The term “data” is not unique to our generation. We were raised in a society dominated by technology and are privileged enough to store and remember most, if not every, moment of our lives. Data is part of our daily jargon: we have data plans on our phones, we store our files in databases in our computer, talk about data when backing up claims in an article…

Although the concept may seem contemporary, the term data originated in the 17th Century when it originally meant scriptural givens. A “heap of data” was understood to mean the word of God, something that is undebatable. Over the centuries, data changed in meaning to the result of experimentation. Now, data has started to replace the term “evidence” and has eventually come to replace it.

To understand data, we need context. If not, it is just information with no rules, boundaries, or meaning. The contexts which define the interpretation can make the same data set mean two different, and even opposing, things. For example, in the 16th century, Tycho Brahe recorded and documented extensive astronomical data. Tycho used it to support his own cosmological system, the Tychonic system, in which the Earth remained stationary in the center while the sun and moon circled around it, and the other planets circled the sun. However, after his death, his student Johannes Kepler used the same data to support the Copernican system, and eventually his own elliptical orbits. It turns out that the Tychonic and Copernican systems were mathematically equivalent, and Tycho’s same data could be used to contextualize the data for either interpretation.

We do not collect data for nothing. Data collection has a purpose, whether it is as the proof of a theorem or the result of an experiment, it is done with purpose. This is why the context cannot be ignored, because if we take something that was done for the sake of something else and consider it without its purpose, it becomes meaningless. Data has no truth. Information as an abstraction with no content.

However, we are shifting into a mode of operation in which nothing needs to be explained anymore. Information is turning into a standalone unit and we no longer need to describe or explain them. This is the moment where data visualization starts being more and more important, and where Professor Hanlon’s talk is particularly relevant. Data has always been visual and is starting to become the main form of evidence. The context matters less and less, and a single interpretation of data is assumed. How do we implicitly agree on one interpretation? Where does the consensus come from? These are a few of the critical questions we must ask ourselves before we commit to an interpretation with no way back.