数据。讲故事。同理心。新型冠状病毒肺炎来自Data + Science的Jeff Shaffer

本集以来自Untold Content和data +Science的数据讲故事培训为动力。雷竞技raybet提现雷竞技电竞竞猜通过学习数据可视化和技术讲故事的最佳实践，将数据转换为强大的可视化故事。无论你是PowerBI还是Tableau的员工——或者只是想更好地交流你的数据——这个研讨会都会激发你去发现数据背后的故事。学习更多在UntoldContent.com/Data-StoryTelling-雷竞技raybet提现Trings。

凯蒂[00:00:04]欢迎来到创新的未解故事，在那里我们扩大了洞察力，影响和创新的解开故事。由未结块内容提供支持。雷竞技电竞竞猜我是你的主人，Katie Trauth Taylor。我们的客人今天是Jeff Shaffer。他是COO和V.P.联菲尼德和他是dataplusscience.com的创始人。杰夫也是辛辛那提大学的兼职教授，一位Tableau Zen Master，以及一本关于数据可视化的巨型书籍的同志，称为仪表板的大书：使用现实世界的业务场景可视化您的数据。杰夫今天感谢你们在播客上。

杰夫[00:00:45]谢谢你让我。

凯蒂[00:00:47]我很荣幸能和你们一起展示。我们一起开了一个关于讲数据的研讨会。因此，我很高兴能与大家分享我们在今天共同创建这个研讨会的过程中所发现的一些见解。

杰夫[00:01:01]是啊，非常有趣。我很高兴能谈论这个话题。

凯蒂[00:01:03]但请先告诉我们一些数据讲故事是什么以及仪表板如何在这方面发挥重要作用。

杰夫(00:01:13)是的。你知道，数据讲故事是一个广义的术语，而人们——它将成为数据可视化社区的一个流行词，可能部分是因为一个作家Cole Kanflic, Cole Nussbaumer Knaflic，有一本很棒的书叫《用数据讲故事》这本书很可能开启了这个术语。她的网站甚至是data.com。所以我认为这可能是在过去的五年里发生的，你知道，这是一件发展中的事情，很多人都在谈论故事是如何在数据本身中发挥作用的。这是一个有争议的术语,我想一点,因为,你知道,故事你思考,你知道的,一个技术定义的一个故事,你知道,它有字符或情节或故事情节,通常数据或仪表板特别不这样做。但也有人在更广泛的意义上使用这个词。所以这就有点，你知道，如果你愿意的话，这就好比打开了一罐蠕虫。但至少我可以告诉你我的看法。从更广泛的角度来看，我认为仪表板特别有助于我们找到故事可能在数据中的位置。不一定要讲故事本身。 You might be monitoring conditions of something. You might be looking at a process or something in your organization and you might use that dashboard on a daily basis. And that might give you an indication that something’s gone wrong. Where to go look. You know, you see smoke, but is it a fire? And so that’s where I kind of see, you know, data storytelling comes into play in a number of areas. But as it relates to dashboards, dashboards may be one of the tools to help you find the stories that you bring out in your data.

凯蒂00:03:10肯定。和大多数组织使用仪表板分析和可视化数据点,是巨大的和努力真的可以看到数据的方式帮助他们采取某种行动,或者像你提到的,知道防止火灾或检查的问题。我们合作的一个有趣的地方是，我认为，我们结合数据本身的概念的方式，我认为有这样一个假设，数据是纯粹的，或者数字永远不会讲故事。但是，随着你对数据的理解、对数据的处理以及我们对数据采取的行动的深入，很明显，讲故事对于数据可视化是不可或缺的。这是我们找到逻辑或模式并从数据中采取行动的方法的一部分。

