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题目自己按照理解翻译的，整篇英文文章还是不错的，大家感兴趣可以看看，当前“大数据”的概念也是有点过火，从外行来看，更需要冷静看待。
原文：It's a Big Data Jungle Out There
"Every now and then, we find ourselves in new and unexpected places."
As these words were spoken by Analise Polsky, a thought leader on the SAS Best Practices team delivering the closing session at this week's Big Data Conference in Chicago, I looked around the room. Pretty much all the audience members seemed to be nodding to themselves absolutely in agreement. That's because it is surely true, especially in the context of the new world of big data.
Polsky herself readily admitted that working for an analytics software company and being immersed in big data was not a destination she imagined for herself as an undergraduate studying anthropology. But neither did she expect to find herself living deep in the Amazon jungle. Oddly enough, she found her inner data geek amid those towering trees -- and what she wouldn't have given to have the advantages of today's visual data tools out there in the field.
In fact, that trip into the Amazon serves as the perfect metaphor for the big data journey. "I remember looking back at the city and watching the lights fade away and seeing this thickening dark row of trees getting taller and taller until there was only darkness and thinking, 'How am I going to survive this?'"
Certainly, that unease is relatable to anybody grappling with crazy amounts and new varieties of data approaching at rapid speed. Polsky said she shares her story of the Amazon because it reflects the way many people feel about big data -- that overwhelming sense of being in the dark and unable to see the trees. "We need to think of big data in a way that makes sense to people who aren't just the data junkies."
Data visualization, which popped up as an important consideration time and again among conference speakers and participants, is essential, she said. "How do we make data consumable? I truly believe that data visualization is going to be even more important going forward, and it's going to be how you're going to tell your organization's data story."
With 70 percent of all sensory receptors in the eyes, data visualization is really the only way people are going to be able to see the trees. It not only will help simplify complex ideas and clarify trends and outliers, but it also will help justify business decisions. "Once we can see, we can understand."
And if your organization is building a data-driven, analytically oriented culture, data visualization provides a great way to foster collaboration. It's a mode of communication, and it provides a way to speak a common language around your data. But there's a caveat : "You have to be speaking the same language."
Say the Southeast region comprises nine states according to sales but 10 in marketing's eyes. A data visualization showing the 11 states that finance considers the Southeast region will be ineffective for the sales and marketing teams. The data has to be consistent -- no different than ever but more vividly captured in a data visualization. "You have to establish accountability around the data. Data visualizations are the output, but people need to know they can trust the data and rely on it."
Just as Polsky had to prepare for her trip into the Amazon -- learning Portuguese, for example -- so must today's analytics professionals as they move into the unchartered territory of big data. Question everything , she advised:

• Do you know what the problem is? "Data visualizations should confirm a hypothesis or answer a question. We don't want to spend a lot of time searching for something with no tie-in back to business challenge."
• Can you act on it? "Exploration is great, analysis is great, but what am I supposed to do about it?"
• Can your data help address the problem? You should recognize that data sometimes won't be the answer. Talent, for example, isn't a data problem; it's an HR problem.
• Who's using the data, and how will they benefit from the visuals? "The benefit has to be driven by a purpose. We're not just talking about reports. We need to monitor information. I work with a lot of data stewards. They need to monitor; they need to see when rules are violated. Visuals help."
• Do they need special skills? Sometimes your visuals will require a graphic designer. "Design does matter. Color matters. The layout matters. It changes our perception."
  

A graphic designer could keep you from making visual mistakes, like using an ineffective
3D pie chart to deliver critical business information.

Picking the right tools is important. "If you want to start talking about developing an analytics culture, you don't necessarily need to have skills today like those needed yesterday," Polsky said. "You can develop skills using intuitive tools -- and you'll have people thinking, 'Hmm, maybe I can do more with analytics.' I know that happened with me."
But data visualization shouldn't boil down to a features and function conversation. It should be about evolution. "So ask the right questions. And don't be afraid to ask additional questions you couldn't before, and let visualizations be one of the ways you get people to the table."
Do you find yourself not being able to see the trees through the big data jungle at your organization? Share your experiences below.

