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10 Steps to Data Vizardry

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Best practices for creating data visualizations and communicating with data. Includes sections on knowing your audience, choosing the right chart, advanced data visualizations, simplifying your …

Best practices for creating data visualizations and communicating with data. Includes sections on knowing your audience, choosing the right chart, advanced data visualizations, simplifying your content, and telling a story with data.

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  • Edit notes:\nStories about kids...Owen in tai kwan do\nCustomize introduction\nShow examples of juice dashboards\nSmile & energy\nSet up text message service...text mac to 44124\nAdd closing statement, inspire\n\nStephen Few presentation\n\nhttp://www.visualisingdata.com/index.php/2011/05/tableau-european-conference-freakalytics-update-day-2/\n\nReview Ken’s presentation for good content:\n* Context section\n* Weather videos\n
  • Today’s presentation is really based on three premises -- each a little more speculative than the last...I hope you’ll go with me\n1. We live in a world swimming in data and the ability to analyze and communicate that data is an important skill\n1. There are people, we’ll call them Data Vizards, who have developed the skill to communicate effectively using data.\n2. You can become more proficient at this by applying some basic skills\nThe third point is particularly important: I’m not saying you need to have an innate talent or study for 10,000 hours. It is within your grasp.\n\n\n
  • Today’s presentation is really based on three premises -- each a little more speculative than the last...I hope you’ll go with me\n1. We live in a world swimming in data and the ability to analyze and communicate that data is an important skill\n1. There are people, we’ll call them Data Vizards, who have developed the skill to communicate effectively using data.\n2. You can become more proficient at this by applying some basic skills\nThe third point is particularly important: I’m not saying you need to have an innate talent or study for 10,000 hours. It is within your grasp.\n\n\n
  • Beginner: Excel defaults or I have people who do that for me\nIntermediate: I’m effective with data presentation, but \nExpert: I’m a Tufte disciple; I will take over the presentation from here\n\nThis presentation is intended to give you a rapid-fire tour of the skills that I believe are essential to being going at communicating with data. It is a “survey” course if you will. There is a lot in here and I hope it can help you find some areas to apply to your work.\n
  • Beginner: Excel defaults or I have people who do that for me\nIntermediate: I’m effective with data presentation, but \nExpert: I’m a Tufte disciple; I will take over the presentation from here\n\nThis presentation is intended to give you a rapid-fire tour of the skills that I believe are essential to being going at communicating with data. It is a “survey” course if you will. There is a lot in here and I hope it can help you find some areas to apply to your work.\n
  • Beginner: Excel defaults or I have people who do that for me\nIntermediate: I’m effective with data presentation, but \nExpert: I’m a Tufte disciple; I will take over the presentation from here\n\nThis presentation is intended to give you a rapid-fire tour of the skills that I believe are essential to being going at communicating with data. It is a “survey” course if you will. There is a lot in here and I hope it can help you find some areas to apply to your work.\n
  • “I wanna be like Mike” -- a generation of basketball players grew up \n
  • “I wanna be like Mike” -- a generation of basketball players grew up \n
  • A generation of basketball players grew up with a role model named Mike; he inspired them with his skill and competitiveness.\nA generation of visualization students needs to find their own role models...\n\nA few days ago I was sitting across from an executive who was quizzing me on my “credentials” for designing data visualizations\nI’m not sure if he liked my answer: I’ve just seen a lot. I have a good sense of what’s possible (almost everything) and the many different ways\nthat talented people have create data visualizations. Don’t be limited by your imagination or what you’ve seen. \nThere are a ton of great resources -- I just want to share a few. In my mind, the gurus in this area tend to exist along two dimensions:\n1. Those that focus on the RIGHT WAY to present data.\n2. Those that focus on CREATIVE NEW WAYS to present data.\n\n
  • A generation of basketball players grew up with a role model named Mike; he inspired them with his skill and competitiveness.\nA generation of visualization students needs to find their own role models...\n\nA few days ago I was sitting across from an executive who was quizzing me on my “credentials” for designing data visualizations\nI’m not sure if he liked my answer: I’ve just seen a lot. I have a good sense of what’s possible (almost everything) and the many different ways\nthat talented people have create data visualizations. Don’t be limited by your imagination or what you’ve seen. \nThere are a ton of great resources -- I just want to share a few. In my mind, the gurus in this area tend to exist along two dimensions:\n1. Those that focus on the RIGHT WAY to present data.\n2. Those that focus on CREATIVE NEW WAYS to present data.\n\n
  • A generation of basketball players grew up with a role model named Mike; he inspired them with his skill and competitiveness.\nA generation of visualization students needs to find their own role models...\n\nA few days ago I was sitting across from an executive who was quizzing me on my “credentials” for designing data visualizations\nI’m not sure if he liked my answer: I’ve just seen a lot. I have a good sense of what’s possible (almost everything) and the many different ways\nthat talented people have create data visualizations. Don’t be limited by your imagination or what you’ve seen. \nThere are a ton of great resources -- I just want to share a few. In my mind, the gurus in this area tend to exist along two dimensions:\n1. Those that focus on the RIGHT WAY to present data.\n2. Those that focus on CREATIVE NEW WAYS to present data.\n\n
  • A generation of basketball players grew up with a role model named Mike; he inspired them with his skill and competitiveness.\nA generation of visualization students needs to find their own role models...\n\nA few days ago I was sitting across from an executive who was quizzing me on my “credentials” for designing data visualizations\nI’m not sure if he liked my answer: I’ve just seen a lot. I have a good sense of what’s possible (almost everything) and the many different ways\nthat talented people have create data visualizations. Don’t be limited by your imagination or what you’ve seen. \nThere are a ton of great resources -- I just want to share a few. In my mind, the gurus in this area tend to exist along two dimensions:\n1. Those that focus on the RIGHT WAY to present data.\n2. Those that focus on CREATIVE NEW WAYS to present data.\n\n
  • Stephen Few would probably acknowledge that he is the “Angry Curmudgeon” Professor of Data Visualization\nNo one offers the sustained rage against poor chart design. Despite that, he is an entertaining read and tireless worker for better display of data\n
  • Then there are the people who are data artists. They create amazing and beautiful visualizations that offer creative new ways to communicate data.\nThey have a lot less interest than Stephen Few in sticking to the book and making every pixel count.\nNevertheless, they are a great source of inspiration. \n\n
  • Finally, in our opinion, nobody beats the New York Times design group for their ability to create easy to understand, practical, yet innovative visualizations.\nThey are the gold standard. Here are a few examples.\n
  • Finally, in our opinion, nobody beats the New York Times design group for their ability to create easy to understand, practical, yet innovative visualizations.\nThey are the gold standard. Here are a few examples.\n
  • Finally, in our opinion, nobody beats the New York Times design group for their ability to create easy to understand, practical, yet innovative visualizations.\nThey are the gold standard. Here are a few examples.\n
  • Finally, in our opinion, nobody beats the New York Times design group for their ability to create easy to understand, practical, yet innovative visualizations.\nThey are the gold standard. Here are a few examples.\n
