productive teams what drives attrition now in 2010 we developed a program called base camp was an internal MBA like training

program within people operations and I was asked to build content on data visualization which was an awesome opportunity because I'd always been really interested in this space but this meant I could pause and research and understand why some of the things I'd arrived at through trial and error over time had been effective so as a note I can honestly say that Google for me was life-changing uh I didn't know it at the time but the very first time I delivered the data visualization course which was in at a people op offsite in montere in the

very first row sat my future husband uh so you could really say without the data visualization course here at Google I wouldn't have these or the third that's on the way uh super life changing but back on Google uh there was broader interest we were finding in the data visualization course so we actually ended up rolling it out across Google which meant I got the opportunity to travel to offices in London and Dublin and Zurich and train trainers and teach courses uh also got a chance to teach courses across a number of us offices including

many right here in Mountain View one of the things that was interesting for me was to see salespeople and Engineers sitting side by side in those courses came to real realized that the skills needed in this area were fundamental uh they weren't specific to Any Given role they also weren't specific to Google over time other organizations started reaching out to me wanting me to go teach their teams and their organizations how to communicate effectively with data so over the course of the past couple of years had the opportunity to work with hundreds of different teams

across many different organizations and usually this takes the form of workshops where I'll spend half a day or a full day teaching foundational lessons on communicating with data and oftentimes what I'll do is solicit examples from the group ahead of time and we'll go through the lessons and then we'll spend time talking about that group's specific examples and I'll go through my makeovers as one potential path that leverages the lessons that we've covered so that it might be cool to take you through a couple of these we won't go through them in a lot of

detail but just to give you a visual sense of what you can learn in the book so this first example is one from the philanthropic sector this was a foundation that wanted to start a conversation on shifting spending from non- initiative which is the big sort of cream colored segment at the top left to higher education that tiny blue segment so this case we change from the pyer if I already talked about My Views there to this right if we want to shift spending let's say we want to shift spending and start a conversation about

that use visual cues to draw our audien's attention to where we want them to pay it let's look at another example so this is one from an IT group who couldn't believe that people after looking at this graph weren't doing anything with this information why weren't they acting upon this information here it gets totally lost in the graph right there's no story to bring it to life now the backstory was if we stare at this long enough we can see there's a gap starting to form out on the right hand side where the number of

tickets coming in are exceeding the number that are being processed now the backstory was they'd lost a couple of resources they were underst staffed and they really wanted to hire a couple more people they couldn't understand why nobody was doing that based on this graph so in this case we changed from that to this again making that call to action clear annotating the context directly on the graph drawing attention to that Gap that's forming on the right hand side so I get one more of these this is an example uh from a small organization who

was recognizing that their Regional sale composition had shifted over time and wanting to have a conversation about what some of the implications of that were now this is an interesting one I've used this a number of times in workshops and when people flip to this graph there's often a sort of immediate negative visceral reaction uh which is something we want to try to avoid in our audiences and now I can't imagine all of them were Packers fans like my husband these are sort of Broncos colors right um but rather this graph was unnecessarily confusing now

the beautiful thing here is there are some clear takeaways articulated at the bottom it's just almost impossible to know where to look in the data for evidence of those takeaways so in this case changed from that to this making the focus on the change tying the text directly through to the data both through proximity and similarity of color now one thing to note is these examples they cross many different Industries mentioned before these skills they're not role specific they're also not really industry specific um rather they're foundational and over time through all of the workshops

that I've taught over the past few years I've codified these lessons and that's what ultimately led to this my book uh and I'm super excited to be able to share with you today a couple quick lessons from my new book storytelling with data so we're going to talk today about two key lessons first off focusing attention and secondly telling a story I want to draw one important distinction at this point which is the distinction between exploratory analysis and explanatory analysis so exploratory analysis you perhaps start off with a question or hypothesis or just digging through

your data trying to understand what's interesting what can I learn about this data that somebody else might care about once you've identified that interesting thing then we move into explanatory space that is where you have something specific you want to communicate to somebody specific it's this latter space we want to keep in mind uh today and when it comes to explanatory analysis these lessons become more important than perhaps any others first off thinking about where you want your audience to pay attention and doing uh things on purpose to make that happen and then secondly never

simply showing data but rather making data a pivotal point in an overarching story so we'll talk briefly through each of these first off focusing attention I can recall a time at Google where I was working on the Project Oxygen study quick show hands how many people are familiar with Project Oxygen most people in the room so Project Oxygen was a study led by my colleague Neil Patel and the goal really was to try to understand on a mathematical statistical level what makes managers effective one of the challenges that we encountered was after the study was

