Dictionary.com describes datum (the singular of data) as “a single piece of information; any fact assumed to be a matter of direct observation.” So when we think about big data, we are thinking about massive amounts of individual pieces of information or individual facts from direct observation. Data simply are what they are, facts and individual observations in isolation.
On the other hand Dictionary.com defines information as “knowledge communicated or received concerning a particular fact or circumstance.” Information is the knowledge, story, and ideas we have about the data. These two definitions are important for thinking about big data. We never talk about big information, but the reality is that big data is less important than the knowledge we generate from the data, and that isn’t as objective as the individual datum.
In The Book of Why Judea Pearl writes, “a generation ago, a marine biologist might have spent months doing a census of his or her favorite species. Now the same biologist has immediate access online to millions of data points on fish, eggs, stomach contents, or anything else he or she wants. Instead of just doing a census, the biologist can tell a story.” Science has become contentious and polarizing recently, and part of the reason has to do with the stories that we are generating based on the big data we are collecting. We can see new patterns, new associations, new correlations, and new trends in data from across the globe. As we have collected this information, our impact on the planet, our understanding of reality, and how we think about ourselves in the universe has changed. Science is not simply facts, that is to say it is not just data. Science is information, it is knowledge and stories that have continued to challenge the narratives we have held onto as a species for thousands of years.
Judea Pearl thinks it is important to recognize the story aspect of big data. He thinks it is crucial that we understand the difference between data and information, because without doing so we turn to the data blindly and can generate an inaccurate story based on what we see. He writes,
“In certain circles there is an almost religious faith that we can find the answers to … questions in the data itself, if only we are sufficiently clever at data mining. However, readers of this book will know that this hype is likely to be misguided. The questions I have just asked are all causal, and causal questions can never be answered from data alone.”
Big data presents us with huge numbers of observations and facts, but those facts alone don’t represent causal structures or deeper interactions within reality. We have to generate information from the data and combine that new knowledge with existing knowledge and causal hypothesis to truly learn something new from big data. If we don’t then we will simply be identifying meaningless correlations without truly understanding what they mean or imply.