The Power of Data Visualization: A Conversation with Andy Poldark

Building Scatter Plots with Data Sets is an Art Form

When it comes to building scatter plots with data sets, one can create infinite different types of scatter plots. This is why practicing and getting involved with free tools like public datasets is essential. One must remember that even public isn't the only one who provides these resources. The idea of copying someone's work, as long as it's done with humility, is also relevant here. It's all about getting inspiration from expert practitioners and using that inspiration to create something new.

Finding Inspiration in Expert Practitioners

In order to become a good artist or database designer, one needs to get inspiration from expert practitioners. This can be achieved by downloading something for free and being inspired by the work of others. The book "The Art of Stealing" by Austin is a great example of this. It discusses how even artists need to copy someone else's work in order to create something new.

Practicing with Free Tools

To get involved in data visualization, one must practice using free tools like Tableau or Power BI. These tools can help one develop the skills needed to build effective scatter plots and other visualizations. It's also essential to learn how to use these tools in a way that feels natural and comfortable.

The Future of Business Intelligence

One of the trends that Andy Poldark is excited about is the democratization of data insights. This refers to making analytics tools more accessible to people who aren't necessarily analytically inclined. To achieve this, companies like Tableau are working on developing conversational interfaces that allow users to interact with their data in a more intuitive way.

Conversational Interfaces and Natural Language Processing

One of the ways that Tableau is achieving this goal is through its Ask Data tool. This tool allows users to ask natural language questions about their data, rather than having to navigate to a specific destination or learn how to use the native interface of the software. Andy Poldark is excited about the potential for these conversational interfaces to bring power and insights to people who might not have had access to them otherwise.

The Impact of AI on Data Analysis

Another trend that Andy Poldark is excited about is the role that artificial intelligence will play in data analysis. Already, we're seeing the development of tools like Dally and God X that can generate artwork using AI. These same technologies are also being applied to data analysis, allowing users to create complex visualizations with ease.

The Importance of Data Culture

As Andy Poldark notes, one of the key trends in business intelligence is the importance of data culture. This refers to the way that companies approach data and analytics as a whole. To achieve this goal, companies need to prioritize making data more accessible and user-friendly. This can be achieved through tools like Ask Data and conversational interfaces.

A Final Message from Andy Poldark

As we near the end of our conversation, Andy Poldark wants to leave his listeners with a few final messages. First, he encourages them to support organizations like Direct Action for Children, which is working to provide education and resources to underprivileged children. Second, he invites anyone who is interested in learning more about data science to start their own journey by downloading DataCamp. Finally, he encourages his listeners to follow him on his newsletter, The Sweet Spot, where he'll be sharing updates on his latest projects.

Conclusion

In conclusion, our conversation with Andy Poldark has highlighted the importance of practicing with free tools, finding inspiration in expert practitioners, and prioritizing data culture. As we move forward into a future where analytics tools are becoming increasingly conversational, it's essential to prioritize making these tools more accessible to everyone. By doing so, we can bring power and insights to people who might not have had access to them otherwise.