杰夫[00:04:17]我想是这样的。我想补充一点，你知道，我们在一起的方式，你和我，在我们的工作，在那个特定的车间，我认为，我们发现了一些正确的东西。在我的教学中，我经常说数据是原始形式的，比如数据库，续集服务器，Excel电子表格或者制表符分隔的文件等等。就其本身而言，它没有任何作用。它刚好坐对了位置。然后你想从这些数据中获取信息。所以你看它，你聚合它，你过滤它，你潜入它，找到关于它的那些东西。最终，这些信息将引导你从这些数据中获得知识。我在知识部分讲了数据到信息，信息到知识的连续统一体。你真的需要我所说的SMB，主题专家，来真正帮助你从数据中的信息中获得知识。 And in the workshop, you know, in our particular case, we were working with a major hospital. And, you know, I’m not a healthcare expert by any means. I’m not a doctor. I have done some consulting in that field, but I don’t know a lot about that data specifically. Right. And so you really have to work together as a group to kind of figure out what that is. I can build a dashboard for somebody that would give them an indication of maybe something gone wrong, but then what? Right. What’s the next step? And so I think that’s the intersection for me is where data storytelling, in a variety of forms. In our particular client, you know, that we were talking to, they had one group that might’ve wanted to hand something out as a sort of a pamphlet. And another instance it might be something they want to add to their website. And then in another group, they were really looking for something that internally they could monitor sort of as a dashboard. And when we think about data storytelling, that’s really different in each one of those categories. Right. It might be a different end product. It might be a different way, a method, of telling the story completely.

凯蒂(00:06:32)是的。因此，数据讲故事的一个关键因素我认为，这个术语与数据收集或数据报告有一点不同，它是你真正批判性地思考接收信息的观众以及他们如何从这些信息中获取知识。在我们的数据讲故事研讨会上我们一起做了很多杰夫，你做得很好涵盖了数据可视化的最佳实践，你可以做的不同类型的数据可视化。然后我们在这些信息的基础上进一步讨论人物和受众以及如何将数据点和数据需求映射到你试图通过这些信息接触到的不同受众。我们讨论了为数据建立上下文的最佳实践。这意味着围绕你的数据可视化或数据的词语。它还意味着你展示的方式或者你选择分享的数据点。我们做这个练习或者重新制作这个巨大的白板练习图我们把不同的人物角色和不同的观众映射到组织试图用他们的数据到达不同的数据点。我们优先考虑这些数据点。我们会思考哪些视觉隐喻是有用的，尤其是那些最佳实践和数据可视化。 And then we think about the target medium and sometimes that’s a dashboard like you mentioned, and sometimes that’s an infographic or a handout. So it’s really this beautiful way of being sure that when we’re thinking about data, we’re not ignoring the fact that we are humans who need to act as a result of seeing data.

杰夫[00:08:26]我觉得这很棒，它是你的工作与我所做的工作之间的另一个人，既是教授或咨询和研讨会。我总是告诉我的学生，你知道，当你一般开始数据可视化时，你需要问的第一个问题是谁是你的观众，是什么消息。你的观点就在那里。你知道，谁是观众？我最近想到了，你知道，有一个第三个元素，这些元素与受众有关。他们如何消费它？所以你知道，谁是你的观众？消息是什么？也许他们将如何消费该消息？

凯蒂[00:08:59]是的。什么是媒介？

杰夫[00:09:01]但我认为这正是你的观点。这就是为什么我如此喜欢你的角色映射，因为它真的涉及到当你谈论谁是观众时，我们用我们的医疗保健例子，我们谈论的是医生吗?我们说的是员工吗?我们说的是病人吗?我们说的是外部人员还是内部人员?所有这些东西都在播放。你知道，当你谈论这些的时候，有些真的很有趣。你想传达什么信息?你知道，在我们目前的环境下，在新冠疫情爆发的情况下，这有点有趣。我们说的是医疗保健。 But, you know, from a doctor standpoint, they might want to look at things on an aggregate basis and look at statistics. But at the end of the day, we’re talking about people, right? We’re talking about patients. And being able to see that. And a patient certainly wants to know a different piece of information, as, you know, what about me? So I think just nailing down that audience, the persona. Who are those people? What information do they need? When do they need it? How are they going to get it? That really drives everything else, whether it. Are we talking about an interactive visualization that’s going to live on a website? Or are we talking about an infographic that you’re gonna hand out in a pamphlet? Or is this a PDF that needs to be emailed out to the staff? You know, every day or every week. And so those are very, very important questions.

