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如今我们可以轻松地从云端雾端，或者某些静态设备（static device）收集数据，但是这些数据无法量化客户体验时的情感。大型企业利用无处不在的信息感知移动设备（information-sensing mobile devices）、软件日志（software logs）、摄像头、麦克风、射频识别（RFID）读取器以及其他的科技手段来尽可能地对所有数据加以利用。然而，我们有如此之多的设备来记录各类数据，却在情感数据的搜集上存在一个黑洞。企业们需要着手制定计划来填补这一虚空。
当大数据分析者和专家们在讨论用户体验和参与度的衡量标准时，92%的人甚至都不会用到“情感”这一词语。而情感的价值在于，它能提供必要的情景，通常还能帮助我们对用户的行为、回忆以及目的进行更好的预测。
在“情感罗盘”缺失的情况下，对大数据的客观分析能够非常精确地定义一次互动或经历。但只有在情感数据中，我们才能找到“某人是否会记住这次经历？”这类问题的答案。情感数据能促使人们将事实和感知，以及那次经历的可能蕴意融合在一起。情感数据的黑洞正是通过这种方式来影响对回忆的预测性建模（predictive modeling）。
我们的研究显示，医生们记住特定的病人，并不是因为他使用了某种疗法或者获得了何种成功，而是因为病人的表现或治疗结果让他有所感觉和触动。如何判断医生是否有记住某位病人？最有效的方法或许就是看那名医生对病人的记忆中掺杂了多少情感。
大卫·盖勒特（David Gelernter）是耶鲁大学一位备受尊重的计算机科学教授。他在他的书中精心构造了以下原理。
“当一位医学专家记住一名病人，他并不只是记住一串文字。他记住的是一次经验，其中包含着繁若星辰且互相关联的感知。当然他也会记住某些词语——一个名字、一次诊断，又或者别的东西。但他还记住了病人的长相、声音、他们相见时的感觉（自信，迷惑？）……显然，这些未被记录下来的感知包含着庞大的信息量。人们可以回忆过往的经历，体会其中储存的感知。但是（在进行大数据分析时），我们只是将‘记忆’简化为单纯的文字，将其迅速归类、评估、归类、评估，结果遗漏了大量的信息。一幅‘会当凌绝顶，一览众山小’的壮丽景象，就这样变成了一张模糊的黑白小照片。
此外，模拟的回忆过程——即把案例从数据库中调出——和真实的回忆过程之间是有很大差别的。对人类而言，一段经历意味着一系列连贯的感觉，这些感觉被打包保存在仓库中以便日后回味。而回忆过程是相反的：一系列的连贯感觉被从仓库里取出并再现——即将那些储藏起来了的感觉重新经历一遍。打个比方，人们记在脑海中的经历比磁带记录下的经历更加生动，虽然最初的一些细节可能会变得模糊，甚至完全缺失，但大体上，回忆者仍能体会到最初的经历。换句话说，对人类而言，回忆并不只是对过往经历进行检索，而是重新体验一次最初的经历。”
要填补大数据中的这一黑洞，捕捉情感数据并将其与更加传统的数据合为一体，唯一的方法就是制定获取情感数据的相关计划，细心设计数据收集过程。只有先确立这一目标，才能了解用户的情感与心声，完整地描绘出他们的体验。
The Black Hole in Big Data
With all these data that is easily collected, from the cloud, the fog or some static device, the data is typically void of any metrics that can quantify emotion for the experiences their customers have. Large corporations use all the data they can gather from ubiquitous information-sensing mobile devices, software logs, cameras, microphones, radio-frequency identification (RFID) readers, and other technologies. But with all the different streaming metrics, there is a black hole that surrounds what should be emotion data. This void needs to be filled and companies need to plan on filling that void.
More than 92% of big data practitioners and experts do not even use the word “emotion” when they discuss experience and engagement metrics. If emotion metrics are not top of mind, than no valid use cases for those metrics will ever surface to the teams making decisions. The value of emotion is that it provides the necessary context and often the better predictors of behaviors, recall and intent.
With no “emotional compass”, the objective analysis in big data can be very accurate in defining a transaction or experience. But the question of, Will someone remember the experience?, exists only in the emotion data. The emotion data is what prompts someone to blend the facts with perceptions and possible implications to the experience. That is how the black hole affects predictive modeling for recall.
Our research has shown that physicians remember specific patients because of how the patient presentation or outcome made them feel, not because of a specific condition, treatment or degree of success. Strong correlation was seen between patients most remembered and severity of disease and type of disease, but the correlation was aligned with basic prevalence and incidence for those same conditions. The strongest predictor of a physician remembering a patient was the amount of emotion associated with each patient recollection (If you wish to know more about this study design, contact me).
David Gelernter, a well-respected professor of computer science at Yale University, crafted an excellent rationale in his book Mirror Worlds: or the Day Software Puts the Universe in a Shoebox…How It Will Happen and What It Will Mean.
“When an expert remembers a patient, he doesn’t remember a mere list of words. He remembers an experience, a whole galaxy of related perceptions. No doubt he remembers certain words — perhaps a name, a diagnosis, maybe some others. But he also remembers what the patient looked like, sounded like; how the encounter made him feel (confident, confused?) … Clearly these unrecorded perceptions have tremendous information content. People can revisit their experiences, examine their stored perceptions in retrospect. In reducing a “memory” to mere words, and a quick-march parade step of attribute, value, attribute, value at that, we are giving up a great deal. We are reducing a vast mountaintop panorama to a grainy little black-and-white photograph.
There is, too, a huge distance between simulated remembering — pulling cases out of the database — and the real thing. To a human being, an experience means a set of coherent sensations, which are wrapped up and sent back to the storeroom for later recollection. Remembering is the reverse: A set of coherent sensations is trundled out of storage and replayed — those archived sensations are re-experienced. The experience is less vivid on tape (so to speak) than it was in person, and portions of the original may be smudged or completely missing, but nonetheless — the Rememberer gets, in essence, another dose of the original experience. For human beings, in other words, remembering isn’t merely retrieving, it is re-experiencing.”
The only way to fill the black hole, capture the emotional data and blend it with the more traditional data is to plan on capturing emotion data and design your process meticulously. Intent is the key when the goal is to understand emotional engagement, the voice of the customer or map the overall experience.
译者： sibyl玥 原作者：Stephen Trask, MT，via：译言网
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本文来源：36大数据
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