done communicating the results of it to a very mixed audience where we had both Engineers who had a great desire for detail they wanted to understand the methodology they wanted to be convinced of the robustness of the analysis at the same time we were also wanting to communicate to sales managers for example who were mostly less concerned with the methodology and more concerned about what's in it for me how should I act differently based on this and so what we found was by really being careful about how we focused attention we could preserve a lot

of that detail but push it to the background and make the metap point pop out so that it was clear let's talk a little bit about how people see to get into more of this how we focus attention so here's a super simplified picture of that process on the left hand side you have light refracting off a stimulus this gets captured by our eyes we don't fully see with our eyes rather most of what we think of as visual processing takes place in our brains now in the brains there are a few types of memory

that are important to understand as we're designing Visual Communications talk about one of them today which is iconic memory iconic memory is super short term it's shorter than short-term memory and information stays there for fractions of a second before it gets forwarded onto our short-term memory the really cool thing about iconic memory is that it's tuned to a specific set of what we call pre-attentive attributes so let's actually pause here and do a quick exercise so in a moment going to put a bunch of numbers up on the screen what I'd like you all to

do as fast as you can is count the number of Threes that you see I got it ring out threes when you know the answer shout it out it is a race you would like to win Ready set go all right six is the correct answer this took a bit of time though right you physically read through these four lines of text look for three which is kind of a complicated shape watch what a different exercise it becomes when I make one tiny change don't have have time to think don't have time to Blink suddenly

there are six threes in front of you this is so apparent so quickly because I'm leveraging your iconic memory I'm using the preattentive attribute of intensity of color in this case to make the threes the one thing that stand out as different from the rest now this is hugely critical because what this means is our pre-attentive attributes if we use them strategically can help enable us to get our audience to see what we want them to see before they even know they're seeing it here are the attributes I won't read through all of these but

notice as your eyes scan across the screen how they're just drawn to the one within each group that's different from the rest you don't really have to consciously look for it now one thing to know about the attributes is people tend to associate quantitative values with some but not others for example most people will consider a long line to represent a greater value than a short line it's one of the reasons uh bar charts are intuitive for us to read but we don't think of hue for example in the same way if I ask you

which is greater red or blue it's not really a meaningful question this is important because it tells us which of the attributes can be used to encode quantitative information and which should be used as categorical differentiators now as you can perhaps imagine preattentive attributes become huge tools for focusing our audience's attention when it comes to visualizing data so here's some sort of generic data from our annual customer survey we can see how we've fared across a number of Dimensions notice how without other visual cues this becomes very much like the count the 3's example again

we have to look at this data read through it figure out what might be important to pay attention to whereas if I'm the one communicating this data I should have already done that for you in which case I can use some pre-attentive attributes perhaps paired with some explanatory text to draw your attention very quickly to one part of the story right pricing convenience we're doing awesome here let's pause and celebrate our success or I can use the same broad strategy to draw your attention totally different place in the data but we're struggling when it comes

to relationship and brand how can we positively impact these areas now there's a test I like to employ in trying to figure out whether you're using your preattentive attributes strategically and that is the where are your eyes drawn test where you look away from your Visual and look back at it or close your eyes and look back at it and just notice where your eyes land first because it's probably where your audience's eyes will land as well so I thought we'd do this with a series of pictures and just talk about the implications for our

visual designs so I'm going to put a series of different pictures up there when I put the picture up to shout out where your eyes go first ready where do your eyes go first here stop sign right you almost can't look anywhere else at the onset because it's bright it's red got these big B bold capital letters It's outlined in white which sets it apart from the background want to think about how you can use some of those cues when you're visualizing data to draw attention as well let's do another one where do your eyes

go here yeah if you're like most people they go to the Sun but if you're like me when you try to look at the sun you get this plane sort of tugging on your peripheral vision or if I try to look at the plane I can see the sun sort of wanting to pull me that way so just be aware that when you're emphasizing multiple things in a graph or on a page this tension that can be created in your audience how about where do your eyes go here this depends a little bit perhaps on

where you're sitting in the room a lot of people will be drawn to that perennial sale sign in bright pink because it's bright because of the black bold lettering on it and then most people from there will continue down and rightward and that's because without other visual cues we typically start at the top left of our page or our screen and do zigzagging Z's across so in this case that draw of The Perennial sale was strong so we started there and then continued on that Z downward and to the right notice that means we missed