"WEBVTTKind: captionsLanguage: enyou're listening to data framed a podcast by datacamp in this show you'll hear all the latest trends and insights in data science whether you're just getting started in your data career or you're a data leader looking to scale data-driven decisions in your organization join us for in-depth discussions with data and analytics leaders at the Forefront of the data Revolution Let's Dive Right In foreign this is Adele data science educator and evangelist at datacamp this is week three of our daily literacy month special at data camp and this week is all dedicated to data visualization and data storytelling and there is no better person to talk about these topics with than Andy caught grave and the Cod grieve is co-author of the big book of dashboards and Senior data evangelist at Salesforce in Tableau he is the host of if data could talk co-host of chart chat and columnist for information age he is also on the 2021 data IQ top 100 most influential people in data with over 15 years of experience in the industry he has inspired thousands of people with technical advice and ideas on how to identify Trends in visual analytics and develop their own data Discovery skills throughout the episode we speak about why data visualization skills are so important how data visualization skills can drive organizational data literacy best practices for visual storytelling and much more if you enjoyed this episode make sure to like make sure to comment like And subscribe to data framed and make sure to check out our content for daily literacy month now on to today's episode Andy it's great to have you back on the show fantastic to be here Adele thanks for having me on again awesome so I am excited for you to be joining us for data literacy month to talk about data visualization creating effective dashboards how it connects with data literacy and much much more but before maybe for the folks who haven't listened to our first episode together you want to share a bit about your background so my name is Andy coggreive I'm senior data evangelist at Salesforce having been at Tableau since September 2011. and I'm co-author of the big book of dashboards and I also host the video series called chart chat with my co-authors and Amanda McCulloch who runs database society and in Sweden chat chat we nerd out every month about the ins and outs of charts we see in the news and the cross media yeah so I've been in this field for over 15 years and just really passionate about data literacy that's really great and I highly recommend for the audience to check out Char chat it's very fun now before deep diving with you today on data visualization dashboards and storytelling I'm going to set the stage for today's chat by really trying to contextualize data visualization within the broader data literacy conversation data visualization is often called the gateway drug to more complex data skills and tasks so can you walk us through maybe in your own words why data visualization is so important and why learning these skills is also really important yeah absolutely so I'm going to attempt to explain why seeing something is important through an audio platform so it's a bit of a challenge but go with me listener and go with me imagine you're looking at a spreadsheet of numbers right I don't know sales of products across different regions loads of numbers if you are looking at that spreadsheet can you see which is the highest selling region and product can you see which is an outlier can you see which is the worst performing well maybe you can but on a spreadsheet of numbers maybe there's 100 200 digits on the spreadsheet it's going to take you minutes to do it and you're probably going to do it inaccurately the power of data visualization is taking aggregated versions of those numbers or even just highlighting numbers in a table so that you can see the information you want to see in milliseconds or less and that is what we're trying to do with data visualization take spreadsheets of numbers or databases full of digits and express them visually in a way that answers questions as quickly as is possible and yeah we're going to get deep much deeper into that then copying on the importance of that skill or that concept of being able to understand data very quickly in some sense this has never been more important today thinking back about the past two years for example if you want to look at a table of covid-19 data spread and how it's evolving that's not going to be that's going to be tantamount for very horrible Public Safety messaging so being able to Showcase that with the chart has ever been more important you know that that's so true with that you know you think we've lived through two nearly three we're getting on for we're approaching our third year of covid and just think about all those government press conferences we sat through all those charts that the media and the medical professions put out in a way of communicating really complicated data in a way that educates and informs a nervous population database was fundamental to the pandemic and is hopefully inspired us all to I think better about data literacy so now that we understand the importance of data visualization why is such an important skill to master as part of a dead literacy Journey whether an organization or an individual I think it's really great now to talk about really what makes an effective data visualization I'd love to dig into the details with you and your book provides a lot of great inspiration for that so in your book The Great Book of dashboards you lay out really well the foundational Elements of Effective data visualizations you're also someone who really borrows from the world of design thinking the world of design visual imagery to improve data visualizations and communicating with data so maybe to first start off can you discuss the different ways design thinking improves data visualization yeah absolutely I think my biggest inspiration here was a book called the Design of Everyday Things by Don Norman Samuel book on engineering design and user experience but really applies to data visualization too something I hadn't appreciated when I first has got into this field the big summary that Don Norman says is designers make pleasurable experiences and you might think what how does that apply to a chart well imagine a boring bar chart but bar charts aren't boring obviously but imagine a boring bar chart if you can how do I bring design Theory into that in order to make the user get the maximum out of that bar chart well I can add a correct title I can add a title which asks or answers the question that the bar chart reveals I can ensure that the data points that I want people to see are highlighted in such a way that they see the longest bar or one particular bar I could do that by softening the way the axes are formatted making them a bit like gray or something so you can use annotation layers and that's just on a single bar chart now advance that into maybe more complex charge you might put on social media or dashboards you communicate with your organization there you've got to create this pleasurable experience in some way that matches the medium via social media or your business intelligence server internally and it's still got to get the right information to the audience in the shortest amount of time possible and again Don Norman talks about how we process any designed object whether it's a remote control a cattle or a chart a dashboard we look at it we have a visceral response we make a judgment based on its appearance we have a behavioral response can this chart actually answer the question we came to it with and then we will reflect um did it look good could I answer my question if the answer is yes then you did a good job and bringing design theories laws or theories and rules from the world of design has really taught me how to get those three levels of processing right in charts and dashboards that we can build and in some sense nailing those levels or processing really enables you as someone who's creating a data visualization to get action from your stakeholders from your audience because otherwise if you don't nail that the objective of your data visualization is necessarily going to be achieved with this convincing a stakeholder or enabling an action yeah and the keyword there is the objective is the visualization you know a common mistake I see all the time is oh they want to see sales data okay or just press press the button I've got a pie chart of sales data you know what do they want to know about sales data what are you trying to communicate and if you haven't thought about the objective of the visualization then it won't be a successful design so expanding on the Notions that you initially laid out here in the book and the different thought leadership you create as well through blog post through your channel your podcast appearances but you always talk about the different elements of an effective data visualization so can you walk us through maybe in more detail what makes a great data visualization great and what are the different elements of such a data visualization my two-word answer is it depends and uh so that's generally my response how should I visualize this job it depends okay now that's a little facetious and perhaps not very helpful for people just starting out on their Journey so if you'll allow me more than two words Adele I'll carry on I'll definitely do okay good so I think in order to measure the success of a visualization you've got to be asking it goes back to what we just mentioned what are you trying to achieve and I think something I'm honing in on is a model that there are four tensions or levers that you're trying to pull and push and pull push and pull whenever you build a visualization and when you think about those four tensions you are then able and whether you've implemented them successfully you're able to judge whether your visualization is great so the first three would be is your objective to show a large amount of detail or just provide the gist of the information the second one would be is this Char going to be for fun or is it serious right you know that's a valid conversation and the third tension would be are you trying to show something where people can explore the data or are you trying to explain a story you've already found fourth tension is is your visualization formatted honestly or is it formatted in a deceptive way so that last one we always need to keep our formatting honest people do create deceptive charts sometimes deliberately sometimes accidentally but we do have to obviously we should all be leaning towards honesty but the other thing about those tensions are really important so imagine I'm doing a presentation to the board and I know a slide is only going to be on screen for about two seconds and I'm going to show it and then move on in that case you have very little time to convey very little information so a successful chart there is super simple with a like an in your face message contrast that with a business user who's got time to explore the data on a business dashboard that you've built that dashboard could have 10 different charts on it and some of them could have loads of data points and that could be interactive actions which create a story and a flow in that case it could be really complicated it's obviously going to be quite serious and it's a very much an exploratory experience so both those scenarios are generating success but the way to know if you failed is if you take that complicated dashboard and put it in a PowerPoint for two seconds and say as you can see the dashboard shows sales are going up and then you move on it's nope I can't so that great dashboard could be appalling if used in the wrong place so that's why it depends because you've got all these little levers you have to push and pull and eventually you can judge success based on what you're trying to achieve yeah and I love that you use the word lever because the way I imagine this when you're breaking it down is that you have kind of this panel with different knobs that you can evaluate the different tensions and depending on the inputs that you have right is this an audience that only has five minutes to listen to your data story your presentation what's the medium by which you're sharing it