Neil Harbisson is completely colour-blind. He sees the world in greyscale. But that does not mean that Neil does not experience colour. Since 2004, he has worn an electronic ‘eye’ that turns the colours into sounds by wavelength. He now experiences the world as a series of tones. He dresses to sound right: when he is feeling good, for example, his clothes make a happy C major chord. For a funeral, he suggests D minor: turquoise, purple and orange.
Neil’s way of looking at the world is very different. However, while most of those around us do not experience such extreme differences, it is important to remember that we are all different. We may not be synaesthetic—experiencing words as sounds, or colours as tastes—but we all see a slightly different picture. As Neil and his fellow speaker at SAS Forum Milan, Margherita Granbassi reminded us, it is often through our emotional response that this difference is both most acute and most visible.
The importance of emotion
Data science and analytics often seem logical disciplines. They are focused on decisions based on evidence: rational and unemotional. Using logic supported by clear evidence , it is easy to make decisions. But we forget the emotional aspects of decision-making at our peril.
Digital transformation is changing our way of being and of doing business. But the process of digitization is not easy. It involves changes to longstanding cultures and behaviors. And change and transformation is an emotional process. Logic is not enough to ensure success: successful transformation needs to win hearts as well as minds. Even if minds can be won with a logical evidence-based appeal, we also know that the way to each heart is likely to be different.
This idea is not new. But as the number of stakeholders involves increases, the process of digital transformation becomes ever more difficult. We hear a lot about the shortage of data scientists, but it may be that there is an even more important shortage area: data scientists who are able to craft a compelling narrative and convincing argument in favor of the logical answer.
Working with the data scientists in my organisation, I see three key skills for successful data scientists who create business impact. You may be thinking that these might be coding, analysis, and perhaps something else technical. You would be wrong.
The ones I have seen as truly essential are communication, industry information, and presentation skills. Industry information is perhaps the most obvious: data scientists need to keep up to date with what is going on in their field. In such a fast-moving area, they need to know about new techniques and tools, and be able to use them when necessary. But communication and presentation skills? Let me elaborate.
It is no good having the most logical argument in the world if you cannot win anyone over. Data scientists need to be able to communicate their work to non-data experts, speaking the same language, and also understanding the business. They must be able to understand problems, which includes understanding emotions, and also explain solutions. Emotional intelligence is the ability to get inside someone else’s skin, and understand what drives them. You can then use your own and their emotional responses to make the right things happen.
Communication with the data science team
Emotional intelligence and communication skills are not just important when dealing with customers, or the ‘business side’. They are equally vital within the data science team. to bring more than one perspective to bear. Asking questions alone is a lonely business, and also risks being a fruitless one, if the questions asked are wrong.
But team-working is not simply a case of bringing people together in a work space. It is a matter of learning to work together productively, engaging with each other’s needs and wants. It is, in fact, an issue of emotional awareness.
We are all, as humans, both emotional and logical. We like logic; it is a good basis for decisions. But decisions also have to ‘feel’ right, and that means something different to all of us. Learning to work with others, including in data science and analytics, means understanding this, and being able to use that knowledge effectively. I may not see the world as a series of sounds, but it is important to remember that others may.