1 00:00:00,445 --> 00:00:06,490 In our last video, we learned our job as practitioners of data visualization 2 00:00:06,490 --> 00:00:12,824 is to provide context and purpose to data to convert that data into information. 3 00:00:12,824 --> 00:00:16,830 And use that information to tell our audience a meaningful story. 4 00:00:16,830 --> 00:00:20,200 But what does that process look like? 5 00:00:20,200 --> 00:00:22,755 Let's say a colleague hands us an Excel or 6 00:00:22,755 --> 00:00:26,290 Google spreadsheet full of facts and figures. 7 00:00:26,290 --> 00:00:29,150 Are we ready to jump right in and start making a chart? 8 00:00:30,250 --> 00:00:31,650 Not quite. 9 00:00:31,650 --> 00:00:36,532 Before we start thinking visually, we need to take four preparatory steps to 10 00:00:36,532 --> 00:00:39,960 help us get ready to select the right visual. 11 00:00:39,960 --> 00:00:42,910 The first step is identifying your audience. 12 00:00:44,330 --> 00:00:49,857 In a textbook called Good Charts, author Scott Berinato illustrates the importance 13 00:00:49,857 --> 00:00:54,780 of knowing your audience by asking a simple question: is this a good chart? 14 00:00:55,990 --> 00:00:59,370 The presentation seems convincing enough. 15 00:00:59,370 --> 00:01:04,483 The y-axis is labeled clearly spanning from 0 to $50 million. 16 00:01:04,483 --> 00:01:11,332 The x-axis presents the years 2010 to 2013 divided into quarters. 17 00:01:11,332 --> 00:01:14,884 And the chart shows an upward trend in the data. 18 00:01:14,884 --> 00:01:18,695 While not every quarter was equally strong, 19 00:01:18,695 --> 00:01:24,365 it looks like the global revenue increased from about $15 million 20 00:01:24,365 --> 00:01:30,149 in early 2010 to closer to 50 million in 2012 and 2013. 21 00:01:30,149 --> 00:01:36,100 So this is a legible illustration of financial data. But is it a good chart? 22 00:01:37,260 --> 00:01:41,326 The answer is we don't know because we don't know who this chart is being 23 00:01:41,326 --> 00:01:42,970 presented to. 24 00:01:42,970 --> 00:01:47,866 As Berinato notes if you're presenting to the company's board of directors, 25 00:01:47,866 --> 00:01:51,970 this chart is probably insufficiently detailed. 26 00:01:51,970 --> 00:01:56,236 On the other hand, this might be a useful chart in welcoming a new hire to 27 00:01:56,236 --> 00:02:00,590 the company by presenting a broad overview of the company's finances. 28 00:02:02,620 --> 00:02:06,407 In the case of illustrating Dennis Rodman's rebounding dominance, 29 00:02:06,407 --> 00:02:09,250 who do you think these charts are for? 30 00:02:09,250 --> 00:02:14,038 Would a casual fan who watches a few games each year care whether Rodman or 31 00:02:14,038 --> 00:02:17,490 Wilt was the more efficient rebounder? 32 00:02:17,490 --> 00:02:18,870 Probably not. 33 00:02:18,870 --> 00:02:22,239 This information is aimed at hardcore fans with a solid 34 00:02:22,239 --> 00:02:24,570 understanding of basketball statistics. 35 00:02:25,840 --> 00:02:29,969 Ben Morris does explain some advanced concepts to make his case, 36 00:02:29,969 --> 00:02:34,030 like providing a definition of team rebound shares. 37 00:02:34,030 --> 00:02:38,036 But Morris doesn't waste time explaining what a rebound is or 38 00:02:38,036 --> 00:02:42,735 that Wilt Chamberlain and Dennis Rodman were both All Star rebounders, 39 00:02:42,735 --> 00:02:46,218 since his audience should understand that already. 40 00:02:46,218 --> 00:02:51,500 If you find you don't know much about your audience, do some research. 41 00:02:51,500 --> 00:02:53,703 If you're not into basketball, and 42 00:02:53,703 --> 00:02:58,820 someone assigns you to visualize a set of basketball stats, read online articles and 43 00:02:58,820 --> 00:03:03,890 talk to some hardcore fans until you understand the language they speak. 