whatever was happening in the top left quadrant and maybe that second and third quadrant as well so going to be thoughtful about the overarching designs of the pages on which your data visualization sits and take that into account just a couple more of these where do your eyes go here everywhere and nowhere all at the same time right colorful is an awesome goal for a birthday party color is not such an awesome goal when it comes to visualizing data when we make so many things different we have a lot of stuff competing for our attention

which actually makes it really hard to look at any one thing check out the difference in how your attention is uh focused here versus here right with the red balloon the one thing that's different on the whole page we almost can't not look at it that is the power of color specifically used strategically take a look now at an example from that Project Oxygen study that I mentioned at the onset this is what one of our original slides looked like it's been genericized a bit we can see our main takeaway at the top some elements

of job satisfaction are more sensitive to manager quality than others we've got some categorizations here and then our data at the bottom here we're not using color so strategically here colors used as a categorical differentiator they're originally we've taken them off here but we're categories along the bottom you can think of those like Google Guist categories things like Career Development and Performance Management and culture just not necessarily how we want to be using our color so in this case our redesign looked like this the graph is mostly the same the contents of the page are

pretty much exactly the same we've just rearranged things a bit and used our pre-attentive attributes color specifically more thoughtfully to really draw our audien's attention to where we want them to pay it while we draw attention we also want to think about embedding our data in story so by way of a Google anecdote can recall a time when I was working with one of the junior analyst on our team and she had just finished analyzing Google gist results results from the annual employee survey for a given part of the organization and was getting ready to

communicate those results to the leader of that team this particular team had been struggling in a lot of places the scores weren't great so there was some sensitivity around how that message should be delivered and the deck at that point was page after page after page of the standard report no story and little narrative to tie it all together would have been very easy for the leader of that group to say oh that's interesting and move on to the next thing that would have been a failure so what I had the analyst do was set

the deck aside and tell me the story tell me what you learned when you're analyzing this data and when we did that the articulation of the story was super powerful there were clear areas for improvement and she knew exactly where to focus action to achieve that Improvement this we could use to light a fire under the leader for that team so it's a good example of how data without story isn't always so meaningful but the story can help bring the data to life so when I think about how we can leverage that power when we

are communic ating with data every time we're doing it here are some facts on a slide go ahead and read through these anybody recognize what we're looking at here what story is this Red Riding Hood right but facts on a slide are not so compelling or memorable if I ask you a day or two from now what distance was it from Red Riding Hood's house to Grandma's or what time did Red Riding Hood get there these aren't likely facts that you will have committed to your memory stories on the other hand are memorable that many

people quick show hands know the story of Red Riding Hood pretty much everybody in the room we'll do a quick thought exercise here we'll just take about 15 seconds close your eyes or stare up at this screen and I'd like you to recall for yourself the story of Red Riding Hood thinking specifically about the plot the twists and the ending 15 seconds here quick show at hands how many people were able to get to the high level story people are always a little afraid to raise their hands at this point for fear of what might

happen next uh let me bear with me I I'll tell you the story that resides in my head so Red Riding Hood sets off she has a basket of goodies she's going to Grandma Grandma's not feeling well and on her way she encounters a wolf the wolf is able to extract from her where she's going and realizes that if he's patient he can have not only one dinner but two so he races ahead to Grandma's eats Grandma uh and dresses up in her clothes gets into her bed Red Riding Hood arrives and S something is

arai and goes through a series of questioning with the wolf posing as grandma oh Grandma what big your how big your eyes are oh Grandma how big your ears are oh Grandma how big your teeth are to which the wolf replies all the better to eat you with uh so wolf actually eats Red Riding Hood as well uh but then guy with an X shows up uh cuts open the wolf's belly and the Wolf had eaten grandma and red in such haste that they're fine they come out um and interestingly if you go back to

the Grims original the wolf doesn't die then they actually fill his stomach with rocks and sew him up so that when he wakes up he drops dead I think it's a warning story in uh you know go straight where you're intended to go don't talk to strangers and so forth uh but what does this tell us about what we're here to talk about today so for me stories like Red Riding Hood are evidence of a couple of things first off is the power of repetition when you consider it's probably been some amount of time since

you've given much thought to the story of Red Riding Hood and yet over the course of time you've perhaps heard that story a number of times read the story of number of times maybe told the story of number of times there's something that happens with that repetition of use of hearing and saying and reading things multiple times that helps form a bridge from our short-term memory to our long-term memory the other cool thing that stories like Red Riding Hood illustrate for us is this magical combination of plot and twist an ending that enable things to

