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 wasseen 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.