determines the level of where you need to put the knobs yeah absolutely and I'll give you an example Hans rosly he was Swedish physician who exploded onto the tab talk scene back in 2006 showing this amazing chart of the health of Nations right and basically his Ted Talk was him talking about a scatter plot and I do this exercise with presentations I put on a slide 150 dots on it and lots of different colors and I asked my audience is this too complicated for a presentation people are like yeah it is it is and then we play the Hans rosin video where he presents the same chart this 150 dot multiple color chart with two measures animations and it's mind-blowing the difference being Hans rosling takes the time to explain what each axis means he focuses on one dot he tells you what one dot means and then he explains what the context is within the greater picture and then he narrates the chart and sort of animates through time so what he's done there is go does my audience need the detail or the gist and what he's realized is he wants to push the lever so that they do get detail the audience does get detail because it's a complicated chart but he realizes in order to achieve sharing the detail in the presentation he has to commit three four five six minutes while the chart isn't telling people what they're seeing so that I think is an example of how somebody can use these levers to achieve something pretty powerful maybe giving another example here flipping the levers can you think of an example where the lever is more on the gist side of the things and it's a bit serious or can you give us maybe another example where it's more just said so another of my favorite examples was a chart originally made back in 2012 by Simon Scott and this was a chart showing conflict related deaths in Iraq from 2003 to 2011. right so not a happy data set right now I'm going to try and explain this the chart that Simon published was a simple bar chart files were pointing down and they peaked in the center of the bar and then he colored them a deep blood red the appearance as you're looking at this bar chart with a sort of inverted trying to look like a smear of blood dripping down the screen and the title was Iraq's bloody Soul so what scar did was use orientation color and title to create this visceral response to think deeply about the human tragedy of what happened in Iraq in that period now what I realized you could do is if you flip the bars the other way up change it blue you actually begin to see the the number of deaths month by month is decreasing so in that case you could actually change the title so deaths are on the decline and try to tell the story of Hope instead of focus on the tragedy and this is where the lever is really applying on your design you're using those design levers to actually change the message in a story completely just with color and orientation that's an amazing example and definitely we've referenced it in earlier examples where you showed it to us especially on a webinar that you attended with datacamp and I highly recommend the audience to check it out there's one section in your book that I love which covers something called pre-attentive attributes and data visualization and it touches Upon A lot of the Notions they discussed here I think these provide a great framework to think about how a data visualization is perceived and how to best construct one can you walk us through maybe what our pre-attentive attributes and how they impact a data visualization's impact on the audience yeah I fell in love with data visualization back many many years ago and it was part of learning about what cognitive science that really turned me on to it and basically our millions of years of evil you should have Hunter Gathering and trying to spot Tigers moving through the grass of the Savannah and avoid the red dangerous poisonous berries enabled our visual system to we could process the natural environment around us before we consciously think about it and that is a gift of evolution wow so avoiding tigers and finding batteries enables us to be better data analysts yes it's true because think about a bar chart a bar chart has rectangles that are different lengths so length is an example of a pre-incentive attribute and so now what actually is it that our brain looks at the different lengths of those bars and actually identifies which is the longest and which is the shortest before we even look at the bar chart consciously so we've already got a head start to the data before we actually think what am I actually looking at or area you can make circle charts a big circle we could pre-attentively see is bigger than a smaller Circle or colors or Hues so if you're looking for a red dot amongst some gray dots you're going to see the red dots pre-attentively these pre-attentive attributes are the atoms from which we build charts length position color Hue size angle there's loads of them and it's probably beyond the scope of this podcast now but we process some of them better than others which is why pie challenge aren't very good because we don't really do angles and areas very accurately but our brain can super accurately see differences in length of bars for example so yeah pretty attentive attributes once you understand those it unlocks so much yeah I couldn't agree more and you can actually leverage them to your advantage while delivering a presentation for example to guide the audience's attention by using these preventive attributes a great example would be you know if you want to point the audience's attention to one bar in your bar chart you can elect to make everything gray and just highlight color on that bar chart mid presentation the it's so easy to misuse color in visualization or any communication media because every tool available to us today can use an infinite amount of colors but the most powerful visualizations and dashboards are the ones that use gray and one color and then really really powerful okay that's awesome so of course given the book is called the Big Book of dashboards I'd love to actually Deep dive with you on dashboarding as dashboards are one of the most effective ways to share insights with data visualizations within any organization today you know many organizations are leveraging tools like Tableau click power bi to do these dashboards so can you walk us through maybe how dashboards extend the power of data visualization and what you have found are the best practices for creating effective dashboards well I think first we actually we have a semantic challenge but I'll ask You Adele how would you define a dashboard so I would Define a dashboard as a collection of data visualizations aimed at answering a specific set of questions around a specific set of data within an organization how does that definition it's great right have you thought deeply about that in the past or is that is that your first step that's kind of my my first step at it but I'm not a data visualization expert as you Andy oh I'm sure you are I'm sure you are okay so in the dashboard can be defined in a gazillion different ways in our book The Big Book of dashboards our definition is only 15 words long and it's super vague because what we realized is as we were looking at all the dashboards that we could find across Industries is that and there were so many different variations so one example you said a collection of charts in your example but we've got some great dashboards that we think are dashboards they're just a single visualization so every time we try to extend our dashboard definition we could just find more and more caveats to be like oh well this dashboard doesn't fit the definitions what we're going to do so in the end we just collapse the definition to something pretty vague about it's an artifact you used to monitor a system and to facilitate understanding I think it was something like that the reason I don't really know what our definition was is that I don't really care about the semantics right you or the audience we are trying to collect information and present it to a user in such a way that they can make decisions check a process or understand more about whatever it is they're looking at right and if you want to call that dashboard great ultimately a dashboard is a word referring to a piece of wood on a stagecoach anyway so it's a word taken from somewhere else so now we've got our definition well now I've maybe destroyed the definition of dashboards who knows I don't know how do you best create one well you have to go to your audience and really understand very deeply what it is they want to see if they say oh I'd like to monitor sales oh great why would you like to monitor sales I mean it might be are we on target is our quotes are on target this quarter why do you want to measure that and they'll come up with a different answer and believe me when you go to users if you ask why four or five times you know this is Classics of business MBA process you'll get to the root cause of what they want once you know what they want you're like well how do you want to see it are you going to be interacting with this thing do you want this thing delivered in an email are you going to be looking good on a cell phone or on a screen in a call center each of those will determine a different delivery mechanism in a different style so to summary of this the best practice for developing effective dashboards is go and speak to the user and understand what they want why they want it and how they want it and then create a really basic prototype and then they'll go no that's not what I wanted at all and then from there you can iterate until you get the right answer what they need that's really great it's definitely complicated but it's also wonderfully simple and accessible which is what's so nice about dashboarding and data visualization in general so the book contains a lot of examples of dashboards from different Industries and different use cases and you showcase brilliantly why these dashboards are effective can you walk us maybe through the different type of dashboards that you've encountered and maybe expanding onto that what makes each of those dashboards effective yeah I think a big question you have to ask is should the dashboard be interactive or not and I'll focus on that for this answer so if if something's going to be interactive then you've got to start asking well do my users understand how to use this dashboard how do I make sure they literally know how to use this platform you know for Tableau server or Tableau Cloud for example they need to know what the URL is and then once they get there what is it they're looking at even when you put filters on a dashboard how do the users even see them right now this sounds so ridiculous Adele that I can say well it's on the screen surely they'll see them well we've done a bunch of eye tracking studies on dashboards you know a lot of which were taken from the big book dashboards and I could have designed a dashboard with filters on it then we allow people to look at the dashboard themselves and they literally don't even look at the filters so then at the end of the exercise you ask them well why didn't you interact with this stuff and they go I didn't see the filters and the inside I might be screwing but they're on the screen you know they're literally in front of you but understanding that people look at screens in a way that you might not predict it might not be as you'd hope so you've got to ensure they can see the things to interact with in the first place so interactive or not is important we have a great example in the book from Arsenal Football Club one of the Premier League teams in England and they have this static chart based on a player's performance which is delivered to the player after each match but just before the training session and it's a static dashboard delivered to their cell phones so that they could show that to their teammates and have a laugh or have some serious Insight but basically analyze their own performance and that really fundamentally different types of dashboards because one's interactive this is an example that isn't but the really lead to the question is like well is this dashboard effective it goes back to the previous question are we thinking about how the user gets it and what we're trying to achieve so yeah I think interactivity or