stick with us in a way that we can later recall and retell to somebody else so when to think about how we can leverage these powerful Concepts when it comes to the stories that we want to tell with our data to get those to be something our audience will remember in a way that they can later recall and retell to somebody else so when we think about the components of the story we want to think back to those same things that we talked about with Red Riding Hood the plot The Twist the ending the plot

becomes what context is essential for your audience what do they need to know in order to be in the right frame of mind for what you're going to tell them then the twists what's interesting about the data and what it shows by the way if there isn't anything interesting about the data don't show the data you're onun the risk of losing your audience's attention for when you do have something important to say with it and then finally the ending the call to action what do you want your audience to do my view is we should

always want our audience to do something and we should be working to make that something as clear as possible because if we simply show data as we saw in that case with the Google gu deck it's easy for our audience to say oh that's interesting and move on to the next thing but if we ask for Action our audience has to respond to that and even if they disagree it starts a conversation and it's a conversation that may never happen if we simply show data let's take a look at an example so in this scenario

imagine that you just wrapped up a summer learning program on science the goal was to get kids excited about science we have some survey data from a survey we gave before the program on the left and after the program on the right where children could classify their interest as board not great okay kind of interested and excited give you a moment to take this in and then we'll talk about it how's it feel comparing segments across two pies not so great right the only thing worse than a pie for me personally two pies especially when

you're trying to compare across them because if anything changed in the data which it should have if there's something interesting you're trying to say the pieces are all an entirely different place over there on the right so you always want to think about what do you want to allow your audience to compare how do you align those things to a common Baseline and put them as close together as possible but check out what happens if I talk you through the narrative so going into the program the biggest segment of students this 40% in green felt

just okay about science maybe hadn't made up their minds one way or the other whereas after the program a really cool thing happened that great big 40% shrunk down to only 14% now there was a little bit of movement in the negative Direction bored and not great went up a percentage Point each but most of the movement was in the positive direction wherein after the program nearly 70% of kids if we add together that purple and teal segments expressed interest towards science this is a successful program we should continue to offer it notice how with

a strong narrative I can actually get away with a kind of crummy visual the alternative does not hold true I can have the most beautiful data visualization in the world and without a compelling story to go with it to make my audience care about it to make it something that resonates with them that sticks with them I run the risk of that beautiful data VIs visualization falling flat so it's not to say we shouldn't spend time perfecting our data visualization but rather to underscore the importance of story and now nirvana in this stuff is reached

when both are strong you have a powerful story and an effective uh visual to back it up so this case we could end up somewhere like this exposure to science excites kids bit of background our call to action let's keep offering this then we get down to the data how do you feel about science beforehand hand most kids tied through both color and proximity to the data point that is evidence of that point most kids felt okay whereas after the program we get this pull to the right hand side where kids are feeling interested they're

feeling excited about science that's the kind of story that we want to create for our audience that's the kind of the way we want to be able to focus attention for our audience so those are the quick lessons I have to cover here with you today wanted to give you a quick sense of how they fit in with the rest of the content in the book so I've listed out the chapters here uh chapter one starts off with a lesson on context having a really clear understanding of who your audience is and what you want

them to know or do before you really spend a lot of time creating visuals or content in chapter two I talk about different types of common displays used to communicate business analytics and go through some use cases and examples of each the third chapter is all about about clutter getting comfortable identifying the stuff that's there that isn't adding information to our visuals and stripping those unnecessary elements away fourth chapter is on focusing attention what we looked at today is just a small subset of much broader content that's covered there fifth lesson is on thinking like

a designer talk about how you can leverage some concepts of traditional design things like affordances and accessibility and building acceptance with your audience when it comes to visualizing data chapter six look at a number of what I'd consider model visuals and I talk about the design thought process used to create those chapter S is focused on story and again what we've looked at today is just a small piece of that chapter 8 pulls all these lessons together goes through a single example from start to finish showing all of these in coordination uh chapter 9 covers

a number of case studies on common challenges faced with when visualizing data and then the final chapter chapter is a wrapup recap what's learned talk about where to go next uh and discuss building storytelling with data competency in your team and in your organization so before I turn us over to Q&A I wanted to say a quick word on Google so when I joined Google in 2007 I was the Envy of all my friends and one thing that's been really cool to see as I've talked to so many people people at so many different organizations

