A tale of Thales, Olives, and the 42 Vs of Big Data

The ancient Greek philosopher Thales was born in Miletus in Greek Ionia. Thales investigated almost all areas of knowledge, philosophy, history, science, mathematics, engineering, geography, and politics.

Thales distinguished himself by thinking differently about the four Greek elements. His approach to the understanding of heavenly phenomena represented the beginning of Greek astronomy. Thales' hypotheses were new and bold, and in freeing phenomena from godly intervention, he paved the way towards scientific endeavor.

It is from Thales, in 600 BC, that we can trace the use of Big Data. He was the first to use large amounts of data to make a predictive use and to make a profit out of them.

"Somehow, through observation of the heavenly bodies, Thales concluded that there would be a bumper crop of olives. He raised the money to put a deposit on the olive presses of Miletus and Chios, so that when the harvest was ready, he was able to let them out at a rate which brought him considerable profit." (Plut.?Vit. Sol.?II.4)

Thales was the first to elaborate a sort of database, in which all the data about the climatic conditions and many other events, apparently disconnected among them, were collected over the years. In fact, it is good to remember that, in addition to the amount of data and the speed with which these are collected, one of the elements that define Big Data is the variety of data collected and then combined to offer completely unexpected solutions to problems.

About 20 years ago, Doug Laney of the META Group (now Gartner) wrote an amazing report entitled “3D Data Management: Controlling Data Volume, Velocity, and Variety.” The 3 Vs of Big Data were born on that day—February 6, 2001.

Ten years later is when Big Data impose themselves to the attention of the public. The Vs inflation continued its inexorable march, and we had the 4 Vs of Big Data, then the 7 Vs, and then the 10 Vs: 1) Volume - 2) Velocity - 3) Variety - 4) Variability - 5) Veracity - 6) Validity - 7) Vulnerability - 8) Volatility - 9) Visualization -10) Value. The 10 Vs of Big Data | Transforming Data with Intelligence (tdwi.org)

?Meanwhile, together with the Vs, we had the Bs (for Bigs), specifically the 10 Bigs: 1) big volume - 2)?big velocity - 3)?big variety - 4)?big veracity - 5) big intelligence - 6) big analytics - 7) big infrastructure - 8) big service - 9) big value 10) big market. https://www.researchgate.net/publication/322592851_10_Bigs_Big_Data_and_Its_Ten_Big_Characteristics

?And then there were the 17 Vs…https://www.irjet.net/archives/V4/i9/IRJET-V4I957.pdf.

We are now in ?an ever more sophisticated era of analytics. Here is the updated list: The 42 V's of Big Data and Data Science | Elder Research. As Tom Shafer acutely argues in his blog: “We can safely say we are now?well on the way?to 100 Vs of Big Data and Data Science!”

I wonder what Thales would think… ??