static is a big decision to make another one is what is the form of delivery is it going to be primarily used on a big screen is it going to be primarily used on a cell phone or is it just going to get delivered in an email again that they will require completely different form factors that you have to take into when you're designing your dashboard that's really great and maybe deep diving here a bit more what strikes me from your answers there are really two main considerations people need to have when thinking about the dashboards they need to create one is the audience right what is the audience expected to achieve with this and then secondly what is the format and the user experience of consumption of dashboard in an organization what are the different types of audiences in a nutshell basically of personas that may expect to interact with the dashboard someone is developing and what is often the ideal way of presenting that information the types of audiences it's difficult to try and summarize that as a capsule but one example I often see is your Executives right let's think about let's go back and mentioned sales earlier let's think about sales an executive CEO or a head of Revenue they want to see what is the aggregated role at Revenue this quarter compared to last quarter compared to Target and compared to this time last year right they want to see this thing rolled up to a very high level of aggregation and they'll have kpis which will show whether they're on Target and then yeah you know some slightly disaggregated breakdown Maybe by region or by product area or something like that that dashboard is useless for the account executive who is actually trying to use data to Target an account so an account executive completely different experience you know maybe they have five or six accounts and one of the accounts is a leading car retailer or something right the executive's dashboard is utterly useless to this person they have to be able to take the same data set and so was maybe they can use data to tailor an account plan to Target that account you know what of the users in that organization been doing have they been looking at our website have they been designed training courses what have we sold to them previously are there any opportunities that have been one or closed recently and so it's the same data set but the dashboard the account executive needs is completely different to the dashboard the exact news and the reason I use this example is sometimes we see in organizations the executives go we have done business intelligence we are successful because I have an executive dashboard hey team everybody is the executive dashboard because it reveals a complete misunderstanding of data culture and data literacy to think that hey that's what you've got is great what about all the people below you in the organization you've got to think for those people too so that's one example that's really great and I'm excited to expand on The Daily literacy component here and maybe on the user experience before we move on to the next question what are things that you would expect need to be considerations as part of a dashboard design process that would be from a user experience perspective so for example one thing that came to mind I was reading an article about this recently is just how important load times are for a dashboard or to be able to be consumed right and this is not an interactivity decision not an aesthetic decision you could have one of the most well-designed dashboards of all time but if it takes more than five to seven seven seconds to load it could really hurt the amount of times it's actually used yeah I think we might have a question later about if database is the gateway drug what else do you need to consider and this is where these things have to be considered I know in Tableau for example if you build a tableau dashboard and keep it Bare Bones super simple and you've got a well-formatted data set and a big nicely resourced server the load time will be really fast if you get carried away as a designer and start thinking I'm going to bring you loads of bells and whistles and background images and do all the really bespoke calculations and add a lot of interactivity for example yeah yeah yeah then you're actually then beginning to put more of a burden on the server and that dreaded load time begins to increase and this is one of the challenges and certainly something when I used to be a dexa analyst when I was a customer of Tableau before 2011. I used to get carried away with building elaborate dashboards that are really intricate but they took forever to load so you have to sometimes recognize that the enthusiastic designer that is inside you trying to build these wonderful experiences has to be balanced with the need to create something that actually doesn't lose people when they're trying to load it it's a great question an important thing to think about yeah that's awesome so of course the other side of things here Beyond user experience and Beyond design is the ability to create a narrative right communicating Data Insights and data storytelling it's extremely important when crafting data visualizations and dashboards so can you walk us through maybe how to effectively embed a narrative within a dashboard and how to convey that insight to a consumer guys I've been resurrecting the talk I did back in 2014 about a dashboard I built which is very much inspired by Dilbert all right what's Gilbert now think about Dilbert comic strip the weekday strip is a three panel comic strip panel one is an introduction to the joke panel two is the joke and the third panel is like some sort of epilogue which hopefully adds and builds on The Jig itself that's an amazing story structure introduce the plot conclude the plot write an epilogue right I built dashboards that follow that story structure three panel dashboard oriented horizontally introduce the data deliver the punchline and create an epilogue all right now but that was for a particularly bespoke situation but the reason I use this example is because dashboards are made of panels containing charts that are in some sort of a grid like structure and they should be in some sort of a grid like structure and where do we see that we see that in comic books right and what do we do in comic books in the Western World we read them from the top left to the right well we read them from the top left to the bottom right in a sort of a linear structure so if you want to form a narrative in a dashboard a really good starting point is to think like a comic strip so in the top left again I'm talking about Western left to right reading cultures here the top left contains the super summary and then from there you can lead the user left right and down into more details so whatever's in the bottom right is kind of the most granular level detail which is where you've got the deepest level of context now that's not a universal way of Designing dashboards that reminding way of doing things but that's one example go and read some Comics basically I highly recommend as well they're incredible mediums to convey storytelling visuals so of course now we covered kind of the main skills when it comes to creating effective dashboards visualizations and narrative but I think given that it's data literacy month I also be remiss not to talk about the connection between the illiteracy and data visualization both from an individual perspective and from an organizational perspective you know I said at the beginning of our episode together as you alluded to as well earlier data visualization is often called the gateway drug to more complex data tasks right so can you walk us through maybe from your perspective how consuming data visualizations and learning about data visualization enables better data literacy within organizations and more thoughtful conversations around data well I think when you have good data set and you're building charts you know your dashboards for example are successful when users come to you and ask more questions but oh I've seen the sales by region thank you very much now What's Happening by product but that's a sign the day of a gotta looked at your visuals understood it and then it's inspired a second level question and that is a sign of success you can't always answer every single second level question you shouldn't aim to do that because you can't anticipate all the questions users are going to have but that's a sign people are engaging right so that's on dashboards as you start bringing data into presentations or even just into meetings where you're throwing data around on screens in an ad hoc manner you can start querying data very very quickly and getting answers instantly I hear customers a lot say one of the problems we have Andy is we spend 15 minutes of the every meeting arguing about the data and I've always thought isn't that kind of the goal and I realized it's a problem if the argument about the data is because you don't agree with where the data's come from and you don't agree about the truth of the data obviously we've got to solve that but I'd love to meetings to be let's go in we're going to try and continue sales how are we going to improve sales this quarter right well let's play with the data let's explore which bits are underperforming or outperforming and let's have that conversation and argue about what the date was saying to come out with the decision so I think that's a really high level of maturity where data is just the fabric of the conversation and data is easy to access easily understood by everybody and driving questions as they arise which dashboards can't do dashboards can only answer questions you already thought about what are you going to do to answer today's question yeah that's really great data visualization skills what's so nice about them as well is that they give you that confidence to be able to criticize a data artifact right because especially visualizations they tend to present themselves as a matter of fact as ground truth right because it's beautifully visualized it's there in your face and having those skill set really equips you to become much more constructive and also much more critical of data yeah that's that's so good and a really important part of that is empathy as well because I'll tell you a little story I used to run a Blog called decipheredreality.com and it analyzed data about the board game Arkham Horror Arkham Horror the card game right it's a nerdy collectible card game where you build decks to take your characters through a scenario against Lovecraft and spider monsters but hey what an awesome game right there's an entire website dedicated to which cards are used in which kind of depth and I started building charts based on the data in this website for fun right and because it was I love the game anyway so I built the chart so here's the most common used card here's the least common used car blah blah blah and then somebody on Reddit replied yes I don't think you understand data visualization analytics should be about this that and the other and you're only sharing the first level of insight I recommend you do this thanks for your advice I'll think about that when I write my second book and when I appear on another set of podcasts and when I do another set of Keynotes to teach another 10 000 people but what that person failed to do let's show the empathy of what was I trying to achieve I was just trying to achieve a little bit of fun looking at a super basic charts I'm going here's the basic thing take the data for what it's worth and have some fun and he had kind of gone well he's seen that and then he's going well I've got these are the 10 questions damn it he's not answered answer these questions he's a failure I only answered the first question now you go and do the work if you're so bothered about it so anyway I could now after three years of hurt laugh about that story but the point being that person was criticizing in a non-ampathetic way and I think data literature mature data literacy knows how to criticize thinking about the designer's intent and which what's the actual goal of the visualization they're critiquing right so ah there you go shared my story yeah that's really great I appreciate the vulnerability I do so given how important data visualization skills are you know what are the ways