is this fascination with Google still exists today so one word of advice from a former googler to current googlers is to really just appreciate everything that Google has to offer take full advantage of all of the opportunities that you have here I like to think that I did and it got me to a really fantastic place so with that I say a very big thank you Cole thank you so much this was so fantastic I've been exposed to your ideas for such a long time now um since 2009 or since 2010 I attended your training

can I not put my feet up there um I attended your trainings and I read your book and I just listened to this um fantastic introduction to your book and I still learned something new and something different sticks with me every time it's like one of those good movies that you keep keep watching over and over again so um we want to open up the next 30 minutes with Q&A so um I have a microphone so I'm going to be uh walking throughout the room to see if there are any questions in the room if

you have any questions for callon VC please email Daniel kuy at um I did already receive one question while you were talking and it is not about the book but the question is from San Francisco um so the question was what is life outside of Google oh life outside of Google so it's rough right you have to do things like make your food yourself go to the grocery store no um life outside of Google is good um but it's sad a little at the same time I think what I miss most about working at Google

hands down were the Fantastic colleagues who I had uh sort of right there when I was working here and when you're working on your own you you miss that sort of sense of camaraderie that you get when you have a team around you so I work with other people but it's always sort of a person here or person there there's not the same sort of energy that you get by being in an office on a daily basis with the people with whom you work um so that's one thing to keep in mind uh when you

venture to the outside um if you're going out on your own um what inspired you to actually write a book you've been given so many workshops um what yeah great question so I think for me it was about being able to bring some of these lessons to a broader audience uh I love teaching on this uh I get really excited about it and I uh like to see the excitement that sort of builds in other people uh but I can only teach so many people you know it's just me uh so being able to write

that book means it can sort of be out there for anybody to pick up uh and especially for people who may not otherwise have an opportunity to go to one of the workshops um it's nice to be able to put the lessons out there hey cool um so uh my question is about interactive visuals um I would guess that in 2008 or 9 when you were starting mostly visualization meant a static thing on a piece of paper and now uh like New York Times interactive visuals uh D3 there's so much interactive I'm curious about how

you think about uh interactive visuals in general and and particularly when you think they're most appropriate or any lessons about interactive visuals yeah great question David so I tend to focus in you know the examples that we saw here and in the book on static visuals when you have a specific story you're trying to tell how do you get get that across your audience in a way that's going to be effective um that said there's also certainly a place for interactive visuals uh one thing I would caution with interactive visuals is just questioning that assumption

that your audience wants to dig I think sometimes we think our audience wants to dig more than they do or sometimes they even tell us they want to dig more than they may actually do and so one way I've seen done of marrying the two is to have that meta story and to call that out and to highlight it and to put it in words but then also allow that interactivity for your the audience who's going to be inclined to dig to be able to do that and the New York Times is a great example

of that right because they'll have a couple headlines that they pull out of you know here are some of the meta interesting points you know are you looking to rent or to buy or you know some of these different interactive visuals that they've put out over the years um but then they also have all the data there for you to be able to play around with as well which can create a different sort of Engagement with the audience as well which is one of the really powerful things about interactive visualizations yeah great question hey Cole

hi uh so kind of building off of David's question I think another common uh way that people look at data that's different from interact or I guess it's a little more interactive um but different from a static is dashboard so we create these big dashboards and um sometimes they're useful sometimes they're not but they have a lot of data there and how do you think about it when it's it's I guess a little bit more challenging because you don't necessarily know ahead of time what the story is going to be and so how do you

still capture that story aspect when you just don't know what it will end up being yeah dashboards are sort of a specific uh different use case as well and when it comes to dashboards if you really are wanting to allow your audience to dig and come up with their own stories then you actually want to stay away from some of the stuff that we talked about here today because as soon as you use color especially to draw your audience's attention to one story actually makes any other potential stories much harder to see uh so dashboards

you want to think about designing uh in Grays when you can or using color only um as a categorical differentiator not as a visual cue that says draw attention here uh dashboards for me uh fit you know I talked about this distinction between exploratory and explanatory and for me dashboards fit more in the exploratory but I think often get sort of tried to be used for the explanatory where a dashboard is sort of you know I've got all of these metrics on a single page on a single screen I can scan through them I can

look for where are things in line with what I expect where are they not in line with what I expect and then pick out you know hey something might be interesting there and then dig in on that and when you can find the interesting thing then instead of using the dashboard to communicate that my view is you should do the stuff we talked about today and take that interesting thing and make that the focus uh and not necessarily confine yourself to the dashboard for that because the challenge is in trying to communicate to an audience