44 00:03:03,890 --> 00:03:08,117 The second thing to do before beginning the process of selecting 45 00:03:08,117 --> 00:03:11,430 a visual is to decide on your story. 46 00:03:11,430 --> 00:03:15,130 You should have a specific audience in mind at this point. 47 00:03:15,130 --> 00:03:18,750 Imagine them encountering your visualization somewhere. 48 00:03:18,750 --> 00:03:20,580 What would you like them to do afterward? 49 00:03:22,250 --> 00:03:26,973 Sometimes the goal of data visualization is to help viewers understand 50 00:03:26,973 --> 00:03:28,001 complex data. 51 00:03:28,001 --> 00:03:33,590 A 2017 Wall Street Journal visualization called the job market tracker presents 52 00:03:33,590 --> 00:03:38,880 the United States employment data from the late 1940s to the mid 2010s. 53 00:03:40,290 --> 00:03:44,877 Provided you have no problem distinguishing color, the chart makes it 54 00:03:44,877 --> 00:03:50,688 pretty obvious that American unemployment was especially low in the late 1960s, and 55 00:03:50,688 --> 00:03:57,044 especially high during the early 1980s, and during the recession of 2008 and 2009. 56 00:03:58,520 --> 00:04:01,990 A nice feature of this chart is its interactivity. 57 00:04:01,990 --> 00:04:07,470 We can compare unemployment rates in America by sex or by race. 58 00:04:07,470 --> 00:04:12,251 Black unemployment numbers in America are distressingly high compared to overall 59 00:04:12,251 --> 00:04:13,900 employment rates. 60 00:04:13,900 --> 00:04:18,420 So this chart tells a clear story of racial inequity provided again that 61 00:04:18,420 --> 00:04:21,920 viewers can distinguish green from red. 62 00:04:21,920 --> 00:04:26,916 This chart could create problems for colorblind users, an audience we'll talk 63 00:04:26,916 --> 00:04:30,890 about in a later video on creating accessible visualizations. 64 00:04:32,070 --> 00:04:35,921 Data Visualization can sometimes evoke an emotional response. 65 00:04:35,921 --> 00:04:40,437 On the interactive timeline called the Poppy Field project visualizes 66 00:04:40,437 --> 00:04:46,450 the number of deaths from human conflict since the start of the 20th century. 67 00:04:46,450 --> 00:04:51,149 Poppies have been used as a symbol of war remembrance since World War I, and 68 00:04:51,149 --> 00:04:55,776 this graph uses the scale of individual poppies to represent the number of 69 00:04:55,776 --> 00:04:58,050 deaths in each war. 70 00:04:58,050 --> 00:05:03,100 We can quickly see that World War II was the deadliest war in modern history. 71 00:05:05,350 --> 00:05:10,740 In addition, the length of the poppy stems symbolize the length of each conflict. 72 00:05:10,740 --> 00:05:14,131 The Israeli occupation of Palestine, for example, 73 00:05:14,131 --> 00:05:17,610 began in the late 1940s and still persists today. 74 00:05:20,070 --> 00:05:24,240 Other times, you'll use your data to inspire action. 75 00:05:24,240 --> 00:05:27,941 At the time Ben Morris wrote the case for Dennis Rodman, 76 00:05:27,941 --> 00:05:31,090 Rodman had been left out of the 2010 vote for 77 00:05:31,090 --> 00:05:36,570 the pro-Basketball Hall of Fame, in his first year of eligibility. 78 00:05:36,570 --> 00:05:42,012 Morris's story was to convince voters of Rodman's underappreciated rebounding 79 00:05:42,012 --> 00:05:47,229 greatness, and it must have helped as Rodman made the Hall of Fame in 2011. 80 00:05:47,229 --> 00:05:51,069 If you're trying to compel users to invest in a company, 81 00:05:51,069 --> 00:05:55,389 vote on a ballot initiative, or take action on climate change, 82 00:05:55,389 --> 00:05:59,550 you'll want to make sure the story you tell is a powerful one.