  • By this I mean: whatever tool you use to present data -- Excel, Tableau, Spotfire, PowerPoint, ...it is worth your while to get good at it.\nYou may be saying: I’ve got someone for that. Or I don’t want to be an Excel jockey.\nThat may be true, but putting in the time has a couple benefits...\n
  • By this I mean: whatever tool you use to present data -- Excel, Tableau, Spotfire, PowerPoint, ...it is worth your while to get good at it.\nYou may be saying: I’ve got someone for that. Or I don’t want to be an Excel jockey.\nThat may be true, but putting in the time has a couple benefits...\n
  • First, it will literally save you time and frustration. Am I assigning homework?\nRather than passing of the responsibility, you have an opportunity to save time, frustration\nData presentation is a long term skill\nPeople say: I want to do great visualizations -- but I don’t want to have to work at it\n\n
  • The second benefit comes when you start to push back the boundaries of what are possible.\nTake Excel (again) as an example. A lot of people would assume it is only good for basic charts.\nWe wanted to challenge that notion...\n
  • We took a couple beautiful visualizations done by the NYT, and recreated them in Excel\nNow that you’ve been inspired by the experts, and you know that you aren’t being held back by your tools.\n
  • This advice is a staple in every presentation about presenting\nKnow who you are communicating to, what their issues are, and how they want to receive information\nRelated concept is the need to have a message -- not just a data dump\n
  • This advice is a staple in every presentation about presenting\nKnow who you are communicating to, what their issues are, and how they want to receive information\nRelated concept is the need to have a message -- not just a data dump\n
  • People look at the world from the perspective of their job role, their relationships, their location, and their experience.\nTo fully connect with them, you need to understand their context and present not just data, but data that translates into meaningful information in a way that fits their needs and ability\nThese are two very different people\nThe marketing analyst might want to know how each campaign performed, but the CMO might just be interested in the overall performance and the best and worst performers.\nOf course, understanding your audience isn’t enough...you’ve got to have something specific to tell them. A message. \n
  • Who knows what the message is for this dashboard? Of course not. There is no message. There’s lots of great information here, shown in some pretty cool ways. But what is it helping me do? Or is it actually leaving me more distracted and confused than before I looked at it? This is the same problem that many information presentations suffer from.\n
  • If you browse to goingtorain.com, it will tell you in a very simple manner “yes” or “no” based on your location. They’ve remained ruthlessly committed to their message. When look at data in our businesses, isn’t this really what we’re trying to get to? Should we hire another developer? Are we going to meet our sales objectives? Will the delivery be on time? Start with your message in mind and make sure, if you don’t do anything else, that you deliver that message.\n
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  • start at :33 sec to end\nhttp://www.youtube.com/watch?v=zSoHkadTAxc\n\nRecently, one of our Juice guys wrong a blog post about the difference between being a data gourmet vs. a data gourmand.\n\n\n
  • The Gourmet values data quality -- the right metrics, in the right context, presented effectively\n\nThe Gourmand is more interested in quantity. Comprehensiveness, more is better...in part because they aren’t quite sure what they’ll do with it.\n\nThis focus on the right data was best summarized by Amanda Cox\n
  • Your challenge: how do you help people focus on JUST THE INFORMATION THAT IS ACTIONABLE.\neverything else is distracting.\n\nActionable has as much to do with the recipient as the information -- is it something they have the power or influence to act on?\n\nWeather information, though, offerings an example. I took a look at the weather.com site a while back:\n
  • Your challenge: how do you help people focus on JUST THE INFORMATION THAT IS ACTIONABLE.\neverything else is distracting.\n\nActionable has as much to do with the recipient as the information -- is it something they have the power or influence to act on?\n\nWeather information, though, offerings an example. I took a look at the weather.com site a while back:\n
  • Your challenge: how do you help people focus on JUST THE INFORMATION THAT IS ACTIONABLE.\neverything else is distracting.\n\nActionable has as much to do with the recipient as the information -- is it something they have the power or influence to act on?\n\nWeather information, though, offerings an example. I took a look at the weather.com site a while back:\n
  • Your challenge: how do you help people focus on JUST THE INFORMATION THAT IS ACTIONABLE.\neverything else is distracting.\n\nActionable has as much to do with the recipient as the information -- is it something they have the power or influence to act on?\n\nWeather information, though, offerings an example. I took a look at the weather.com site a while back:\n
  • The last section was about Choosing the Right Data; Now let’s discuss Choosing the Right Chart\n
  • The last section was about Choosing the Right Data; Now let’s discuss Choosing the Right Chart\n
  • Hook: This is the most common question -- the holy grail of data visualization. \n(There are some good frameworks -- notably Andrew Abela's)\nHowever, I'm afraid I can't offer you a magic bullet or comprehensive framework...The best I can do today is help you understand WHAT MAKES AN EFFECTIVE CHART\n\nDoing this well -- the most important attributes of your data (based on the question you want to answer)\nmapped to the visual elements that most effectively convey that information...that is the challenge\n
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  • {Bring up Michel, have audience compare things}\n\nAND if you use any of these, people will perceive that they have meaning....\n
  • {Bring up Michel, have audience compare things}\n\nAND if you use any of these, people will perceive that they have meaning....\n
  • {Bring up Michel, have audience compare things}\n\nAND if you use any of these, people will perceive that they have meaning....\n
  • Because we struggle so much to define a path to the right chart, we find it a lot easier to pick apart charts that are clearly wrong\nTwo things wrong:\n1. Add up to more than 100%\n2. Pie charts depend on radial distance, rather than length -- and this is exacerbated when in 3-D\n
  • Fail because it uses connectors between the points -- which imply a relationship. Just because they are next to each other, doesn’t mean a relationship exists.\n
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  • If you go to sites like inforgraphics you’ll see daily examples that are \n\nintriguing , [click]\ninformative, [click]\ncurious, [click] \nthought provoking, [click]\n and beautiful\n\nBut as far as we’re concerned, a visualization technique really isn’t worth considering for your own work unless they are:\n1. Easy for people to grasp\n2. Solves a common problem\n3. Reproducible -- i.e. you don’t need to be a programmer or artist to render them with new data, usable in multiple situations\nCreate opportunities to answer more complex questions that many standard charts cannot\n
  • If you go to sites like inforgraphics you’ll see daily examples that are \n\nintriguing , [click]\ninformative, [click]\ncurious, [click] \nthought provoking, [click]\n and beautiful\n\nBut as far as we’re concerned, a visualization technique really isn’t worth considering for your own work unless they are:\n1. Easy for people to grasp\n2. Solves a common problem\n3. Reproducible -- i.e. you don’t need to be a programmer or artist to render them with new data, usable in multiple situations\nCreate opportunities to answer more complex questions that many standard charts cannot\n
  • If you go to sites like inforgraphics you’ll see daily examples that are \n\nintriguing , [click]\ninformative, [click]\ncurious, [click] \nthought provoking, [click]\n and beautiful\n\nBut as far as we’re concerned, a visualization technique really isn’t worth considering for your own work unless they are:\n1. Easy for people to grasp\n2. Solves a common problem\n3. Reproducible -- i.e. you don’t need to be a programmer or artist to render them with new data, usable in multiple situations\nCreate opportunities to answer more complex questions that many standard charts cannot\n