凯蒂[00:10:25]让我们共享我们最喜欢的一些数据故事或数据可视化。你先去，因为我觉得你是一个知识库，谈到真正有效和有趣的方式，让数据带来生活。

杰夫[00:10:41]哦,哇。我有很多最爱的。在数据可视化的世界里，要挑选出不同的类型是很困难的。我喜欢的一些设计师，你知道，从数据即信息图和数据即设计的角度来看，比如Georgia Lupi。她经营一家叫"准确"的公司很长时间了。她跳槽去了一家平面设计公司。但我喜欢她的工作和她的成就。还有一位来自平面设计界的平面设计师，尼古拉斯·费尔顿，他创作了10年的费尔顿报告。这些都是几年前的了。他不再那样做了。 A number of years ago. But I just—I love their work and I still use that as inspiration in the work that I do. I follow a lot of people in the Tableau community, you know, being one of the Tableau Zen masters and tableau being my tool of choice. I follow a lot of people in the Tableau community. So there’s countless people in the Tableau community, many of them, you know, good friends of mine. So I look to that for examples as well. And you know, having written the big book of dashboards, I always gravitate to great examples that people have out there, real-world dashboards. A good friend of mine, Chris Love, has a website called Everyday Dashboards. And I find that one fascinating because it’s people who have taken dashboards that they use at work every day and either anonymized it or turned it into a way that they could share it. But you get to see, you know, not work necessarily. That’s polished by The New York Times for the front page of the newspaper. It’s everyday stuff that people use to get the job done. And so I often gravitate to those kind of things as well.

凯蒂[00:12:39]你能跟我们分享一下你目前正在做的或者最近正在做的项目吗?

杰夫[00:12:45]我今年参与的最有趣的项目是与旧金山的一个叫Splash的组织合作。这是一个非营利组织，帮助把水，干净的水，带到世界上不同的国家，特别是现在在加尔各答，在印度和埃塞俄比亚的大型项目。所以通过Tableau基金会，我和另一位Tableau禅宗大师，Kristie Martini，他和我为他们建造了一个阶段，在几周前投入使用。我们做了一个仪表盘，上面有一些地图，它是交互式的。他们想要一个卫星功能，能够以卫星的方式看地图。然后他们有一些其他的关键输出，他们想要跟踪，所以我们建立了一个输出仪表板。所以这很有趣。我听说，在冠状病毒爆发之前，他们就在旅行，但他们经常出差，为不同国家的资助者、捐助者和项目经理。听到仪表板项目是如何发展的，我很兴奋。这很令人兴奋。 That’s probably my most recent project that I have had going on.

凯蒂[00:14:05]完美。我会分享一个链接，如果你能公开分享的话。我会写在节目笔记里的。

杰夫00:14:10绝对。

凯蒂[00:14:11]再举几个例子，杰夫，我得感谢你和我分享这些。但是阿尔贝托·卢卡斯的洛佩兹在《国家地理》工作，他们报道儿童营养不良。我还会把这个链接到讲义上，这样你们就能看到了。这很难做到。很难用播客的形式来谈论数据故事。但在《国家地理》的这篇文章中，你必须亲自剪下一个嵌入页面的尺子，然后你可以把尺子圈起来，形成一个圈，你可以看到营养不良社区的儿童的上臂中长。所以，这是一个非常有力的例子，它试图帮助引起共鸣，并真正能够从战术上感受到，在儿童面临营养不良的地区，他们的上臂会议是多么的小。这是一个非常有力的例子。你跟我说了，杰夫。

杰夫[00:15:19]是的，我认为，这是去年，只是让它成为个人的一个伟大榜样。这就是我所做的一些教学和研讨会中的另一件事是，如果你想在可视化上参加参与，你知道，让它成为个人。所以你想到我最喜欢的一个例子是我的同志，史蒂夫·魏克勒。他对美国人民的年龄进行了可视化。如果你刚刚展示了美国年龄的分发的可视化，你可能会看看并说，好吧，你知道，所以是什么？也许在数据中可能会看到这种有趣的事情。但他所做的事情，而是使用其他一些技术，这些技巧是新闻组织中的其他人擅长这样做。但是你让它成为个人，并说输入你的年龄。当你把你的年龄放在那里时，你知道，这种可视化重新计算，向您展示比你年长的人口。基于作为40或50的男性，或者如果你是女性，那么年轻人。 And so you kind of get a sense for where you are. And so I think that particular visit you’re talking about really makes it personal because it takes something that you don’t really see. You don’t really have a way of grasping it visually or mentally. How bad is it? And yet you tear this thing off and put it on your wrist and it’s you. And then all of a sudden, it’s wow, you know, it’s just kind of hits you. And so I think that’s sort of the ultimate in sort of making it personal, hitting the message home, right?

