There are many discussions on the impact of Big Data on decision making, what seems kind of misleading to me. Big Data will never help you to make better decisions, analytics – that are its outcome – yes. We’re talking too much of the engine, which is not that news – since computing exists we’ve always been doing as big as possible with data, what’s new is how big big is today – while the real matter is the outcome.
That said, I see the reproach made to this massive of data : by trying to quantify and rationalize anything more and more, we’re losing track of what’s peculiar to the human nature. It’s irrational nature, its emotionalism. As a matter of fact , emotions drive many things : decisions, buying, the impact of an experience, how we’ll value it and what consequences we’ll draw from it.
Big Data is the engine, analytics the outcome
A couple of weeks ago I was attending the IBM Solutions Connect Conference in Paris. After a couple of hours spent in the social business room to hear things on the collaboration side of digital transformation, I had the curiosity to have a look at the Big Data track. I’m not going to tell you what the speaker, Erick Brethenoux, showed us about how a store could push coupons on my mobile phone when I’m nearby in order to offer me exactly what I’d like to buy at this very moment or how to target drivers in the area to make them feel like buying a chicken to take away. What really drew my attention is something he mentioned at the end of his speech.
He referred to his talk at the recent Ted@IBM event, which video follows.
So, today, we can quantify emotions. Or, to be more specific, discover what leads someone to feel an emotion and understand the impact on this emotion on the person. The purpose is to recreate the conditions of a given emotion for a given person in order to make this person make the decision or take the action we’d like her to.
Are emotional analytics the future of context understanding ?
There are many use cases for this but the most interesting may not be the most obvious. Of course many will immediately think of customers and our to trigger the buying decision…with all the controversies such practices will raise. But it can also help to have a more qualitative approach of customer and brand experience : discover the levers that will make people feel what we’d like them to feel both online and offline. If everybody agrees that experience is at the heart of digital business models, many still struggle with the idea because it’s hard to have a quantifiable and measurable way to deal with it. Such an approach would help to quantify and formalize the definition of an experience, even to equate it. Another use case could be employee engagement in order to understand and recreate the related emotions.
We can also think about less mercenary approaches. In his talk, Erick Brethenoux also shared the example of adults patients that recovered better and faster from surgery when they shared the recovery room with young ones. We can also think about the emotional context of a successful learning experience. On the HR and management side there’s a strong focus on the importance of “soft rewards” today but it’s still empirical. What does someone reacts to, what do they feel when they get such or such reward ?
We must not confuse emotion analysis with sentiment analysis, which is an older and better known field. Emotions trigger sentiments (at least it’s my understanding) so it allows to one step head in the process of triggering an action or a decision. More, sentiments are – in my opinion – related to people while emotions are more about the context.
Emotion analysis is not sentiment analysis
This new field is still to be tackled but it brings an interesting contribution to the understanding of how people react and many things that we used to perceive (or to think we perceived them) but without any understanding of their logics.
There’s another point I liked in this presentation. Erick Brethenoux highlighted the potential of emotion analytics but reminded us that these powerful technologies can be used for the better…or the worse. Technology is not good or bad in itself, what matters is how and why we use it. Hence the need for a data ethics.
Image credit : Emotions via Shutterstock