  • If you go to sites like inforgraphics you’ll see daily examples that are \n\nintriguing , [click]\ninformative, [click]\ncurious, [click] \nthought provoking, [click]\n and beautiful\n\nBut as far as we’re concerned, a visualization technique really isn’t worth considering for your own work unless they are:\n1. Easy for people to grasp\n2. Solves a common problem\n3. Reproducible -- i.e. you don’t need to be a programmer or artist to render them with new data, usable in multiple situations\nCreate opportunities to answer more complex questions that many standard charts cannot\n
  • If you go to sites like inforgraphics you’ll see daily examples that are \n\nintriguing , [click]\ninformative, [click]\ncurious, [click] \nthought provoking, [click]\n and beautiful\n\nBut as far as we’re concerned, a visualization technique really isn’t worth considering for your own work unless they are:\n1. Easy for people to grasp\n2. Solves a common problem\n3. Reproducible -- i.e. you don’t need to be a programmer or artist to render them with new data, usable in multiple situations\nCreate opportunities to answer more complex questions that many standard charts cannot\n
  • If you go to sites like inforgraphics you’ll see daily examples that are \n\nintriguing , [click]\ninformative, [click]\ncurious, [click] \nthought provoking, [click]\n and beautiful\n\nBut as far as we’re concerned, a visualization technique really isn’t worth considering for your own work unless they are:\n1. Easy for people to grasp\n2. Solves a common problem\n3. Reproducible -- i.e. you don’t need to be a programmer or artist to render them with new data, usable in multiple situations\nCreate opportunities to answer more complex questions that many standard charts cannot\n
  • If you go to sites like inforgraphics you’ll see daily examples that are \n\nintriguing , [click]\ninformative, [click]\ncurious, [click] \nthought provoking, [click]\n and beautiful\n\nBut as far as we’re concerned, a visualization technique really isn’t worth considering for your own work unless they are:\n1. Easy for people to grasp\n2. Solves a common problem\n3. Reproducible -- i.e. you don’t need to be a programmer or artist to render them with new data, usable in multiple situations\nCreate opportunities to answer more complex questions that many standard charts cannot\n
  • If you go to sites like inforgraphics you’ll see daily examples that are \n\nintriguing , [click]\ninformative, [click]\ncurious, [click] \nthought provoking, [click]\n and beautiful\n\nBut as far as we’re concerned, a visualization technique really isn’t worth considering for your own work unless they are:\n1. Easy for people to grasp\n2. Solves a common problem\n3. Reproducible -- i.e. you don’t need to be a programmer or artist to render them with new data, usable in multiple situations\nCreate opportunities to answer more complex questions that many standard charts cannot\n
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  • Raise your hand if you are familiar with this visualization? [audience participation]\nIt is call the Map of the Market and it shows market sectors and individual companies\n* size of each box represents market cap\n* color of each box represents the change for the day.\n\nTreemaps aren’t exactly simple\nThey are one of the most effective ways to show complex data\n\nHere’s what they do well:\n1. Show the full picture\n2. Zoom down to detail\n3. See what is big...and therefore important\n4. See what is changing, up and down.\n\n
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  • One of your central challenges in communicating data is battling the inertia of complexity -- more data, more reports, more visual mess\n\nToo complex turns people off (iPod vs. MP3 players)\nBut, inertia drives complexity\n“We should show this, that, and the other thing” -- have you ever asked around for what people want to see--no shortage of ideas.\nRules of thumb...\nYou won’t be there to explain it\nIf you are explaining the chart and not the data, you’ve lost your way\n
  • What are some ways to lower barriers to understanding...\n* Distracting graphics:\n* Language:\n* Chart deciphering: If you have to explain it, you’ve lost. E.g. dual axis chart...-- I used to be fine with these.\nWe forget -- our audience has thought a lot less about then data than we have\n\n
  • What are some ways to lower barriers to understanding...\n* Distracting graphics:\n* Language:\n* Chart deciphering: If you have to explain it, you’ve lost. E.g. dual axis chart...-- I used to be fine with these.\nWe forget -- our audience has thought a lot less about then data than we have\n\n
  • Lets talk about “distracting graphical elements”\n\nGood presentation presents only what is necessary; Bond, James Bond. It is clean, tailored like Bond’s immaculate tuxedo.\n\nBad presentation [Click]; well. It’s something else. You can dress up the exact same data in a different suit. The results are distracting. Let’s step back a bit [Click] [Click]. Yes, the suit makes the man.\n\nLet’s just say; here I don’t notice the man, I’m trying to understand what’s going on with his giant red diaper. The clothes distract from the message\n
  • Lets talk about “distracting graphical elements”\n\nGood presentation presents only what is necessary; Bond, James Bond. It is clean, tailored like Bond’s immaculate tuxedo.\n\nBad presentation [Click]; well. It’s something else. You can dress up the exact same data in a different suit. The results are distracting. Let’s step back a bit [Click] [Click]. Yes, the suit makes the man.\n\nLet’s just say; here I don’t notice the man, I’m trying to understand what’s going on with his giant red diaper. The clothes distract from the message\n
  • The primary rule of chart design is to look at every element in your chart and make sure it is supporting the story the data is telling.\n\nThese rules were popularized by Edward Tufte (professor emeritus at Yale).\n\nLet me show you an example of what is possible when we remove ornamentation \n
  • In this example, first the extraneous decoration is removed, then we made sure that all the pixels were working hard.\n
  • Here’s a table. Where’s the tablejunk here? the unnecessary and distracting decorative elements.\n
  • Display the smallest amount of numbers that you can to support the needs of the table. Also notice that we put a header in the top to show the units.\n\nI want to talk a little bit more about data precision\n
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  • [pause] Everybody has preferences about what they think is attractive, right? So why do we think that looks don’t matter when it comes to data visualization? \n\nLet’s take a look look at a few ways to make your data look like a fuzzy kitten.\n
  • Hook: The intersection of people good with data and design is a bit rare. But you don’t need to be a designer -- there are some simple things you can do to “play one on TV”\nTrust me: these are the people I try to hire\n
  • Hook: we’re not all experts at combining colors...\n* Less is more\n* Choosing the right scheme\n* Go to the experts\n
  • Overheated use of color\n
  • Restrained: few colors, subtle\nMeaningful: drawing your attention, color implies information\n
  • Finally, let’s talk just a little about colors. Proper color application can be tricky, but start with knowing about 3 ways to color your data.\n\nThe first is called Sequential coloring. This is used for data that has an order like quartiles or ranges. The resulting colors therefore also have an order\n\nThe second is called Diverging coloring. This model is used when there is a mid point or norm in the data. The two color on the ends show extremes.\n\nThe third is is called Categorical. Use this model when the data is for different items that don’t imply sequence, such as departments, or states.\n\nSo what are some great color sets to use? Cynthia Brewer has created a great site called colorbrewer that has many awesome color sets that you can use.\n\nlean on the experts\n
  • Finally, let’s talk just a little about colors. Proper color application can be tricky, but start with knowing about 3 ways to color your data.\n\nThe first is called Sequential coloring. This is used for data that has an order like quartiles or ranges. The resulting colors therefore also have an order\n\nThe second is called Diverging coloring. This model is used when there is a mid point or norm in the data. The two color on the ends show extremes.\n\nThe third is is called Categorical. Use this model when the data is for different items that don’t imply sequence, such as departments, or states.\n\nSo what are some great color sets to use? Cynthia Brewer has created a great site called colorbrewer that has many awesome color sets that you can use.\n\nlean on the experts\n