you recommend to people within an organization to become better at visualizing data and also consuming data visualizations well I guess first of all I'd say listen to the data Camp podcast get involved in data literacy months and all the resources that are there for you right so that's a given but also just practice practice practice data visualization and getting good at building dashboards and being able to communicate effectively with data it is an art and a skill it's technical it's art related and you know an artist is not successful from day one an author is not successful from day one a coder is not successful from day one they have tried they've played they've succeeded they've failed and every single step they're taking is teaching them a little bit more there are free tools such as Tableau public you can just download it or use it on the browser connect the data and get going and every time you build something you are learning on top of that there's really rich and active communities you can get involved with one example in the Tableau Community is something called back to viz Basics every two weeks it's like here's a really basic data set and the challenge is build a bar chart or build a scatter plot it doesn't get easier than that and so the barrier eventually is super super low because you can learn that skill in about half an hour but then you could connect to all the people who've done it the same task that month and believe me build a scatter plot with this data set will generate infinite different types of Scatter Plots right so practice get involved in free tools obviously tablet public isn't the only one and then essentially steal like an artist still like an artist was a great book by Austin but his manifest it was in order to become a good artist or in this case a database designer you can go and get inspiration from expert practitioners and sort of copy their work in a way which is not plagiaristic right copy something for inspiration and with humility so I think get involved download something for free and be inspired by the work of others that's definitely the case and I really appreciate these insights and advice and now Andy as we near the end of our episode today I'd love to ask you what are you up to next and where can audiences find you and given your position at Tableau what are future Trends and releases in the business intelligence space that you are excited about so I've recently got promoted so I've seen your data evangelist at Salesforce now and I'm excited to bring data visualization and data culture understanding to salesforces customers and Prospects which is a really big platform so I'm excited about that a time of recording I'm going to be going on a sabbatical soon so at the end of that I'm beginning to work on a new book can't say very much now but you've heard me talking for an hour and you've got an idea about what I'm passionate about so just keep your eyes peeled on that so that's me and then future business intelligence Trends I think what we're seeing in our area is the Tableau and other tools like power bi and click create tools which in the hands of expert analysts can turn data into anything what I think Tableau hoped 10 years ago is that anybody can learn to use tablet what we've learned is that actually analysts like to use Tableau on people who are analytically inclined like to use tablet right so they put in the effort to learn the platform however not everybody has that inclination to spend the time learning the internets of using this platform so it's how do you bring the power of say Tableau or any analytics platform to people who for whatever reason can't invest the time into learning that drag and drop experience and we're doing it through things like ask data which is a natural language interface to Tableau and we've been working on it for years and the latest iterations are are even beginning to tempt me you know a 15-year veteran of using Tableau away from the Native Tableau interface I'm just going to type a sentence like Martin Google and then Tinker with the words in that sentence to take it with the view so I think our goal is to try and bring the power of analytics to people who aren't analysts you know that's things to ask data and bringing data to the user rather than asking them to go to a destination to see data so yeah that's Trend I'm excited about it's very exciting to see the trend of conversational interfaces and all sorts of analytics tools like even you see it in open source programming languages like python you now have something like God X and you say hey I need this data set I need a function that creates that and it creates it for you as well so this is going to be very exciting towards the democratization of data and the democratization of Data Insights in general yeah I I don't have you been using the AI generating artwork yeah I have I have dally too yeah yeah oh yeah sorry it's dally not more yeah yeah they're amazing right and that kind of AI that language interpretation you know that's what we're trying to bring to data as well that's awesome so Andy it was amazing having you back on the show is there any final call to action before wrap up today yeah I guess it's like first of all day to Camp Dayton literacy month I think what you're doing is fantastic at direct Camp so I support everything that's going on there second just start your own Journey if you are thinking about starting your journey just download a day to say try it and see what you can find from it and I guess if people want to follow me I have a newsletter called The Sweet Spot which will get renamed soon but that's slightly to do with the book but currently called The Sweet Spot and I'm sure we'll put links to that in show notes awesome thank you so much Andy for coming on data friend my absolute pleasure thanks Adele I think you did a great job and I hope you all enjoyed you've been listening to data framed a podcast by datacamp keep connected with us by subscribing to the show in your favorite podcast player please give us a rating leave a comment and share episodes you love that helps us keep delivering insights into all things data thanks for listening until next timeyou're listening to data framed a podcast by datacamp in this show you'll hear all the latest trends and insights in data science whether you're just getting started in your data career or you're a data leader looking to scale data-driven decisions in your organization join us for in-depth discussions with data and analytics leaders at the Forefront of the data Revolution Let's Dive Right In foreign this is Adele data science educator and evangelist at datacamp this is week three of our daily literacy month special at data camp and this week is all dedicated to data visualization and data storytelling and there is no better person to talk about these topics with than Andy caught grave and the Cod grieve is co-author of the big book of dashboards and Senior data evangelist at Salesforce in Tableau he is the host of if data could talk co-host of chart chat and columnist for information age he is also on the 2021 data IQ top 100 most influential people in data with over 15 years of experience in the industry he has inspired thousands of people with technical advice and ideas on how to identify Trends in visual analytics and develop their own data Discovery skills throughout the episode we speak about why data visualization skills are so important how data visualization skills can drive organizational data literacy best practices for visual storytelling and much more if you enjoyed this episode make sure to like make sure to comment like And subscribe to data framed and make sure to check out our content for daily literacy month now on to today's episode Andy it's great to have you back on the show fantastic to be here Adele thanks for having me on again awesome so I am excited for you to be joining us for data literacy month to talk about data visualization creating effective dashboards how it connects with data literacy and much much more but before maybe for the folks who haven't listened to our first episode together you want to share a bit about your background so my name is Andy coggreive I'm senior data evangelist at Salesforce having been at Tableau since September 2011. and I'm co-author of the big book of dashboards and I also host the video series called chart chat with my co-authors and Amanda McCulloch who runs database society and in Sweden chat chat we nerd out every month about the ins and outs of charts we see in the news and the cross media yeah so I've been in this field for over 15 years and just really passionate about data literacy that's really great and I highly recommend for the audience to check out Char chat it's very fun now before deep diving with you today on data visualization dashboards and storytelling I'm going to set the stage for today's chat by really trying to contextualize data visualization within the broader data literacy conversation data visualization is often called the gateway drug to more complex data skills and tasks so can you walk us through maybe in your own words why data visualization is so important and why learning these skills is also really important yeah absolutely so I'm going to attempt to explain why seeing something is important through an audio platform so it's a bit of a challenge but go with me listener and go with me imagine you're looking at a spreadsheet of numbers right I don't know sales of products across different regions loads of numbers if you are looking at that spreadsheet can you see which is the highest selling region and product can you see which is an outlier can you see which is the worst performing well maybe you can but on a spreadsheet of numbers maybe there's 100 200 digits on the spreadsheet it's going to take you minutes to do it and you're probably going to do it inaccurately the power of data visualization is taking aggregated versions of those numbers or even just highlighting numbers in a table so that you can see the information you want to see in milliseconds or less and that is what we're trying to do with data visualization take spreadsheets of numbers or databases full of digits and express them visually in a way that answers questions as quickly as is possible and yeah we're going to get deep much deeper into that then copying on the importance of that skill or that concept of being able to understand data very quickly in some sense this has never been more important today thinking back about the past two years for example if you want to look at a table of covid-19 data spread and how it's evolving that's not going to be that's going to be tantamount for very horrible Public Safety messaging so being able to Showcase that with the chart has ever been more important you know that that's so true with that you know you think we've lived through two nearly three we're getting on for we're approaching our third year of covid and just think about all those government press conferences we sat through all those charts that the media and the medical professions put out in a way of communicating really complicated data in a way that educates and informs a nervous population database was fundamental to the pandemic and is hopefully inspired us all to I think better about data literacy so now that we understand the importance of data visualization why is such an important skill to master as part of a dead literacy Journey whether an organization or an individual I think it's really great now to talk about really what makes an effective data visualization I'd love to dig into the details with you and your book provides a lot of great inspiration for that so in your book The Great Book of dashboards you lay out really well the foundational Elements of Effective data visualizations you're also someone who really borrows from the world of design thinking the world of design visual imagery to improve data visualizations and communicating with data so maybe to first start off can you discuss the different ways design thinking improves data visualization yeah absolutely I think my biggest inspiration here was a book called the Design of Everyday Things by Don Norman Samuel book on engineering design and user experience but really applies to data visualization too something I hadn't appreciated when I first has got into this