The Renaissance of Big Data

Throughout time we have consistently attempted to improve the way we communicate. Some of the first forms of communication developed before 3500 BC were through imagery—paintings on walls describing what emotions were being experienced and physical events occurring at that time. We have evolved since then—Egyptian hieroglyphics, the development of paper which led to the ability to send messages to people by horse, the telegraph, Morse code, telephone, radio, television, computers, and finally, the Internet.

So the great new marketing movement regarding big data should be taking us to a place where current technology is a thing of the past, right? Well, in short, what we are actually doing is going back to the future—taking what has become a complicated mess of information and using imagery to simplify it.

The fact that we create as much data every two days as we had from the beginning of time until 2003 has left marketers asking the question: How do we make sense of it all? The answer is to revert back to what was the simplest form of communication: paintings on walls that describe emotions and events so that we can learn from our past.

We call the individuals who can do this data analysts. They will communicate all of this data through imagery. They will tell stories that we can learn from. They will help mold our future to avoid the mistakes of our past. The definition of a data analyst is a business analytics specialist who creates graphs, charts, infographs, and other visual tools that help people better understand data.

A data artist uses data streams and advanced analytics systems in the same way a regular artist uses oil and brushes, stone and chisels, or wood and carving knives. Their purpose is data reduction, which is data used for summary charts, and data revelation, which is identifying something that has never been seen before.

Data can no longer just be about the numbers; it must tell a compelling story that carries intuitive value. This value must be tangible and have a bottom line philosophy, answering the question: How does data help improve our organization?