  • Finally, let’s talk just a little about colors. Proper color application can be tricky, but start with knowing about 3 ways to color your data.\n\nThe first is called Sequential coloring. This is used for data that has an order like quartiles or ranges. The resulting colors therefore also have an order\n\nThe second is called Diverging coloring. This model is used when there is a mid point or norm in the data. The two color on the ends show extremes.\n\nThe third is is called Categorical. Use this model when the data is for different items that don’t imply sequence, such as departments, or states.\n\nSo what are some great color sets to use? Cynthia Brewer has created a great site called colorbrewer that has many awesome color sets that you can use.\n\nlean on the experts\n
  • Finally, let’s talk just a little about colors. Proper color application can be tricky, but start with knowing about 3 ways to color your data.\n\nThe first is called Sequential coloring. This is used for data that has an order like quartiles or ranges. The resulting colors therefore also have an order\n\nThe second is called Diverging coloring. This model is used when there is a mid point or norm in the data. The two color on the ends show extremes.\n\nThe third is is called Categorical. Use this model when the data is for different items that don’t imply sequence, such as departments, or states.\n\nSo what are some great color sets to use? Cynthia Brewer has created a great site called colorbrewer that has many awesome color sets that you can use.\n\nlean on the experts\n
  • Finally, let’s talk just a little about colors. Proper color application can be tricky, but start with knowing about 3 ways to color your data.\n\nThe first is called Sequential coloring. This is used for data that has an order like quartiles or ranges. The resulting colors therefore also have an order\n\nThe second is called Diverging coloring. This model is used when there is a mid point or norm in the data. The two color on the ends show extremes.\n\nThe third is is called Categorical. Use this model when the data is for different items that don’t imply sequence, such as departments, or states.\n\nSo what are some great color sets to use? Cynthia Brewer has created a great site called colorbrewer that has many awesome color sets that you can use.\n\nlean on the experts\n
  • Finally, let’s talk just a little about colors. Proper color application can be tricky, but start with knowing about 3 ways to color your data.\n\nThe first is called Sequential coloring. This is used for data that has an order like quartiles or ranges. The resulting colors therefore also have an order\n\nThe second is called Diverging coloring. This model is used when there is a mid point or norm in the data. The two color on the ends show extremes.\n\nThe third is is called Categorical. Use this model when the data is for different items that don’t imply sequence, such as departments, or states.\n\nSo what are some great color sets to use? Cynthia Brewer has created a great site called colorbrewer that has many awesome color sets that you can use.\n\nlean on the experts\n
  • Finally, let’s talk just a little about colors. Proper color application can be tricky, but start with knowing about 3 ways to color your data.\n\nThe first is called Sequential coloring. This is used for data that has an order like quartiles or ranges. The resulting colors therefore also have an order\n\nThe second is called Diverging coloring. This model is used when there is a mid point or norm in the data. The two color on the ends show extremes.\n\nThe third is is called Categorical. Use this model when the data is for different items that don’t imply sequence, such as departments, or states.\n\nSo what are some great color sets to use? Cynthia Brewer has created a great site called colorbrewer that has many awesome color sets that you can use.\n\nlean on the experts\n
  • Finally, let’s talk just a little about colors. Proper color application can be tricky, but start with knowing about 3 ways to color your data.\n\nThe first is called Sequential coloring. This is used for data that has an order like quartiles or ranges. The resulting colors therefore also have an order\n\nThe second is called Diverging coloring. This model is used when there is a mid point or norm in the data. The two color on the ends show extremes.\n\nThe third is is called Categorical. Use this model when the data is for different items that don’t imply sequence, such as departments, or states.\n\nSo what are some great color sets to use? Cynthia Brewer has created a great site called colorbrewer that has many awesome color sets that you can use.\n\nlean on the experts\n
  • Finally, let’s talk just a little about colors. Proper color application can be tricky, but start with knowing about 3 ways to color your data.\n\nThe first is called Sequential coloring. This is used for data that has an order like quartiles or ranges. The resulting colors therefore also have an order\n\nThe second is called Diverging coloring. This model is used when there is a mid point or norm in the data. The two color on the ends show extremes.\n\nThe third is is called Categorical. Use this model when the data is for different items that don’t imply sequence, such as departments, or states.\n\nSo what are some great color sets to use? Cynthia Brewer has created a great site called colorbrewer that has many awesome color sets that you can use.\n\nlean on the experts\n
  • Finally, let’s talk just a little about colors. Proper color application can be tricky, but start with knowing about 3 ways to color your data.\n\nThe first is called Sequential coloring. This is used for data that has an order like quartiles or ranges. The resulting colors therefore also have an order\n\nThe second is called Diverging coloring. This model is used when there is a mid point or norm in the data. The two color on the ends show extremes.\n\nThe third is is called Categorical. Use this model when the data is for different items that don’t imply sequence, such as departments, or states.\n\nSo what are some great color sets to use? Cynthia Brewer has created a great site called colorbrewer that has many awesome color sets that you can use.\n\nlean on the experts\n
  • Finally, let’s talk just a little about colors. Proper color application can be tricky, but start with knowing about 3 ways to color your data.\n\nThe first is called Sequential coloring. This is used for data that has an order like quartiles or ranges. The resulting colors therefore also have an order\n\nThe second is called Diverging coloring. This model is used when there is a mid point or norm in the data. The two color on the ends show extremes.\n\nThe third is is called Categorical. Use this model when the data is for different items that don’t imply sequence, such as departments, or states.\n\nSo what are some great color sets to use? Cynthia Brewer has created a great site called colorbrewer that has many awesome color sets that you can use.\n\nlean on the experts\n
  • Finally, let’s talk just a little about colors. Proper color application can be tricky, but start with knowing about 3 ways to color your data.\n\nThe first is called Sequential coloring. This is used for data that has an order like quartiles or ranges. The resulting colors therefore also have an order\n\nThe second is called Diverging coloring. This model is used when there is a mid point or norm in the data. The two color on the ends show extremes.\n\nThe third is is called Categorical. Use this model when the data is for different items that don’t imply sequence, such as departments, or states.\n\nSo what are some great color sets to use? Cynthia Brewer has created a great site called colorbrewer that has many awesome color sets that you can use.\n\nlean on the experts\n
  • Finally, let’s talk just a little about colors. Proper color application can be tricky, but start with knowing about 3 ways to color your data.\n\nThe first is called Sequential coloring. This is used for data that has an order like quartiles or ranges. The resulting colors therefore also have an order\n\nThe second is called Diverging coloring. This model is used when there is a mid point or norm in the data. The two color on the ends show extremes.\n\nThe third is is called Categorical. Use this model when the data is for different items that don’t imply sequence, such as departments, or states.\n\nSo what are some great color sets to use? Cynthia Brewer has created a great site called colorbrewer that has many awesome color sets that you can use.\n\nlean on the experts\n