with a dashboard is by showing them so much it's hard to draw attention to one particular place uh first of all thanks for such a compelling presentation it was incredible awesome thank um my question is in addition to reading your book what are some resources that you you could recommend for us to explore data and kind of create those effective presentations and like on the flip side like what are your favorite tools like do you use like Tableau or or sheets of course yeah great question so yeah when it comes to getting inspiration uh for

visualizing data there are there's an massive amount of content out there on the web you Google it and you'll come up with some great things um some really fantastic blogs a lot of great work um there's Al a lot of not great work and so you sort of want to have a lens on of what is effective why is it effective but then also when when do you see things that aren't effective and right just because it's put out by a recognized publication doesn't necessarily mean that um that they're visualizing data well um but some

sources of consistently good work places like we talked about the New York Times uh the Wall Street Journal National Geographic does some really great um data visualizations uh when it comes to Tools in particular ular uh everything we looked at today was Excel uh which is what I find myself using primarily because it's what most of my clients use uh Tableau is certainly increasingly popular my view is you should find a tool pick a tool and get to know it as best you can so that it doesn't become a limiting factor in applying some of

the things that we talked about today any tool can be used well and any tool can be used poorly uh and the cool things about the lessons we went through today and the lessons in the book is they're not specific to Any Given tool they're tool agnostic there are foundational principles that you can apply in varying extent in any tool I had a question just around what you have seen or if you've seen any particular learnings as regards to localization or you know when you talked about iconography towards the beginning of your presentation I know

that's so different that we see in street signs depending on what country you're in curious if you've seen that at all with the data visualization side of things including maybe with color that yeah yeah and color is the place that U comes to my mind immediately with that question question uh because one thing to be aware of color in particular has this unique ability to impart tone and sort of inight emotions and so you always want to think about how you're using color and what sort of tone you want to set whether it's in a

graph or in uh the broader communication that uh contains that graph and use color to reinforce that uh but on that note one thing to keep in mind is that different cultures associate different meanings with different colors so depending on who your audience is who you're communicating to that's something to take into account David mckenas has its uh beautiful uh sort of visualization that's at the same time interesting tool for visualizing data which is it's called colors in culture and it's this big color wheel and colors in that case is British he's British Co c

o l o u RS um but it shows you the connotations that different colors have in different cultures uh so it can be a very useful tool uh if you are communicating to an international audience uh his site is informationis beautiful.now to weave all of those disparate pieces together because as we talked about that's one way of really making it memorable for your audience uh a specific strategy you can use depending on the situation is something similar to what we looked at with that um generic bar graph from the uh customer survey where if you're

showing a bunch of data and you want to be able to talk your audience through it but then focus on one specific thing at a time you can start off with just the data or even just a blank graph sometimes that has the axes labeled and titled but no actual data explain to your audience what they're going to be looking at then you layer on the data and then you maybe use color or another visual cue to draw your audience's attention to one part and talk about that then draw attention to another part and then

talk about that is a nice way of being able to build familiarity with the data uh with your audience as you talk through it and then also Focus attention really specifically within that broader data set uh when you have specific things you want to say about it great question then let me ask you uh one um your opinions about green I noticed that you didn't use any green on the slides oh interesting point so that was by accident probably more than intentional uh although back on the topic of color one thing you want to be

sensitive to is color blindness so roughly 8% of men and half a percent of women experience some form of color blindness uh most typically that manifests itself as difficulty in distinguishing between shades of red and shades of green which means you want to in general avoid using shades of red and shades of green together or if you want to leverage that connotation right green it went up that's good red it went down that's bad you can do so just make sure you have some additional visual cues there make the numbers also bold put the plus

or minus sign in front of them do something else to set them apart visually so you're not inadvertently disenfranchising part of your audience personally I tend to do my designs mostly in Gray and then use blue really sparingly to draw attention I like blue because you avoid the color blind issues also prints well in black and white if that's a concern um but that said blue certainly not your only other not your only option and you want to think about so we talked about the tones of different colors you want to think about brand colors

and all of these things and how those can fold into how do you how you visualize your data Cole I just want to point out that um even though you haven't been at Google for a number of years you're still super influential and I see a lot of great slides that are blue are gray and I for one have a delight in cutting all the Clutter you know and I think it's it's because of you so thank you so much for your contributions um one question that we had for you um we've talked a lot