凯蒂[00:16:56]是的，绝对。一项研究，你和我认为都很着迷，由纽约工程和法律进行。[2.5s]并在一起，他们试图了解是否使数据视觉本身看起来和感觉更可关联或更加个性化或个性化，是否会对观众的同情产生影响。因此，例如，而不是在数据视觉中只有一个点来表示一个人，他们可能会有一个人的图标，就像一个看起来更像一个人或甚至更具体的视觉。他们可能会向该人提供一个名字，他们尝试了所有这些不同类型的实验，以显示更通用或更个性化的。以及他们对观众的同情水平产生影响，因为他们看待该数据。这项研究的真正有趣的发现是，使数据视觉本身似乎没有对同理心产生影响。但故事或文本周围的数据视觉所做的。因此，如果文本对被列入数据的数据视觉中的人员的个人故事更详细地进行了更多细节，那就引起了更多的同理心。[25.2S]他们承认这是一个小型研究设计，并且它可能需要在更广泛的背景下完成。 But what are your thoughts on that finding?

杰夫[00:18:24]这让我很着迷。这可能也和你们的鞋笔记有关。

凯蒂[00:18:28]好的，我会链接到它的。

杰夫[00:18:31]我对此并不感到惊讶。你知道，思考你知道的是有效的，你知道，你看到了很少，你看到的小人物经常代表条形图，或者你知道，某种形式的同型。而且我一直想知道，你知道，这是一个如点。而且我认为研究类型的导线可能是正确的。所以我认为这是令人着迷的。有很多其他研究。You know, things about where you put things on a page, you know, and so your headline, whether you’re creating a dashboard or a visualization or just a PowerPoint slide, your top left corner of your of your biz is where everybody is going to look, you know, at first. Right. And so what you’re talking about, that text, having a—thinking about what the title is, having a descriptive subtitle, having good annotation layers and having them in the right place, organized on the page can make all the difference in the world to something like that. In our dashboard workshops, we talk about, you know, your key performance indicators or bands, as we often call them. You know, these big numbers, you put them across the top of your visualization because that’s where people are going to look. And it’s sort of the headline, right? It’s the headline of the story. And then you kind of get down into the details underneath it. Now, one thing I will say is, you know, this study didn’t compare this, but I think there’s something to be said for when we’re aggregating data versus disaggregating that data. I think that if we ran that study and said, OK, here’s bar charts showing the average lifespan of somebody who has the Coronavirus, you know, or the death rate or something, that’s going to be a lot less personal than if I had dots, you know, for every one of those people. And so I think that’s maybe something to be said is maybe being careful about, you know, aggregating up, losing that personal touch of it, that, you know, there may be something to showing one hundred dots on a page from my hundred patients. And this is you and this is where everybody else is versus just saying, oh, here’s the average where the one hundred patients are and here’s where you are. So I think, you know, it kind of goes both ways. But I think there’s some interesting things about that study and hopefully they’ll be future studies in that area.

凯蒂[00:20:53]我们确实深入研究了一些围绕数据讲故事的更复杂的挑战，包括从引发共鸣到在如何从道德上思考如何可视化数据等方方面面。让我们倒退一点和谈论的一些基础知识,因为我喜欢和你一起工作在这个车间和其他项目是你真的精制的创建模式,你会看到不同的类型的数据可视化以及他们对观众的影响。我想有一件事可能会让一些听众感到惊讶，那就是我们应该尽量避免使用饼状图。