  • Finally, let’s talk just a little about colors. Proper color application can be tricky, but start with knowing about 3 ways to color your data.\n\nThe first is called Sequential coloring. This is used for data that has an order like quartiles or ranges. The resulting colors therefore also have an order\n\nThe second is called Diverging coloring. This model is used when there is a mid point or norm in the data. The two color on the ends show extremes.\n\nThe third is is called Categorical. Use this model when the data is for different items that don’t imply sequence, such as departments, or states.\n\nSo what are some great color sets to use? Cynthia Brewer has created a great site called colorbrewer that has many awesome color sets that you can use.\n\nlean on the experts\n
  • Finally, let’s talk just a little about colors. Proper color application can be tricky, but start with knowing about 3 ways to color your data.\n\nThe first is called Sequential coloring. This is used for data that has an order like quartiles or ranges. The resulting colors therefore also have an order\n\nThe second is called Diverging coloring. This model is used when there is a mid point or norm in the data. The two color on the ends show extremes.\n\nThe third is is called Categorical. Use this model when the data is for different items that don’t imply sequence, such as departments, or states.\n\nSo what are some great color sets to use? Cynthia Brewer has created a great site called colorbrewer that has many awesome color sets that you can use.\n\nlean on the experts\n
  • Finally, let’s talk just a little about colors. Proper color application can be tricky, but start with knowing about 3 ways to color your data.\n\nThe first is called Sequential coloring. This is used for data that has an order like quartiles or ranges. The resulting colors therefore also have an order\n\nThe second is called Diverging coloring. This model is used when there is a mid point or norm in the data. The two color on the ends show extremes.\n\nThe third is is called Categorical. Use this model when the data is for different items that don’t imply sequence, such as departments, or states.\n\nSo what are some great color sets to use? Cynthia Brewer has created a great site called colorbrewer that has many awesome color sets that you can use.\n\nlean on the experts\n
  • Finally, let’s talk just a little about colors. Proper color application can be tricky, but start with knowing about 3 ways to color your data.\n\nThe first is called Sequential coloring. This is used for data that has an order like quartiles or ranges. The resulting colors therefore also have an order\n\nThe second is called Diverging coloring. This model is used when there is a mid point or norm in the data. The two color on the ends show extremes.\n\nThe third is is called Categorical. Use this model when the data is for different items that don’t imply sequence, such as departments, or states.\n\nSo what are some great color sets to use? Cynthia Brewer has created a great site called colorbrewer that has many awesome color sets that you can use.\n\nlean on the experts\n
  • Finally, let’s talk just a little about colors. Proper color application can be tricky, but start with knowing about 3 ways to color your data.\n\nThe first is called Sequential coloring. This is used for data that has an order like quartiles or ranges. The resulting colors therefore also have an order\n\nThe second is called Diverging coloring. This model is used when there is a mid point or norm in the data. The two color on the ends show extremes.\n\nThe third is is called Categorical. Use this model when the data is for different items that don’t imply sequence, such as departments, or states.\n\nSo what are some great color sets to use? Cynthia Brewer has created a great site called colorbrewer that has many awesome color sets that you can use.\n\nlean on the experts\n
  • Finally, let’s talk just a little about colors. Proper color application can be tricky, but start with knowing about 3 ways to color your data.\n\nThe first is called Sequential coloring. This is used for data that has an order like quartiles or ranges. The resulting colors therefore also have an order\n\nThe second is called Diverging coloring. This model is used when there is a mid point or norm in the data. The two color on the ends show extremes.\n\nThe third is is called Categorical. Use this model when the data is for different items that don’t imply sequence, such as departments, or states.\n\nSo what are some great color sets to use? Cynthia Brewer has created a great site called colorbrewer that has many awesome color sets that you can use.\n\nlean on the experts\n
  • Finally, let’s talk just a little about colors. Proper color application can be tricky, but start with knowing about 3 ways to color your data.\n\nThe first is called Sequential coloring. This is used for data that has an order like quartiles or ranges. The resulting colors therefore also have an order\n\nThe second is called Diverging coloring. This model is used when there is a mid point or norm in the data. The two color on the ends show extremes.\n\nThe third is is called Categorical. Use this model when the data is for different items that don’t imply sequence, such as departments, or states.\n\nSo what are some great color sets to use? Cynthia Brewer has created a great site called colorbrewer that has many awesome color sets that you can use.\n\nlean on the experts\n
  • Finally, let’s talk just a little about colors. Proper color application can be tricky, but start with knowing about 3 ways to color your data.\n\nThe first is called Sequential coloring. This is used for data that has an order like quartiles or ranges. The resulting colors therefore also have an order\n\nThe second is called Diverging coloring. This model is used when there is a mid point or norm in the data. The two color on the ends show extremes.\n\nThe third is is called Categorical. Use this model when the data is for different items that don’t imply sequence, such as departments, or states.\n\nSo what are some great color sets to use? Cynthia Brewer has created a great site called colorbrewer that has many awesome color sets that you can use.\n\nlean on the experts\n
  • Finally, let’s talk just a little about colors. Proper color application can be tricky, but start with knowing about 3 ways to color your data.\n\nThe first is called Sequential coloring. This is used for data that has an order like quartiles or ranges. The resulting colors therefore also have an order\n\nThe second is called Diverging coloring. This model is used when there is a mid point or norm in the data. The two color on the ends show extremes.\n\nThe third is is called Categorical. Use this model when the data is for different items that don’t imply sequence, such as departments, or states.\n\nSo what are some great color sets to use? Cynthia Brewer has created a great site called colorbrewer that has many awesome color sets that you can use.\n\nlean on the experts\n
  • Finally, let’s talk just a little about colors. Proper color application can be tricky, but start with knowing about 3 ways to color your data.\n\nThe first is called Sequential coloring. This is used for data that has an order like quartiles or ranges. The resulting colors therefore also have an order\n\nThe second is called Diverging coloring. This model is used when there is a mid point or norm in the data. The two color on the ends show extremes.\n\nThe third is is called Categorical. Use this model when the data is for different items that don’t imply sequence, such as departments, or states.\n\nSo what are some great color sets to use? Cynthia Brewer has created a great site called colorbrewer that has many awesome color sets that you can use.\n\nlean on the experts\n
  • Finally, let’s talk just a little about colors. Proper color application can be tricky, but start with knowing about 3 ways to color your data.\n\nThe first is called Sequential coloring. This is used for data that has an order like quartiles or ranges. The resulting colors therefore also have an order\n\nThe second is called Diverging coloring. This model is used when there is a mid point or norm in the data. The two color on the ends show extremes.\n\nThe third is is called Categorical. Use this model when the data is for different items that don’t imply sequence, such as departments, or states.\n\nSo what are some great color sets to use? Cynthia Brewer has created a great site called colorbrewer that has many awesome color sets that you can use.\n\nlean on the experts\n