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关键词：大数据 Organization Collaboratio Conversation Participants unexpected audience 英文文章 members around

这是一个大数据的丛林
现在，我们发现自己在新的和意想不到的地方
由Analise Polsky ，在SAS最佳实践团队提供在本周的大数据在芝加哥会议闭幕会上的思想领袖说过这些话，我看着周围的房间。几乎所有的观众似乎点头自己绝对一致的。这是因为它肯定是真实的，尤其是在大数据的背景下，新的世界。
Polsky本人爽快地承认，分析软件公司工作，沉浸在大数据不是一个目标，她想象自己作为本科生学习人类学。但她也没有想到，却发现自己生活在亚马逊丛林深处。奇怪的是，她发现她内心的怪胎数据中那些参天大树 - 和她不会有今天的可视化数据工具的优势，在该领域有。
事实上，这一趟到亚马逊作为完美的比喻为“大数据”的旅程。 “我记得在城市回首，看灯光消逝，看到这个增厚暗排树木变得越来越高，直到有只有黑暗和思维， ”如何我要生存？ “
当然，这种不安是听上去很像人拼杀疯狂的金额及新品种的速度快的数据接近。 Polsky说，她分享她的故事，因为它反映了很多人都觉得大数据的方式 - 那铺天盖地的感觉，在黑暗中无法看到树木的亚马逊。 “我们需要思考的大数据在某种程度上是有道理的人谁不只是数据瘾君子。 ”
数据可视化，作为一个重要的考虑因素一次又一次的会议发言者和与会者之间的弹出，是必不可少的， “她说。 “我们如何让数据耗材？我真的相信，数据可视化将是更重要的未来，这将是你要告诉您的组织的数据故事。 ”
有70％的所有感受器眼睛，数据可视化是真的只有这样的人才能够看到树木。它不仅将有助于简化复杂的想法和明确的趋势和异常，但它也将有助于证明业务决策。 “一旦我们可以看到，我们可以理解。 ”
如果您的组织是一个数据驱动的，面向分析的文化建设，数据可视化提供了一个伟大的方式，以促进合作。这是一个沟通的模式，有一种通用的语言，在你的数据提供了一种方法。但是有一个警告： “你必须讲同一种语言。”
说东南亚地区包括9个国家销售，但营销的眼睛10 。数据可视化的销售及市场推广队伍， ，显示融资认为东南亚地区的11个州将是无效的。的数据是一致的 - 比以往任何时候都没有什么不同，但更鲜明地捕获数据可视化。 “你必须建立问责围绕数据，数据可视化输出，但人们需要知道他们可以信任的数据，并依靠它。 ”
正如Polsky到亚马逊 - 学习葡萄牙语，她此行的例子 - 所以今天的分析专家必须做好准备，因为他们进入大数据难以估计的领土。质疑一切，她建议：

你知不知道是什么问题？ “数据可视化应确认一个假设或回答问题。我们不想花了很多时间寻找的东西，不打领带的业务挑战。 ”
你可以做就可以了吗？ “勘探是伟大的，分析是伟大的，但我应该做些什么？ ”
您的数据可以帮助解决这个问题呢？你应该认识到，数据有时候会不会是答案。人才，例如，是不是数据问题，它是一个人力资源的问题。
谁在使用数据，以及他们如何从视觉效果中受益？ “的好处有目的将推动我们不只是在谈论报告。我们需要监测信息。我的工作有很多的数据管家，他们需要监控，违反规则时，他们需要看到的视觉效果。帮助“ 。
他们需要特殊的技能吗？有时，您的视觉效果将要求一个平面设计师。 “设计的事。颜色事宜。布局事宜。它改变了我们的看法。 ”

一个平面设计师，可以让你从视觉错误，如使用无效三维饼图提供关键业务信息。


挑选合适的工具是非常重要的。 “如果你想开始谈论发展分析文化，你不一定需要有一技之长，像今天所需要的昨天， ” Polsky说。 “你可以开发技能，使用直观的工具 - 你将人的思维，”嗯，也许我可以做更多的分析。 “我知道，与我发生了。 “
但是，数据可视化，不应该归结到功能和功能的谈话。它应约进化。 “所以提出正确的问题，不要害怕问其他问题之前，你不能让可视化的方式让别人去表。 ”
你觉得自己不能够看到通过大数据在您的组织中的丛林树木？下面分享您的经验。

赞。。。。个人觉得国内现在大数据热还仅局限在如何做个报表，如何分析。真正的大数据思维，数据挖掘还是没多少人懂