杰夫[00:21:37]你知道，那个特定的图表类型，饼图和甜甜圈图表，在数据可视化社区中获得了很多坏的压力。人们经常与图表相关联。你知道，我知道的很多同事，你知道，我们只会说不要用它们。而且你知道建议可能是好的建议。我认为它比永远的差别更细致 - 无论你知道，无论是数据viz图表是什么，它都会回到数据viz中的基本构建块，我们称之为前提属性。只有人类真的很擅长一些事情，并不擅长其他事情。所以，我认为，我认为，这个问题导致的是真正的人类擅长，我们可以利用人类擅长快速准确地参加信息的东西。在大多数数据的数据中都在那里。基础研究真的衡量了这两件事。这就是所有研究的真正基于的东西。 More recently, we’ve studied other things like memory of a viz or things like that. But, you know, really at the heart of it, are we getting the information quickly and accurately? And so as an example or the example you used. We are generally better. We are better as humans. And this has been studied with looking at things like position. We’re very, very good at the position of objects in space. We’re very, very good at looking at the length or width of something. But we really fail miserably when it comes to estimating the size of something or the angle of something or the arc of something or even color, you know, trying to figure out how much more blue is that. You know, is it twice as much blue or is it three times as much blue? That’s gonna be a very, very difficult task, you know, to do. And so it’s really a learning, I guess, the basics of data visualization more than just the chart types, but just sort of the fundamental, you know, way our brain interprets this information and does it quickly and then leveraging those things to get the right things on a page. You know, I think one of the things that you probably picked up on from a lot of those slides is simplicity, really. I mean, even if I take a, you know, the evil pie chart, I can make a pie chart, you know, useable by just reducing its complexity. So instead of having 18 slices, maybe I only have two or, you know, just show one number. Eighty five percent or something like that. And so really, no matter what chart type you pick, if I say bar charts or stack bar charts or line charts, if I add 50 colors to it and add a thousand labels, it’s gonna become incomprehensible and we’re gonna overload the reader and they’re not gonna get the message no matter what chart type I use. Right. So it’s kind of a combination of these things that you kind of learn and put together.

凯蒂[00:24:38]现在请告诉我们如何将数据可视化并将其移动到仪表板上，以便我们查看多个数据集或数据点。您能告诉我们一些最佳实践吗?当我们迁移并使用数据构建一个更大的故事时，我们应该牢记在心。

杰夫(00:24:56)是的。男孩，这是一个伟大的问题，因为——我认为这是许多组织每天面临的挑战。所以，我要从最开始，也就是你的数据开始。你的数据有多好?所以，你知道，你得坐下来好好想想。所以你需要弄清楚在你的组织中你想要衡量的关键是什么?关键绩效指标是什么?我们知道他们吗?我们想追踪什么?一旦你有了这些，你就必须从数据开始，因为你甚至可能没有数据来跟踪你需要跟踪的东西。正确的。 And so it starts with the data having, you know, some semblance of data, governance, data gathering, knowing where it is, what’s the source of it, how good is it? Can we trust it? Right. There’s the sort of the veracity of the data, if you will. And then, you know, once you have that, then you can kind of put those things together. You know, I find many organizations—well, I’ll use the healthcare example again that you and I collaborated on. They had data coming in from, you know, a dozen different sources. And so, you know that that adds to the complexity of it. Where does it come from? How good is it? Can we trust it? How often is it updated? There’s—data is always messy, you know, especially if there’s free-form responses in the data and things like that. So that that’s really the starting point for me. We have to figure out what we’re trying to measure, what we’re trying to improve, what we’re trying to monitor. And then, you know, we go to the data and see if we can put that together. Then the next step is, you know, sort of the design of that thinking about, OK, well, we want to measure what? Do we want to measure our actuals versus a target? Do we want to see something over time? Do we want to see the location of people? And that’s going to drive what visualizations we choose, whether we’re using a bar chart or the target line or whether we’re plotting people on a map. That’s going to be the tool that we use to answer the questions that we asked in the first part. And then putting it all together on the dashboard, you know, as nuanced. And, you know, we wrote a book about it. You know, that part is almost the easy part after you—if you’ve done the first two parts correctly, getting it together in the final step is almost the easy part. Right. You know, putting it together in a way—in a simplistic sort of simple as can be with as much detail as necessary in a way that people can see it and use it.