  • Finally, let’s talk just a little about colors. Proper color application can be tricky, but start with knowing about 3 ways to color your data.\n\nThe first is called Sequential coloring. This is used for data that has an order like quartiles or ranges. The resulting colors therefore also have an order\n\nThe second is called Diverging coloring. This model is used when there is a mid point or norm in the data. The two color on the ends show extremes.\n\nThe third is is called Categorical. Use this model when the data is for different items that don’t imply sequence, such as departments, or states.\n\nSo what are some great color sets to use? Cynthia Brewer has created a great site called colorbrewer that has many awesome color sets that you can use.\n\nlean on the experts\n
  • Finally, let’s talk just a little about colors. Proper color application can be tricky, but start with knowing about 3 ways to color your data.\n\nThe first is called Sequential coloring. This is used for data that has an order like quartiles or ranges. The resulting colors therefore also have an order\n\nThe second is called Diverging coloring. This model is used when there is a mid point or norm in the data. The two color on the ends show extremes.\n\nThe third is is called Categorical. Use this model when the data is for different items that don’t imply sequence, such as departments, or states.\n\nSo what are some great color sets to use? Cynthia Brewer has created a great site called colorbrewer that has many awesome color sets that you can use.\n\nlean on the experts\n
  • Finally, let’s talk just a little about colors. Proper color application can be tricky, but start with knowing about 3 ways to color your data.\n\nThe first is called Sequential coloring. This is used for data that has an order like quartiles or ranges. The resulting colors therefore also have an order\n\nThe second is called Diverging coloring. This model is used when there is a mid point or norm in the data. The two color on the ends show extremes.\n\nThe third is is called Categorical. Use this model when the data is for different items that don’t imply sequence, such as departments, or states.\n\nSo what are some great color sets to use? Cynthia Brewer has created a great site called colorbrewer that has many awesome color sets that you can use.\n\nlean on the experts\n
  • Finally, let’s talk just a little about colors. Proper color application can be tricky, but start with knowing about 3 ways to color your data.\n\nThe first is called Sequential coloring. This is used for data that has an order like quartiles or ranges. The resulting colors therefore also have an order\n\nThe second is called Diverging coloring. This model is used when there is a mid point or norm in the data. The two color on the ends show extremes.\n\nThe third is is called Categorical. Use this model when the data is for different items that don’t imply sequence, such as departments, or states.\n\nSo what are some great color sets to use? Cynthia Brewer has created a great site called colorbrewer that has many awesome color sets that you can use.\n\nlean on the experts\n
  • Finally, let’s talk just a little about colors. Proper color application can be tricky, but start with knowing about 3 ways to color your data.\n\nThe first is called Sequential coloring. This is used for data that has an order like quartiles or ranges. The resulting colors therefore also have an order\n\nThe second is called Diverging coloring. This model is used when there is a mid point or norm in the data. The two color on the ends show extremes.\n\nThe third is is called Categorical. Use this model when the data is for different items that don’t imply sequence, such as departments, or states.\n\nSo what are some great color sets to use? Cynthia Brewer has created a great site called colorbrewer that has many awesome color sets that you can use.\n\nlean on the experts\n
  • Next, let’s talk a little about contrast. Contrast is used to highlight key points in your information.\n\nIn practice, this is more often about about de-emphasizing the stuff that isn’t as important. Let’s look at an example. [click]\n
  • Next, let’s talk a little about contrast. Contrast is used to highlight key points in your information.\n\nIn practice, this is more often about about de-emphasizing the stuff that isn’t as important. Let’s look at an example. [click]\n
  • So, how do you ever select the right one? Many times when you try to build a text based information document, you just end up with a Frankenstein of sizes and fonts. That’s why Juice created we’ve created something we call the simple font framework. In four easy steps you’ll be able to convert your Frankenstein into a Venus Demilo\n
  • We start by specifying the body text. This is what the vast majority of your text will be written in. You want to pick a small-ish size of a simple font like Arial or Georgia.\n
  • We start by specifying the body text. This is what the vast majority of your text will be written in. You want to pick a small-ish size of a simple font like Arial or Georgia.\n
  • Next, use headers to separate and name sections. For headers, base this font on the body. If your body font is 10points in size, your header font would be about 15 to 20 points.\n
  • Next, use headers to separate and name sections. For headers, base this font on the body. If your body font is 10points in size, your header font would be about 15 to 20 points.\n
  • Often times there notes that you need to add to summarize content or highlight advanced topics. You want notes to be more subtle than the body, so go with the same font style at about 85% size with a lower contrast color.\n
  • Often times there notes that you need to add to summarize content or highlight advanced topics. You want notes to be more subtle than the body, so go with the same font style at about 85% size with a lower contrast color.\n
  • Finally, for those few situations where you want to highlight some text, apply the emphasis rules we discussed earlier.\n
  • Finally, for those few situations where you want to highlight some text, apply the emphasis rules we discussed earlier.\n
  • And now you know how to apply the Juice Simple Font Framework to tighten up your text so that it looks intentional and not accidental.\n
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  • Data Storytelling is a bit of an exaggeration\nNevertheless, there are things Vizards can learn from the art of storytelling\n
  • {stop before 0:40}\nThe relevant point that I think he makes is that it isn’t good enough to just put your product out there;\nyou need to sell your message; data communication isn’t the same things as data delivery\n\n
  • The standard practice for laying out a dashboard, unfortunately, has been to slot charts into a grid.\nWhen I first look at this, does it help me organize my thinking, or tell me anything about the big picture?\nA dashboard represents something real E.g. a process, organization, activity, campaign, etc.\nFlow has to be looked at from the right perspective/organizational view...otherwise, it just gets a tad overwhelming. - James\nWhat is the structure of your dashboard that helps someone wrap their head around it -- and how does someone navigate through that structure?\n1. Provides a logical structure that reflects the thing the dashboard is about\n2. Provides a workflow that helps users go from common questions to find answers\n
  • Many dashboards are created to show a process or something with a sequence of events\ne.g. sales pipelines, marketing campaign, operational process\nWhy not build your dashboard EXPLICITLY around this process. It gives people something easy to grasp immediately.\n
  • In addition to a structure, you also want to make a WORKFLOW that moves users through the process of exploration.\nIn my experiences, a majority of the time you want to enables a very EXPLICIT DRILL-DOWN from the highest level key metrics to detailed data.\nIn this dashboard, the marketing campaign key metrics at the top then lead the user to information about who saw the campaign, and increasingly levels of detail.\nWorkflow is about the stages you want the user to go through. Let’s look at some of the common workflows\n