about the what in the book I'm also curious about the how what was the most difficult part of writing this book I mean it was was it a bigger challenge that you had predicted like what was what was super Difficult about it yeah that's a tough question uh so I tend to be very organized and very structured so I once I decided I was going to do this I set out and I made the plan of here's what each chapter is going to be here's the timeline set some sort of aggressive timelines with my publisher

uh so I think really the hardest part was time right because actually physically writing and creating the visuals takes a lot of time as you saw there are um couple small people who live at my house and um uh yeah so time is precious uh so trying to fit it in between all of life's other things uh was challenging at times but I think overall worked out really well what's one thing you see people doing consistently wrong is say there's one message you want the audience to take away what would it be well I'm going

to Parlay that into two um things because I can do that uh so we've talked about color a bit but the lowest hanging fruit uh typically when I'm working with different organizations is being thoughtful in their use of color uh I think when it comes to communicating with data you never want to use something to use color to make something colorful but rather color when used sparingly and strategically can be one of your most powerful tools for drawing your audience's attention to where you want them to pay it um so being intentional in your use

of color would be one big tip the other we talked about this would be to never just show data always have a story and articulate that story in words either through your voice over or you know if it's on a slide or on a graph through physical text on that graph so that your audience isn't left guessing what they're meant to get out of it but rather you've uh put that work there for them um Cole you give us a preview on focusing attention and telling a story but I'm curious of the other other eight

chapters of all the 10 in the book what was your favorite um and why interesting question uh I think for me my favorite was actually the chapter on storytelling because for me that was the one that it was harder than the others uh I go the book goes much more in depth on storytelling than I've historically gone in the workshops so for some of the chapters it was they were pretty easy to write because it was mostly just writing the words that I say in the workshops but the storytelling chapter was not like that at

all I paused and I did a lot of research and I tried to organize it one way and then realized that wasn't working tried to organize another way so it was a lot of going back to the drawing board and trying to figure out how do the pieces fit together how can I weave it together in a way that's going to be compelling for people reading it be understandable for people reading it um but I actually I'm really happy with how that one turned out so I think that's probably my favorite chapter your target audience

who do you think your target audience is it's really anybody who has a need to communicate with data so whether that's working with data on a daily basis or less frequently and the concepts that we talk about or that I talk about in the book they the examples are specific to data for the most part but really it's anytime you need to communicate visually to an audience and a lot of it goes back to really thinking about who your audience is and how you want them to use the information that you're putting in front of

them and then just designing thoughtfully with that in mind Cole one question um that kind of came up in the audience is like what are other resources that are available what are other experts out there so expanding on that question um somebody like an Edward Tucky do you talk to people that are like known for being data visualization experts um and I'm really curious to hear like do you disagree with them on anything or have you like ever had like a data Vis like battle out or something great question um and actually we'll come back

to pie charts on this question yeah absolutely data viz is it's a really fun Community it's a relatively close-nit sort of the the main players all sort of know each other or know of each other and we have some correspondents and one of the things that's really cool is that there's really a lot of open sharing right because the goal is to make everybody better every body more effective at this stuff um but one particular disagreement uh so Robert kosara is uh one of the main data Vis visualization researcher guys uh at Tableau and he

was actually one of the reviewers on my book and he disagrees that pie charts are inherently evil uh and so he and I have had some uh decent debate on this um you my view is relatively strong that you know pie charts they just you can say some general things right the segment big the segment's small but you can't really say like how much bigger how much smaller answer some of the more um uh specific questions H whereas his view is a pie chart absolutely has its place it is the most effective visual for communicating

part of a whole uh but people often misuse them and use them to try to do other things outside of that um so his view is rather than banish them completely let's teach people how to use them smartly um I disagree but we've agreed to disagree and we're on good terms it's awesome Cole You've conducted so many workshops on this topic have you noticed any differences uh between people attending these workshops between various Industries people from the tech sector versus academics versus people from the banking industry yeah I think there are absolutely differences when it

comes to just culturally how to different organizations deal with data communicate with data uh but one thing that's been cool to see is that these lessons they they stay the same irrespective of Industry they sort of cut across all Industries uh which is interesting and for me being able to see the organization's data and see some of how they've communicated with Data before going in gives me such an interesting perspective and lens uh on the organization um but so I think there differences in how organizations use data but the concepts that we talk about in