field the big summary that Don Norman says is designers make pleasurable experiences and you might think what how does that apply to a chart well imagine a boring bar chart but bar charts aren't boring obviously but imagine a boring bar chart if you can how do I bring design Theory into that in order to make the user get the maximum out of that bar chart well I can add a correct title I can add a title which asks or answers the question that the bar chart reveals I can ensure that the data points that I want people to see are highlighted in such a way that they see the longest bar or one particular bar I could do that by softening the way the axes are formatted making them a bit like gray or something so you can use annotation layers and that's just on a single bar chart now advance that into maybe more complex charge you might put on social media or dashboards you communicate with your organization there you've got to create this pleasurable experience in some way that matches the medium via social media or your business intelligence server internally and it's still got to get the right information to the audience in the shortest amount of time possible and again Don Norman talks about how we process any designed object whether it's a remote control a cattle or a chart a dashboard we look at it we have a visceral response we make a judgment based on its appearance we have a behavioral response can this chart actually answer the question we came to it with and then we will reflect um did it look good could I answer my question if the answer is yes then you did a good job and bringing design theories laws or theories and rules from the world of design has really taught me how to get those three levels of processing right in charts and dashboards that we can build and in some sense nailing those levels or processing really enables you as someone who's creating a data visualization to get action from your stakeholders from your audience because otherwise if you don't nail that the objective of your data visualization is necessarily going to be achieved with this convincing a stakeholder or enabling an action yeah and the keyword there is the objective is the visualization you know a common mistake I see all the time is oh they want to see sales data okay or just press press the button I've got a pie chart of sales data you know what do they want to know about sales data what are you trying to communicate and if you haven't thought about the objective of the visualization then it won't be a successful design so expanding on the Notions that you initially laid out here in the book and the different thought leadership you create as well through blog post through your channel your podcast appearances but you always talk about the different elements of an effective data visualization so can you walk us through maybe in more detail what makes a great data visualization great and what are the different elements of such a data visualization my two-word answer is it depends and uh so that's generally my response how should I visualize this job it depends okay now that's a little facetious and perhaps not very helpful for people just starting out on their Journey so if you'll allow me more than two words Adele I'll carry on I'll definitely do okay good so I think in order to measure the success of a visualization you've got to be asking it goes back to what we just mentioned what are you trying to achieve and I think something I'm honing in on is a model that there are four tensions or levers that you're trying to pull and push and pull push and pull whenever you build a visualization and when you think about those four tensions you are then able and whether you've implemented them successfully you're able to judge whether your visualization is great so the first three would be is your objective to show a large amount of detail or just provide the gist of the information the second one would be is this Char going to be for fun or is it serious right you know that's a valid conversation and the third tension would be are you trying to show something where people can explore the data or are you trying to explain a story you've already found fourth tension is is your visualization formatted honestly or is it formatted in a deceptive way so that last one we always need to keep our formatting honest people do create deceptive charts sometimes deliberately sometimes accidentally but we do have to obviously we should all be leaning towards honesty but the other thing about those tensions are really important so imagine I'm doing a presentation to the board and I know a slide is only going to be on screen for about two seconds and I'm going to show it and then move on in that case you have very little time to convey very little information so a successful chart there is super simple with a like an in your face message contrast that with a business user who's got time to explore the data on a business dashboard that you've built that dashboard could have 10 different charts on it and some of them could have loads of data points and that could be interactive actions which create a story and a flow in that case it could be really complicated it's obviously going to be quite serious and it's a very much an exploratory experience so both those scenarios are generating success but the way to know if you failed is if you take that complicated dashboard and put it in a PowerPoint for two seconds and say as you can see the dashboard shows sales are going up and then you move on it's nope I can't so that great dashboard could be appalling if used in the wrong place so that's why it depends because you've got all these little levers you have to push and pull and eventually you can judge success based on what you're trying to achieve yeah and I love that you use the word lever because the way I imagine this when you're breaking it down is that you have kind of this panel with different knobs that you can evaluate the different tensions and depending on the inputs that you have right is this an audience that only has five minutes to listen to your data story your presentation what's the medium by which you're sharing it determines the level of where you need to put the knobs yeah absolutely and I'll give you an example Hans rosly he was Swedish physician who exploded onto the tab talk scene back in 2006 showing this amazing chart of the health of Nations right and basically his Ted Talk was him talking about a scatter plot and I do this exercise with presentations I put on a slide 150 dots on it and lots of different colors and I asked my audience is this too complicated for a presentation people are like yeah it is it is and then we play the Hans rosin video where he presents the same chart this 150 dot multiple color chart with two measures animations and it's mind-blowing the difference being Hans rosling takes the time to explain what each axis means he focuses on one dot he tells you what one dot means and then he explains what the context is within the greater picture and then he narrates the chart and sort of animates through time so what he's done there is go does my audience need the detail or the gist and what he's realized is he wants to push the lever so that they do get detail the audience does get detail because it's a complicated chart but he realizes in order to achieve sharing the detail in the presentation he has to commit three four five six minutes while the chart isn't telling people what they're seeing so that I think is an example of how somebody can use these levers to achieve something pretty powerful maybe giving another example here flipping the levers can you think of an example where the lever is more on the gist side of the things and it's a bit serious or can you give us maybe another example where it's more just said so another of my favorite examples was a chart originally made back in 2012 by Simon Scott and this was a chart showing conflict related deaths in Iraq from 2003 to 2011. right so not a happy data set right now I'm going to try and explain this the chart that Simon published was a simple bar chart files were pointing down and they peaked in the center of the bar and then he colored them a deep blood red the appearance as you're looking at this bar chart with a sort of inverted trying to look like a smear of blood dripping down the screen and the title was Iraq's bloody Soul so what scar did was use orientation color and title to create this visceral response to think deeply about the human tragedy of what happened in Iraq in that period now what I realized you could do is if you flip the bars the other way up change it blue you actually begin to see the the number of deaths month by month is decreasing so in that case you could actually change the title so deaths are on the decline and try to tell the story of Hope instead of focus on the tragedy and this is where the lever is really applying on your design you're using those design levers to actually change the message in a story completely just with color and orientation that's an amazing example and definitely we've referenced it in earlier examples where you showed it to us especially on a webinar that you attended with datacamp and I highly recommend the audience to check it out there's one section in your book that I love which covers something called pre-attentive attributes and data visualization and it touches Upon A lot of the Notions they discussed here I think these provide a great framework to think about how a data visualization is perceived and how to best construct one can you walk us through maybe what our pre-attentive attributes and how they impact a data visualization's impact on the audience yeah I fell in love with data visualization back many many years ago and it was part of learning about what cognitive science that really turned me on to it and basically our millions of years of evil you should have Hunter Gathering and trying to spot Tigers moving through the grass of the Savannah and avoid the red dangerous poisonous berries enabled our visual system to we could process the natural environment around us before we consciously think about it and that is a gift of evolution wow so avoiding tigers and finding batteries enables us to be better data analysts yes it's true because think about a bar chart a bar chart has rectangles that are different lengths so length is an example of a pre-incentive attribute and so now what actually is it that our brain looks at the different lengths of those bars and actually identifies which is the longest and which is the shortest before we even look at the bar chart consciously so we've already got a head start to the data before we actually think what am I actually looking at or area you can make circle charts a big circle we could pre-attentively see is bigger than a smaller Circle or colors or Hues so if you're looking for a red dot amongst some gray dots you're going to see the red dots pre-attentively these pre-attentive attributes are the atoms from which we build charts length position color Hue size angle there's loads of them and it's probably beyond the scope of this podcast now but we process some of them better than others which is why pie challenge aren't very good because we don't really do angles and areas very accurately but our brain can super accurately see differences in length of bars for example so yeah pretty attentive attributes once you understand those it unlocks so much yeah I couldn't agree more and you can actually leverage them to your advantage while delivering a presentation for example to guide the audience's attention by using these preventive attributes a great example would be you know if you want to point the audience's attention to one bar in your bar chart you can elect to make everything gray and just highlight color on that bar chart mid presentation the it's so easy to misuse color in visualization or any communication media because every tool available to us today can use an infinite amount of colors but the most powerful visualizations and dashboards are the ones that use gray and one color and then really really powerful okay that's awesome so of course given the book is called the Big Book of dashboards I'd love to actually Deep dive with you on dashboarding as dashboards are one of the most effective ways to share insights with data visualizations