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  • You have choices for how data is presented\nMake those choices deliberately -- based on your audience, their needs, the information\n\n
  • You have choices for how data is presented\nMake those choices deliberately -- based on your audience, their needs, the information\n\n
  • You have choices for how data is presented\nMake those choices deliberately -- based on your audience, their needs, the information\n\n
  • You have choices for how data is presented\nMake those choices deliberately -- based on your audience, their needs, the information\n\n
  • consider HOW the data is communicated -- the format in which it is conveyed\n
  • consider HOW the data is communicated -- the format in which it is conveyed\n
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Transcript

  • 1. 10 Steps toData VizardryZach GemignaniCEO + founderJuice Inc.zach@juiceanalytics.com
  • 2. What is a Data Vizard?How do I become one?
  • 3. What is a Data Vizard?Someone with the abilityto tell clear, compellingstories with data.How do I become one?
  • 4. What is a Data Vizard?Someone with the abilityto tell clear, compellingstories with data.How do I become one?By consistently applying aset of straightforwardskills and principles
  • 5. What level are you?
  • 6. What level are you?beginner
  • 7. What level are you?beginner intermediate
  • 8. What level are you?beginner intermediate advanced
  • 9. ACHIEVE THE RIGHT CHARTCHOOSE SIMPLICITYGO A DATA GOURMETBE BEYOND THE BASICSCHOOSE THEAUDIENCEKNOW YOUR RIGHT CHARTLEARN YOUR TOOLSBE A DATA GOURMETLEARN FROM THE BESTKNOW YOUR AUDIENCEDATALEARN YOUR TOOLSVIZARDS...LEARN FROM THE BEST
  • 10. TELL A STORYGO BEYOND THE BASICSACT LIKE THE RIGHT CHART A DESIGNERCHOOSE SIMPLICITYACHIEVE GOURMETBE A DATA THE BASICSGO BEYOND AUDIENCEKNOW YOUR RIGHT CHARTCHOOSE THE TOOLSLEARN YOURBE A DATA GOURMETLEARNYOUR AUDIENCEDATA FROM THE BESTKNOWVIZARDS...LEARN YOUR TOOLSLEARN FROM THE BEST
  • 11. morewhat’s right less
  • 12. morewhat’s right less status quo inspiring what’s possible
  • 13. more Stephen Few New York Timeswhat’s right Jonathan Harris less status quo inspiring what’s possible
  • 14. “eloquence through simplicity” “ Business Objects is a leading business intelligence vendor (based on sales), but its products consistently demonstrate that they don’t understand analytics and haven’t a clue about data visualization. A vendor that claims to be the best, which Business Objects unabashedly claims (just like every other major BI vendor), should be ashamed of selling such moronic products. Don’t reward them for ” irresponsible work—products that assume their customers are halfwits...Stephen Fewwww.perceptualedge.com/blog
  • 15. data visualization as artJonathan Harriswww.wefeelfine.org www.number27.org
  • 16. New York Times
  • 17. New York Times
  • 18. New York Times
  • 19. New York Times
  • 20. New York Times www.smallmeans.com/new-york-times-infographics/
  • 21. TELL A STORYGO BEYOND THE BASICSACT LIKE A DESIGNERCHOOSE THE RIGHT CHARTACHIEVE SIMPLICITYBE A DATA GOURMETGO BEYOND THE BASICSKNOW YOUR AUDIENCECHOOSE THE RIGHT CHARTLEARN YOUR TOOLSBE A DATA GOURMETLEARNYOUR AUDIENCEDATA FROM THE BESTKNOWVIZARDS...LEARN YOUR TOOLSLEARN FROM THE BEST
  • 22. FORM FOLLOWS FUNCTIONACHIEVE SIMPLICITYTELL A STORYGO BEYOND THE BASICSACT LIKE A DESIGNERCHOOSE THE RIGHT CHARTACHIEVE SIMPLICITYBE A DATA GOURMETGO BEYOND THE BASICSKNOW YOUR AUDIENCECHOOSE THE RIGHT CHARTLEARN YOUR TOOLSDATADATA GOURMETBE ALEARNYOUR AUDIENCE FROM THE BESTVIZARDS...KNOWLEARN YOUR TOOLSLEARN FROM THE BEST
  • 23. “ The more technique you have, the less ” you have to worry about it. » Pablo Picasso
  • 24. “ I am always doing that which I cannot do, in order that I ” may learn how to do it.Pablo Picasso »
  • 25. New York Times (Flash)
  • 26. New York Times (Flash)Juice Analytics (Excel)
  • 27. FORM FOLLOWS FUNCTIONACHIEVE SIMPLICITYTELL A STORYGO BEYOND THE BASICSACT LIKE A DESIGNERCHOOSE THE RIGHT CHARTACHIEVE SIMPLICITYBE A DATA GOURMETGO BEYOND THE BASICSKNOW YOUR AUDIENCECHOOSE THE RIGHT CHARTLEARN FROM THE BESTDATADATA GOURMETBE AVIZARDS... AUDIENCEKNOW YOURLEARN YOUR TOOLS
  • 28. ACT LIKE A DESIGNERFORM FOLLOWS FUNCTIONACHIEVE SIMPLICITYTELL A STORYGO BEYOND THE BASICSACT LIKE A DESIGNERCHOOSE THE RIGHT CHARTACHIEVE SIMPLICITYBE A DATA GOURMETGO BEYOND THE BASICSKNOW YOUR AUDIENCEDATACHOOSE THE RIGHT CHARTLEARN FROM THE BESTVIZARDS... GOURMETBE A DATAKNOW YOUR AUDIENCELEARN YOUR TOOLS
  • 29. Chief Marketing Officer Marketing Analyst
  • 30. goingtorain.com
  • 31. ACT LIKE A DESIGNERFORM FOLLOWS FUNCTIONACHIEVE SIMPLICITYTELL A STORYGO BEYOND THE BASICSACT LIKE A DESIGNERCHOOSE THE RIGHT CHARTACHIEVE SIMPLICITYBE A DATA GOURMETGO BEYOND THE BASICSLEARN FROM RIGHT CHARTDATACHOOSE THE THE BESTVIZARDS... GOURMETBE A DATAKNOW YOUR AUDIENCE
  • 32. TELL A STORYACT LIKE A DESIGNERFORM FOLLOWS FUNCTIONACHIEVE SIMPLICITYTELL A STORYGO BEYOND THE BASICSACT LIKE A DESIGNERCHOOSE THE RIGHT CHARTACHIEVE SIMPLICITYBE A DATA GOURMETDATAGO BEYOND THE BASICSLEARN FROM RIGHT CHARTVIZARDS...CHOOSE THE THE BESTBE A DATA GOURMETKNOW YOUR AUDIENCE
  • 33. http://www.youtube.com/watch?v=zSoHkadTAxc
  • 34. Data Gourmet Data Gourmand
  • 35. Data Gourmet Data Gourmand “ Data isn’t like your kids. You don’t ” have to pretend to love them equally. » Amanda Cox, New York Times
  • 36. Interesting < Useful < Actionable
  • 37. Interesting < Useful < Actionable
  • 38. Interesting < Useful < Actionable
  • 39. Interesting < Useful < Actionable
  • 40. TELL A STORYACT LIKE A DESIGNERFORM FOLLOWS FUNCTIONACHIEVE SIMPLICITYTELL A STORYGO BEYOND THE BASICSACT LIKE A DESIGNERCHOOSE THE RIGHT CHARTACHIEVE SIMPLICITYLEARN FROM THE BESTDATAGO BEYOND THE BASICSVIZARDS...CHOOSE THE RIGHT CHARTBE A DATA GOURMET
  • 41. FORM FOLLOWS FUNCTIONTELL A STORYACT LIKE A DESIGNERFORM FOLLOWS FUNCTIONACHIEVE SIMPLICITYTELL A STORYGO BEYOND THE BASICSACT LIKE A DESIGNERCHOOSE THE RIGHT CHARTDATAACHIEVE SIMPLICITYLEARN FROM THE BESTVIZARDS... THE BASICSGO BEYONDCHOOSE THE RIGHT CHARTBE A DATA GOURMET
  • 42. QuestionData parametersChart categoryMapping of datato chart elements
  • 43. How do my sales breakdown by visitor type?
  • 44. How do mysales break salesdown by visitor type? value/ metric
  • 45. How do mysales break sales visitor type down by visitor type?attribute/ value/dimension metric
  • 46. How do mysales break down sales break visitor type down by visitor type?attribute/ value/ chartdimension metric category
  • 47. How do mysales break down sales break visitor type down by visitor type?attribute/ value/ chartdimension metric category What is the trend in visitors across time?
  • 48. How do mysales break down sales break visitor type down by visitor type?attribute/ value/ chartdimension metric category visitors What is the trend in visitors across time?
  • 49. How do mysales break down sales break visitor type down by visitor type?attribute/ value/ chartdimension metric category visitors What is the trend in visitors across time time?
  • 50. How do mysales break down sales break visitor type down by visitor type?attribute/ value/ chartdimension metric category What is thetrend in visitors trend visitors across time time?
  • 51. Chart categoriesDistributionComparisonComposition www.extremepresentation.com/ design/charts/RelationshipTrend www.chartchooser.com
  • 52. People perceive...
  • 53. People perceive... Accurately Length of a line Position in 2D space Semi-accurately Area Color intensity Radial distance Position in 3D space Not accurately at all Odds of winning in Vegas
  • 54. People perceive... Accurately Length of a line Position in 2D space Semi-accurately Area Color intensity Radial distance Position in 3D space Not accurately at all Odds of winning in Vegas
  • 55. People perceive... Accurately Length of a line Position in 2D space Semi-accurately Area Color intensity Radial distance Position in 3D space Not accurately at all Odds of winning in Vegas
  • 56. Back Palin 70%Back Huckabee 63% Back Romney 60%
  • 57. IntelMicrosoftSamsung Mercury Zeus Reebok Adidas LG Nike Apple Google
  • 58. FORM FOLLOWS FUNCTIONTELL A STORYACT LIKE A DESIGNERFORM FOLLOWS FUNCTIONACHIEVE SIMPLICITYTELL A STORYGO BEYOND THE BASICSACT LIKE A DESIGNEREARN FROM THE BESTDATAACHIEVE SIMPLICITYVIZARDS... THE BASICSGO BEYONDCHOOSE THE RIGHT CHART
  • 59. FORM FOLLOWS FUNCTIONTELL A STORYACT LIKE A DESIGNERFORM FOLLOWS FUNCTIONACHIEVE SIMPLICITYTELL A STORYGO BEYOND THE BASICSDATAACT LIKE A DESIGNEREARN FROM THE BESTVIZARDS...ACHIEVE SIMPLICITYGO BEYOND THE BASICSCHOOSE THE RIGHT CHART
  • 60. Good visualizationtechniques...