the book really span everything Cole um one question um you mentioned that like you did a lot of research for the book you mentioned some like I'm guessing Neuroscience type Concepts what sort of disciplines did you draw on um you know when you were like researching or composing or as youve become more and more of an expert yeah one of the interdisciplinary places um that I've drawn a lot of inspiration from is just the area of physical design uh you know when you think about if you're designing a chair how do you make you know

how do you make that work for your audience and now data is different because it's not sort of a tangible thing so the things you have at DIS your disposal to show how to use something is uh not not tangible it's it's visual um so then it's thinking about how do you visualize this how do people see bringing in some of those sorts of things uh one example that I like um are people familiar with the Oxo brand of kitchen gadgets um so they're things like vegetable peelers or spatulas or like a garlic press um

and if you just sort of spew them or lay them out on a counter it's intuitive how to pick them up and you sort of don't even realize that when you're picking them up um because they're formed in the way that's going to make you pick them up in the way they're intended to be used uh which is brilliant right from a design standpoint and we want to think about how we can leverage that same those same sort of cues when it comes to our data of how do you make it so obvious to the

audience how they're supposed to use that data that they can't use it any other way right that they can't help but see what you want them to see um so design is probably one of the big places uh that I drew from when it came to researching some of the stuff for the book in regards to tools earlier you mentioned Excel and PowerPoint but are there any other tools I need to be fluent in to apply your lessons no and and not necessarily be Excel and PowerPoint either we talked about Tableau um you know sheets

there are many different tools out there and again my view is that any tool can be used well and any tool can be used not so well um but pick a tool get to know it as best you can so that it doesn't become a limiting factor when it comes to applying some of the lessons that we've talked about and some of the lessons covered in the book um Cole if you were to do um you know storytelling with data V2 um in 10 years or 20 years I know it takes a lot of time

to write these books what would it be on like would anything change dramatically or what do you think yeah that's an interesting question I used to always get that question at Google as well of like what's data visualization 2011 when's that coming what does that look like and for me there isn't an obvious sort of next one because the concepts that we talk about they're they're fundamental they should use always you know and as you get more experience visualizing data it's not that the way you visualize it changes I think it just becomes more nuanced

in how you apply some of the things that we talk about here so for me there isn't an obvious sort of next iteration um but who knows that may change after the next 100 workshops or so hi Cole um I I taught um I teach their wonderful closer of data visualization here at Google and today um so I te data B one which is pretty much focused on the first part of your presentation today I really loved it with the second part of your presentation with the storytelling I think it made perfect sense and I

think a lot of us could benefit from learning these two things together because storytelling is obviously such an important part of the overall lesson here so um is this something that we can steal with pride and I used to work with the people Dev team so I would love to share anything that's available and um I I just think a lot of googlers will benefit with the second part of this presentation today yeah absolutely I mean uh definitely check out the book cuz like I said it's covered storytelling is covered in much more depth there

because the storytelling piece really came in uh after the original course here was developed so there's not a lot of that content um there um but yeah I was I mean this is a space where when you see something good or effective steal it use it for your own use um there's no shame in that at all um and it's by practicing these sorts of things that we all get better um so yeah absolutely Cole it is so great to see you back at Google as someone who took the very early version of the course

and kind of I knew this is something special which I wish somebody rest of the world gets to see and I know how passionate about you are about this topic I'm so happy to see this coming uh thank you for taking the time to come and talk to us my question uh was around you know this this is just as much from the organizational perspective it's a skill but it's also like the culture of just having so much focus on it so as you speak with clients from variety of Industries what are the type of

thing that you think like we as sort of employees or the leaders can do to kind of like get a culture of you know having focus on this aspect just as much as anything else like data infrastructure or anything else how what can we do to get this message out in the world yeah I mean so Google's already taking steps right the fact that there is a data course here and that it's made widely available um sort of proves that there is an appetite for this and resources for it which is awesome I think when

it comes to propelling that even further and and embedding it throughout the culture it's about recognizing when it's done well and promoting that right when it's done well when there are good examples of really um highlighting those to other people and and making it a goal uh it's always been interesting to me because if you think of the entire analytical process you start off with a question or hypothesis then you collect the data then you clean the data then you analyze the data and at that point you can get away with just throwing it in

a graph and being done where the graph is the only part of that entire process that your audience ever sees so my view has always been it deserves at least as much time and attention as the other parts uh so I think as you have more examples internally of people doing that well that you can sort of hold up and say here this is what we should emulate it starts building uh culture around that over time uh and investing in people when it comes to the training developing internal experts to whom others can turn all

of these things can help sort of continue that positive momentum and with that thank you so much thank you