within any organization today you know many organizations are leveraging tools like Tableau click power bi to do these dashboards so can you walk us through maybe how dashboards extend the power of data visualization and what you have found are the best practices for creating effective dashboards well I think first we actually we have a semantic challenge but I'll ask You Adele how would you define a dashboard so I would Define a dashboard as a collection of data visualizations aimed at answering a specific set of questions around a specific set of data within an organization how does that definition it's great right have you thought deeply about that in the past or is that is that your first step that's kind of my my first step at it but I'm not a data visualization expert as you Andy oh I'm sure you are I'm sure you are okay so in the dashboard can be defined in a gazillion different ways in our book The Big Book of dashboards our definition is only 15 words long and it's super vague because what we realized is as we were looking at all the dashboards that we could find across Industries is that and there were so many different variations so one example you said a collection of charts in your example but we've got some great dashboards that we think are dashboards they're just a single visualization so every time we try to extend our dashboard definition we could just find more and more caveats to be like oh well this dashboard doesn't fit the definitions what we're going to do so in the end we just collapse the definition to something pretty vague about it's an artifact you used to monitor a system and to facilitate understanding I think it was something like that the reason I don't really know what our definition was is that I don't really care about the semantics right you or the audience we are trying to collect information and present it to a user in such a way that they can make decisions check a process or understand more about whatever it is they're looking at right and if you want to call that dashboard great ultimately a dashboard is a word referring to a piece of wood on a stagecoach anyway so it's a word taken from somewhere else so now we've got our definition well now I've maybe destroyed the definition of dashboards who knows I don't know how do you best create one well you have to go to your audience and really understand very deeply what it is they want to see if they say oh I'd like to monitor sales oh great why would you like to monitor sales I mean it might be are we on target is our quotes are on target this quarter why do you want to measure that and they'll come up with a different answer and believe me when you go to users if you ask why four or five times you know this is Classics of business MBA process you'll get to the root cause of what they want once you know what they want you're like well how do you want to see it are you going to be interacting with this thing do you want this thing delivered in an email are you going to be looking good on a cell phone or on a screen in a call center each of those will determine a different delivery mechanism in a different style so to summary of this the best practice for developing effective dashboards is go and speak to the user and understand what they want why they want it and how they want it and then create a really basic prototype and then they'll go no that's not what I wanted at all and then from there you can iterate until you get the right answer what they need that's really great it's definitely complicated but it's also wonderfully simple and accessible which is what's so nice about dashboarding and data visualization in general so the book contains a lot of examples of dashboards from different Industries and different use cases and you showcase brilliantly why these dashboards are effective can you walk us maybe through the different type of dashboards that you've encountered and maybe expanding onto that what makes each of those dashboards effective yeah I think a big question you have to ask is should the dashboard be interactive or not and I'll focus on that for this answer so if if something's going to be interactive then you've got to start asking well do my users understand how to use this dashboard how do I make sure they literally know how to use this platform you know for Tableau server or Tableau Cloud for example they need to know what the URL is and then once they get there what is it they're looking at even when you put filters on a dashboard how do the users even see them right now this sounds so ridiculous Adele that I can say well it's on the screen surely they'll see them well we've done a bunch of eye tracking studies on dashboards you know a lot of which were taken from the big book dashboards and I could have designed a dashboard with filters on it then we allow people to look at the dashboard themselves and they literally don't even look at the filters so then at the end of the exercise you ask them well why didn't you interact with this stuff and they go I didn't see the filters and the inside I might be screwing but they're on the screen you know they're literally in front of you but understanding that people look at screens in a way that you might not predict it might not be as you'd hope so you've got to ensure they can see the things to interact with in the first place so interactive or not is important we have a great example in the book from Arsenal Football Club one of the Premier League teams in England and they have this static chart based on a player's performance which is delivered to the player after each match but just before the training session and it's a static dashboard delivered to their cell phones so that they could show that to their teammates and have a laugh or have some serious Insight but basically analyze their own performance and that really fundamentally different types of dashboards because one's interactive this is an example that isn't but the really lead to the question is like well is this dashboard effective it goes back to the previous question are we thinking about how the user gets it and what we're trying to achieve so yeah I think interactivity or static is a big decision to make another one is what is the form of delivery is it going to be primarily used on a big screen is it going to be primarily used on a cell phone or is it just going to get delivered in an email again that they will require completely different form factors that you have to take into when you're designing your dashboard that's really great and maybe deep diving here a bit more what strikes me from your answers there are really two main considerations people need to have when thinking about the dashboards they need to create one is the audience right what is the audience expected to achieve with this and then secondly what is the format and the user experience of consumption of dashboard in an organization what are the different types of audiences in a nutshell basically of personas that may expect to interact with the dashboard someone is developing and what is often the ideal way of presenting that information the types of audiences it's difficult to try and summarize that as a capsule but one example I often see is your Executives right let's think about let's go back and mentioned sales earlier let's think about sales an executive CEO or a head of Revenue they want to see what is the aggregated role at Revenue this quarter compared to last quarter compared to Target and compared to this time last year right they want to see this thing rolled up to a very high level of aggregation and they'll have kpis which will show whether they're on Target and then yeah you know some slightly disaggregated breakdown Maybe by region or by product area or something like that that dashboard is useless for the account executive who is actually trying to use data to Target an account so an account executive completely different experience you know maybe they have five or six accounts and one of the accounts is a leading car retailer or something right the executive's dashboard is utterly useless to this person they have to be able to take the same data set and so was maybe they can use data to tailor an account plan to Target that account you know what of the users in that organization been doing have they been looking at our website have they been designed training courses what have we sold to them previously are there any opportunities that have been one or closed recently and so it's the same data set but the dashboard the account executive needs is completely different to the dashboard the exact news and the reason I use this example is sometimes we see in organizations the executives go we have done business intelligence we are successful because I have an executive dashboard hey team everybody is the executive dashboard because it reveals a complete misunderstanding of data culture and data literacy to think that hey that's what you've got is great what about all the people below you in the organization you've got to think for those people too so that's one example that's really great and I'm excited to expand on The Daily literacy component here and maybe on the user experience before we move on to the next question what are things that you would expect need to be considerations as part of a dashboard design process that would be from a user experience perspective so for example one thing that came to mind I was reading an article about this recently is just how important load times are for a dashboard or to be able to be consumed right and this is not an interactivity decision not an aesthetic decision you could have one of the most well-designed dashboards of all time but if it takes more than five to seven seven seconds to load it could really hurt the amount of times it's actually used yeah I think we might have a question later about if database is the gateway drug what else do you need to consider and this is where these things have to be considered I know in Tableau for example if you build a tableau dashboard and keep it Bare Bones super simple and you've got a well-formatted data set and a big nicely resourced server the load time will be really fast if you get carried away as a designer and start thinking I'm going to bring you loads of bells and whistles and background images and do all the really bespoke calculations and add a lot of interactivity for example yeah yeah yeah then you're actually then beginning to put more of a burden on the server and that dreaded load time begins to increase and this is one of the challenges and certainly something when I used to be a dexa analyst when I was a customer of Tableau before 2011. I used to get carried away with building elaborate dashboards that are really intricate but they took forever to load so you have to sometimes recognize that the enthusiastic designer that is inside you trying to build these wonderful experiences has to be balanced with the need to create something that actually doesn't lose people when they're trying to load it it's a great question an important thing to think about yeah that's awesome so of course the other side of things here Beyond user experience and Beyond design is the ability to create a narrative right communicating Data Insights and data storytelling it's extremely important when crafting data visualizations and dashboards so can you walk us through maybe how to effectively embed a narrative within a dashboard and how to convey that insight to a consumer guys I've been resurrecting the talk I did back in 2014 about a dashboard I built which is very much inspired by Dilbert all right what's Gilbert now think about Dilbert comic strip the weekday strip is a three panel comic strip panel one is an introduction to the joke panel two is the joke and the third panel is like some sort of epilogue which hopefully adds and builds on The Jig itself that's an amazing story structure introduce the plot conclude the plot write an epilogue right I built dashboards that follow that story structure three panel dashboard oriented horizontally introduce the data deliver the punchline and create an epilogue all right now but that was for a particularly bespoke situation but the reason I use this example is because dashboards are made of panels containing charts that are in some sort of a grid like structure and they should be in some