  • 61. Good visualizationtechniques... easy to understand solve common problems reproducable
  • 62. small multiplesffctn.com/a/expensevisualizer/
  • 63. treemap www.juiceanalytics.com/demos/airline/
  • 64. unit chart visualization.geblogs.com/visualization/health_visualizer/
  • 65. animated bubble chart www.gapminder.org/world
  • 66. lava lamp chart www.juiceanalytics.com/gallery/tv-top-earner/
  • 67. FORM FOLLOWS FUNCTIONTELL A STORYACT LIKE A DESIGNERFORM FOLLOWS FUNCTIONACHIEVE SIMPLICITYTELL A STORYRN FROM A DESIGNERDATAACT LIKE THE BESTVIZARDS...ACHIEVE SIMPLICITYGO BEYOND THE BASICS
  • 68. FORM FOLLOWS FUNCTIONTELL A STORYACT LIKE A DESIGNERFORM FOLLOWS FUNCTIONACHIEVE SIMPLICITYDATA A STORYTELLRN FROM A DESIGNER THE BESTVIZARDS...ACT LIKEACHIEVE SIMPLICITYGO BEYOND THE BASICS
  • 69. “ A wealth of information creates a ” poverty of attention. » Herbert Simon, Nobel Laureate Economist
  • 70. Distracting graphical elements
  • 71. Distracting graphical elements Complex, jargon-y labels
  • 72. Distracting graphical elements Complex, jargon-y labelsChart deciphering
  • 73. Distracting elements
  • 74. Distracting elements
  • 75. Distracting elements
  • 76. Fundamental rules of chart design Reduce chartjunk Increase data-ink ratio (remove chart elements that are (make every pixel tell a story about your data) decorative or ornamental)
  • 77. Images courtesy of Tim Bray: http://www.tbray.org/ongoing/When/200x/2003/03/13/Data-Ink
  • 78. Reduce tablejunk
  • 79. Increase data-ink ratio
  • 80. FORM FOLLOWS FUNCTIONTELL A STORYACT LIKE A DESIGNERFORM FOLLOWS FUNCTIONTHE BESTDATA A STORYTELLVIZARDS... DESIGNERACT LIKE AACHIEVE SIMPLICITY
  • 81. FORM FOLLOWS FUNCTIONTELL A STORYACT LIKE A DESIGNERDATA FOLLOWS FUNCTIONFORMTHE BESTVIZARDS...TELL A STORYACT LIKE A DESIGNERACHIEVE SIMPLICITY
  • 82. looks matter
  • 83. Data + Design data junkies designers
  • 84. Color
  • 85. Color
  • 86. http://www.fusion41.com/visuals/
  • 87. WebTrends 10restrainedmeaningful
  • 88. Color schemes for data
  • 89. Color schemes for dataSequentialColors can be ordered from low to high Low High
  • 90. Color schemes for dataSequentialColors can be ordered from low to high Low HighDivergingTwo sequential schemes extended outfrom a critical midpoint value Low Critical High midpoint
  • 91. Color schemes for data Sequential Colors can be ordered from low to high Low High Diverging Two sequential schemes extended out from a critical midpoint value Low Critical High midpoint Categorical Lots of contrast between each adjacent colorcolorbrewer2.org
  • 92. Use contrast to supportyour message 80 70 80 Region 1 Region 2 55 58 60 60 Region 3 43 40 40 20 20 0 0 North South East West 2007 2008 2009 2010
  • 93. Use contrast to supportyour message 80 70 80 Region 1 Region 2 55 58 60 60 Region 3 43 40 40 20 20 0 0 North South East West 2007 2008 2009 2010 80 70 55 58 60 43 40 20 0 North South East West
  • 94. Use contrast to supportyour message 80 70 80 Region 1 Region 2 55 58 60 60 Region 3 43 40 40 20 20 0 0 North South East West 2007 2008 2009 2010 80 70 80 Region 1 58 Region 2 60 55 60 Region 3 43 40 40 20 20 0 0 North South East West 2007 2008 2009 2010
  • 95. Fonts - Juice’s Simple FontFramework History of Medicine Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum. Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam,
  • 96. History of MedicineLorem ipsum dolor sit amet, consectetur adipisicing elit, sed doeiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim adminim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip exea commodo consequat. Duis aute irure dolor in reprehenderit involuptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sintoccaecat cupidatat non proident, sunt in culpa qui officia deserunt mollitanim id est laborum. Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam,
  • 97. History of Medicine Body TextLorem ipsum dolor sit amet, consectetur adipisicing elit, sed do Clean readable text,eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad 50-80% of your textminim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex will look like this.ea commodo consequat. Duis aute irure dolor in reprehenderit involuptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint •10-16ptoccaecat cupidatat non proident, sunt in culpa qui officia deserunt mollitanim id est laborum. •Arial or Georgia Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam,
  • 98. History of MedicineLorem ipsum dolor sit amet, consectetur adipisicing elit, sed doeiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim adminim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip exea commodo consequat. Duis aute irure dolor in reprehenderit involuptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sintoccaecat cupidatat non proident, sunt in culpa qui officia deserunt mollitanim id est laborum. Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam,
  • 99. History of MedicineHeader dolor sit amet, consectetur adipisicing elit, sed do Lorem ipsumUse toveniam, quis nostrudut labore et dolore magna aliqua.ut aliquipad eiusmod tempor incididunt minim separate and exercitation ullamco laboris nisi Ut enim exname sections ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non of body sunt in culpa qui officia deserunt mollit •150%-200% proident, anim id est laborum. Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam,
  • 100. History of MedicineLorem ipsum dolor sit amet, consectetur adipisicing elit, sed doeiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim adminim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip exea commodo consequat. Duis aute irure dolor in reprehenderit involuptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sintoccaecat cupidatat non proident, sunt in culpa qui officia deserunt mollitanim id est laborum. Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam,
  • 101. History of MedicineLorem ipsum dolor sit amet, consectetur adipisicing elit, sed doeiusmod tempor incididunt85% of body • ut labore et dolore magna aliqua. Ut enim ad •Lower contrastminim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex Notesea commodo consequat. Duis aute irure dolor in reprehenderit involuptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint Additional thingsoccaecat cupidatat non proident, sunt in culpa qui officia deserunt mollitanim id est user should be a laborum. aware of Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam,
  • 102. History of MedicineLorem ipsum dolor sit amet, consectetur adipisicing elit, sed doeiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim adminim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip exea commodo consequat. Duis aute irure dolor in reprehenderit involuptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sintoccaecat cupidatat non proident, sunt in culpa qui officia deserunt mollitanim id est laborum. Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam,
  • 103. History of MedicineLorem ipsum dolor sit amet, consectetur adipisicing elit, sed doeiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad Emphasisminim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip exea commodo consequat. Duis aute irure dolor in reprehenderit in Draw the eyevoluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint to key pointsoccaecat cupidatat non proident, sunt in culpa qui officia deserunt mollitanim id est laborum. •Same size as body •High-impact color/bold/italic consectetur Lorem ipsum dolor sit amet, adipisicing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam,
  • 104. History of MedicineLorem ipsum dolor sit amet, consectetur adipisicing elit, sed doeiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim adminim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip exea commodo consequat. Duis aute irure dolor in reprehenderit involuptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sintoccaecat cupidatat non proident, sunt in culpa qui officia deserunt mollitanim id est laborum. Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam,
  • 105. FORM FOLLOWS FUNCTIONTELL A STORYBEST FOLLOWS FUNCTIONDATAFORMVIZARDS...TELL A STORYACT LIKE A DESIGNER
  • 106. FORM FOLLOWS FUNCTIONTELL A STORYDATABEST FOLLOWS FUNCTIONVIZARDS...FORMTELL A STORYACT LIKE A DESIGNER
  • 107. setting scope of analysisexposition backgroundcharacters point of viewsequence flow conflict questionsresolution answers
  • 108. Be a salesmanhttp://www.youtube.com/watch?feature=player_embedded&v=wVQPY4LlbJ4
  • 109. What does this tell me?
  • 110. Process
  • 111. Drill-down workflow
  • 112. FORM FOLLOWS FUNCTIONDATAVIZARDS...FORM FOLLOWS FUNCTIONTELL A STORY
  • 113. FORM FOLLOWS FUNCTIONDATAVIZARDS...FORM FOLLOWS FUNCTIONTELL A STORY
  • 114. form paper Excel online app e-mail large screen
  • 115. form paper Excel online app e-mail largefunction screentimelinessaestheticmobilityconnectivitydata detaildata densityinteractivitycollaboration
  • 116. form paper Excel online app e-mail largefunction screentimelinessaestheticmobilityconnectivitydata detaildata densityinteractivitycollaboration
  • 117. LEARN FROM THE BESTLEARN YOUR TOOLSKNOW YOUR AUDIENCEBE A DATA GOURMETCHOOSE THE RIGHT CHARTGO BEYOND THE BASICSACHIEVE SIMPLICITYACT LIKE A DESIGNERTELL A STORYFORM FOLLOWS FUNCTION
  • 118. List of resourcesSkill Resources www.smallmeans.com/new-york-times-infographics/Learn from the best www.perceptualedge.com/blog infographics.alltop.comLearn your tools www.juiceanalytics.com/writing/excel-training-worksheet/Know your audienceBe a data gourmet www.juiceanalytics.com/writing/being-a-data-gourmet/ www.chartchooser.comChoose the right chart www.extremepresentation.com/design/charts/ www.juiceanalytics.com/writing/chart-selection-art-and-science/Go beyond the basics A Tour of the Visualization Zoo (queue.acm.org/detail.cfm?id=1805128)Achieve simplicity Edward Tufte Q&A (www.edwardtufte.com/bboard/q-and-a?topic_id=1) colorbrewer2.orgAct like a designer www.juiceanalytics.com/writing/simple-font-framework/Tell a story www.duarte.com/books/resonate/www/Form follow function
  • 119. we craft applications that make using data enjoyable and rewarding