凯蒂[00:27:26]在数据讲故事的未来时，我们应该预期什么？我正在考虑专门了解人工智能，大数据和，你知道，越来越多的技术能力，将数据解释和分析到某种程度上？

杰夫[00:27:44]我觉得很棒。我认为这很可怕。你知道，两者合二为一。你知道，最重要的是工具变得越来越好。他们找到了方法，我将和我最熟悉的Tableau对话。你知道，他们每季度发布一次，他们添加的功能非常快。令人惊讶的是，一个季度又一个季度，他们都在添加这些功能。他们去年关注的其中一个是他们所谓的询问数据，你有一个人工智能引擎，它会在幕后计算出你问的问题，当你说上个月的销售额是多少。然后你说。俄亥俄州怎么样? You know, it doesn’t start your query over, it says, oh, you want to know how many sales you know in the US. And then when you asked in Ohio, it’s sub queries that goes down to Ohio. I think that’s brilliant. You know, it’s a great tool. Where I think it’s horrifying is we have to be really, really careful. Again, it goes back to our data. Do you understand the data that you brought in? You know. Did you already aggregate the data before it was brought in or maybe it was not aggregated? So when you start asking questions, you better be really careful about what that data was that you brought in, because, you know, you’re going to ask a question, it’s gonna give you an answer and you’re going to—if you treat that as gospel, you could get yourself into a lot of trouble. So I can just think of, you know, instances where, you know, you’ll bring in data of, you know, ten years over time of, say, health care data, you know, child mortality data or something. And you ask a question, well, did it some that up for you or did it average it for you? And did it average it how? And over what period of time? And those are all things that at least today and in the near future, we need to be in control of. Right. We need to understand how it’s doing that and not just letting the A.I. take over the answer for us and trusting it.

凯蒂(00:29:43)是的。这是一个令人难以置信的责任我们投入技术和程度,我们可以确保从伦理的角度来看,这些算法是准确的,我们仍将在确保其准确性和确保人类的解释是正确的。我不知道人类是否会在这一过程中发挥作用。至少我希望我们不会讲到这一点。

杰夫[00:30:18]是的。其中一部分可能只是，我认为，我们在曲线上的地方。正确的。每个人都在谈论人工智能，专门的机器学习。人们在意识到，你知道，要这样做，我们必须是，你知道，我们必须这样做，保持未来。这一切都很棒。这只是我猜这是我们数据的批判性思考不是我们在这一点上放弃的东西。所以我认为在你谈过的时候，你知道，在数据讲故事中，我只是不想依靠，你知道，在不久的将来给我那个故事。我想应用人类的认知元素，你知道，能够解释那些结果，然后最终提出这个故事。也许这可能会从现在开始改变20年。 But I think where we are today, you know, that’s one of the fears I have.

凯蒂[00:31:09]你知道，回到同理心的概念。我认为这就是恐惧如此真实的部分原因。我从事的一项技术写作领域的研究，是一名专业写作研究人员，进入军队，观察他们在空袭决策时分析数据的做法。有时数据更个性化，有时数据不那么个性化。这意味着他们使用特定的暗语或方式，修辞，修辞选择，从数据中去除这些群体的个性。研究发现，当这种个性从数据和他们在空袭决策中使用的语言中进一步去除时，空袭就会更加频繁。就像你说的，他们没有批判性思维。我也会链接到那个研究，因为我认为它是一个有力的例子，让我们记住为什么我们应该时刻记住我们从数据中告诉自己的故事。如果语言的选择,我们正在分析数据正在距离自己从这一信息的影响,我认为我们需要特别注意的,也许我们应该使某些操作或加速某些操作或删除同理心的时刻。

杰夫[00:32:50]我们现在生活在一起，这是冠心病遍布世界各地的数据和来自各地的数据的方式绝对是真实的。John Hopkins有一个集中式数据库，其中一群人在Tableau敲入并为每个人提供的金额，我昨天思考。或者我认为这是昨天。有一个reddit亚替德文名为的信息很漂亮。这是第一天，这是该帖子的大部分可视化。我们基于冠状病毒。张贴了52％或者一些被宣布的可视化与此有关。这就是我想的事情，你知道，你在那里击中了，我们只需要真的小心。你知道，我可以展示说话的数量，你知道，电晕病毒和说，哦，好吧，死亡率只是。而且，您知道，占百分比，2％，1％，低于百分比，无论如何。 But it’s much more detailed. Right. If you dive in and kind of filter that down and see, oh, if you’re over a certain age, you know, the death rate is 15 percent. And so it’s easy to, you know, throw this off and throw out data and just say, OK, well, it’s not so bad. It’s just like the flu or it’s not spreading as quick. And you’re inferring data or you’re aggregating data in a way where you lose that empathy that you’re talking about. You think about, OK, well, if I’m 75 or 80 years old, I may not feel that way. And so, you know, for somebody my age to say, oh, I’m not really worried about it because it’s, you know, 0.5 percent chance of dying, that kind of disconnects us from sort of the rest of humanity there, doesn’t it? And so visualizing that in ways I’ve seen a lot of discussion in the last week where people have just, myself included, just have taken the route of, you know what? We’re just not going to visualize that data because we just don’t know enough about it at this point to be confident in what we’re producing. And I think that goes to the opposite of empathy. Right. We could actually do harm in some situations.