sort of a grid like structure and where do we see that we see that in comic books right and what do we do in comic books in the Western World we read them from the top left to the right well we read them from the top left to the bottom right in a sort of a linear structure so if you want to form a narrative in a dashboard a really good starting point is to think like a comic strip so in the top left again I'm talking about Western left to right reading cultures here the top left contains the super summary and then from there you can lead the user left right and down into more details so whatever's in the bottom right is kind of the most granular level detail which is where you've got the deepest level of context now that's not a universal way of Designing dashboards that reminding way of doing things but that's one example go and read some Comics basically I highly recommend as well they're incredible mediums to convey storytelling visuals so of course now we covered kind of the main skills when it comes to creating effective dashboards visualizations and narrative but I think given that it's data literacy month I also be remiss not to talk about the connection between the illiteracy and data visualization both from an individual perspective and from an organizational perspective you know I said at the beginning of our episode together as you alluded to as well earlier data visualization is often called the gateway drug to more complex data tasks right so can you walk us through maybe from your perspective how consuming data visualizations and learning about data visualization enables better data literacy within organizations and more thoughtful conversations around data well I think when you have good data set and you're building charts you know your dashboards for example are successful when users come to you and ask more questions but oh I've seen the sales by region thank you very much now What's Happening by product but that's a sign the day of a gotta looked at your visuals understood it and then it's inspired a second level question and that is a sign of success you can't always answer every single second level question you shouldn't aim to do that because you can't anticipate all the questions users are going to have but that's a sign people are engaging right so that's on dashboards as you start bringing data into presentations or even just into meetings where you're throwing data around on screens in an ad hoc manner you can start querying data very very quickly and getting answers instantly I hear customers a lot say one of the problems we have Andy is we spend 15 minutes of the every meeting arguing about the data and I've always thought isn't that kind of the goal and I realized it's a problem if the argument about the data is because you don't agree with where the data's come from and you don't agree about the truth of the data obviously we've got to solve that but I'd love to meetings to be let's go in we're going to try and continue sales how are we going to improve sales this quarter right well let's play with the data let's explore which bits are underperforming or outperforming and let's have that conversation and argue about what the date was saying to come out with the decision so I think that's a really high level of maturity where data is just the fabric of the conversation and data is easy to access easily understood by everybody and driving questions as they arise which dashboards can't do dashboards can only answer questions you already thought about what are you going to do to answer today's question yeah that's really great data visualization skills what's so nice about them as well is that they give you that confidence to be able to criticize a data artifact right because especially visualizations they tend to present themselves as a matter of fact as ground truth right because it's beautifully visualized it's there in your face and having those skill set really equips you to become much more constructive and also much more critical of data yeah that's that's so good and a really important part of that is empathy as well because I'll tell you a little story I used to run a Blog called decipheredreality.com and it analyzed data about the board game Arkham Horror Arkham Horror the card game right it's a nerdy collectible card game where you build decks to take your characters through a scenario against Lovecraft and spider monsters but hey what an awesome game right there's an entire website dedicated to which cards are used in which kind of depth and I started building charts based on the data in this website for fun right and because it was I love the game anyway so I built the chart so here's the most common used card here's the least common used car blah blah blah and then somebody on Reddit replied yes I don't think you understand data visualization analytics should be about this that and the other and you're only sharing the first level of insight I recommend you do this thanks for your advice I'll think about that when I write my second book and when I appear on another set of podcasts and when I do another set of Keynotes to teach another 10 000 people but what that person failed to do let's show the empathy of what was I trying to achieve I was just trying to achieve a little bit of fun looking at a super basic charts I'm going here's the basic thing take the data for what it's worth and have some fun and he had kind of gone well he's seen that and then he's going well I've got these are the 10 questions damn it he's not answered answer these questions he's a failure I only answered the first question now you go and do the work if you're so bothered about it so anyway I could now after three years of hurt laugh about that story but the point being that person was criticizing in a non-ampathetic way and I think data literature mature data literacy knows how to criticize thinking about the designer's intent and which what's the actual goal of the visualization they're critiquing right so ah there you go shared my story yeah that's really great I appreciate the vulnerability I do so given how important data visualization skills are you know what are the ways you recommend to people within an organization to become better at visualizing data and also consuming data visualizations well I guess first of all I'd say listen to the data Camp podcast get involved in data literacy months and all the resources that are there for you right so that's a given but also just practice practice practice data visualization and getting good at building dashboards and being able to communicate effectively with data it is an art and a skill it's technical it's art related and you know an artist is not successful from day one an author is not successful from day one a coder is not successful from day one they have tried they've played they've succeeded they've failed and every single step they're taking is teaching them a little bit more there are free tools such as Tableau public you can just download it or use it on the browser connect the data and get going and every time you build something you are learning on top of that there's really rich and active communities you can get involved with one example in the Tableau Community is something called back to viz Basics every two weeks it's like here's a really basic data set and the challenge is build a bar chart or build a scatter plot it doesn't get easier than that and so the barrier eventually is super super low because you can learn that skill in about half an hour but then you could connect to all the people who've done it the same task that month and believe me build a scatter plot with this data set will generate infinite different types of Scatter Plots right so practice get involved in free tools obviously tablet public isn't the only one and then essentially steal like an artist still like an artist was a great book by Austin but his manifest it was in order to become a good artist or in this case a database designer you can go and get inspiration from expert practitioners and sort of copy their work in a way which is not plagiaristic right copy something for inspiration and with humility so I think get involved download something for free and be inspired by the work of others that's definitely the case and I really appreciate these insights and advice and now Andy as we near the end of our episode today I'd love to ask you what are you up to next and where can audiences find you and given your position at Tableau what are future Trends and releases in the business intelligence space that you are excited about so I've recently got promoted so I've seen your data evangelist at Salesforce now and I'm excited to bring data visualization and data culture understanding to salesforces customers and Prospects which is a really big platform so I'm excited about that a time of recording I'm going to be going on a sabbatical soon so at the end of that I'm beginning to work on a new book can't say very much now but you've heard me talking for an hour and you've got an idea about what I'm passionate about so just keep your eyes peeled on that so that's me and then future business intelligence Trends I think what we're seeing in our area is the Tableau and other tools like power bi and click create tools which in the hands of expert analysts can turn data into anything what I think Tableau hoped 10 years ago is that anybody can learn to use tablet what we've learned is that actually analysts like to use Tableau on people who are analytically inclined like to use tablet right so they put in the effort to learn the platform however not everybody has that inclination to spend the time learning the internets of using this platform so it's how do you bring the power of say Tableau or any analytics platform to people who for whatever reason can't invest the time into learning that drag and drop experience and we're doing it through things like ask data which is a natural language interface to Tableau and we've been working on it for years and the latest iterations are are even beginning to tempt me you know a 15-year veteran of using Tableau away from the Native Tableau interface I'm just going to type a sentence like Martin Google and then Tinker with the words in that sentence to take it with the view so I think our goal is to try and bring the power of analytics to people who aren't analysts you know that's things to ask data and bringing data to the user rather than asking them to go to a destination to see data so yeah that's Trend I'm excited about it's very exciting to see the trend of conversational interfaces and all sorts of analytics tools like even you see it in open source programming languages like python you now have something like God X and you say hey I need this data set I need a function that creates that and it creates it for you as well so this is going to be very exciting towards the democratization of data and the democratization of Data Insights in general yeah I I don't have you been using the AI generating artwork yeah I have I have dally too yeah yeah oh yeah sorry it's dally not more yeah yeah they're amazing right and that kind of AI that language interpretation you know that's what we're trying to bring to data as well that's awesome so Andy it was amazing having you back on the show is there any final call to action before wrap up today yeah I guess it's like first of all day to Camp Dayton literacy month I think what you're doing is fantastic at direct Camp so I support everything that's going on there second just start your own Journey if you are thinking about starting your journey just download a day to say try it and see what you can find from it and I guess if people want to follow me I have a newsletter called The Sweet Spot which will get renamed soon but that's slightly to do with the book but currently called The Sweet Spot and I'm sure we'll put links to that in show notes awesome thank you so much Andy for coming on data friend my absolute pleasure thanks Adele I think you did a great job and I hope you all enjoyed you've been listening to data framed a podcast by datacamp keep connected with us by subscribing to the show in your favorite podcast player please give us a rating leave a comment and share episodes you love that helps us keep delivering insights into all things data thanks for listening until next time\n"