凯蒂(00:35:00)是的。这是令人难以置信的，特别是随着越来越多的数据公开，越来越多的工具，如Power Behind Tableau。我们必须开始质疑这些可视化的合法性并确保我们正在分析。这消息来源可靠吗?因为就像你提到的，那些公开可用的可视化数据，现在有50%是关于冠状病毒的。那么，在理解或评估可视化是否可信方面你有什么方法，有什么策略可以推荐给公众吗?

杰夫(00:35:39)嗯,是的。我的意思是，有两件事。第一，如果我是可视化者，我必须问自己这是我自己做过的，我需要把它形象化吗?我告诉你，我下载了数据。我已经连接了数据，连接了Tableau小组之前的数据。我甚至连上了他们的数据。我做了一些可视化。但我决定不发表这些。我只是想了解我自己，看看数据，看看外面的vizes是怎么回事。当我阅读它们时，我只需要带着一点健康的怀疑态度看待它们。 Right. They’re visualizing this information. I’m not saying that they’re all bad by any means. It’s just you have to understand the context in which the data was gathered, for example, to compare it to the flu. Well the flu’s been around for a very, very long time. And we have a long, long history of data on that. This this this Coronavirus is brand new. So to make an A-B comparison there makes it really, really difficult because we don’t know yet. Right. To make any kind of comparison to what’s going on in China. You know, that’s different conditions, different health conditions, different amount of people and a different amount of space in different environments under different government control. We can’t take the data that we have there and superimpose it on the United States and say it’s going to travel as faster or slower or even the same. So just things that we just have to kind of be careful of is I guess this applies to any data, really, but especially in this data is using it in a way that we’re making assumptions of the data. Right.

凯蒂[00:37:14]绝对。这是正确的。是的。即使在NPR和我的驱动器到录音室，他们正在谈论医学界是否试图得到这种精致的平衡。你知道，除了公众，我们真的需要罢工，这在知道病毒在任何特定时刻的地方以及如何迅速传播和平衡，这与进入测试的事实使得更多的公众面临风险。因此，试图有点我想到这一点，那么如果你有症状，建议就是一种，试着孤立自己。如果你 - 如果这些症状加速或变得更糟，那就去看医疗提供者。所以也许不要跳转到立即进行测试，因为这会让其他人面临风险。正确的。暴露。 So it’s such a strange time that we’re living in right now. And I’m really grateful that we’ve been able to think together about how data storytelling and data visualization is part of that conversation.

杰夫[00:38:17]是的。特别是与您经常谈论的同理心。我认为这就是正确的。

凯蒂[00:38:24]杰夫，非常感谢你在播客。我喜欢和你合作。我喜欢你拥有这个令人难以置信的网站。如果您 - 我推荐每个人都查看dataplusscience.com。这是一个令人难以置信的资源，杰夫打破了不同的可视化策略。我们一起考虑讲故事。我们希望将来有更多内容，在该主题上将在一起。但非常感谢你，杰夫，因为在这里。

杰夫[00:38:49]谢谢。感谢您的款待。

凯蒂[00:38:52]杰夫，如果人们想在社交媒体上找到你，去哪里找你?

杰夫[00:38:56]我在推特上很活跃我的推特账号有很高的viz v-i-z能力，而且我在附近。容易找到。在LinkedIn或Facebook上联系。我有一个数据+科学的页面，连接到data + science.com。社交媒体上到处都是。

凯蒂[00:39:20]精彩。非常感谢，杰夫。下次再聊。

杰夫(00:39:23)谢谢。

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