A Brief History of Data: Part 2 - Analytics

When did data analysis start? This article will allow you to go back in time and discover the evolution of data analysis. This article is the second in a series of articles. Find the first article on data infrastructures.

Let's start far away: 2000 years before our era.

-2000: Invention of the database

We have been analyzing data since the birth of civilization. The oldest preserved traces of writing are not ethereal things like poems, great speeches, love letters or novels, but rather examples of data analysis. 

Among the Sumerians, the scribes drew up lists of ploughmen employed by the state and preserved these data on clay tablets, thus constituting the first data base. These inscriptions made it possible to calculate salaries and thus the first data analysis. 


John Graunt introduced statistical data analysis with the bubonic plague. This London haberdasher published the first collection of public health records when he recorded mortality rates and their variations during the bubonic plague in England.

20th century

Let's start with this anecdote: in 1926, Nikola Tesla predicted that man would one day have access to large amounts of data thanks to an instrument that could be carried "in the pocket of a vest".

The 60s and 70s

Let's then jump directly to the 60's, where data analysis was structured in a way that is closer to the one we know. The computing capacities of computers started to increase, the first databases started to be computerized as opposed to the previous ones which were purely paper-based. 

Few calculations were made using this data, so one could search through a database, limited by the size of the screen. So when the amount of data started to multiply, maintenance problems started to arise. And you know what? 60 years later, this maintenance problem still exists in many organizations. 

A need for structuring the data was then felt. SQL (Structured Query Language) appeared in the 1970s. It was at this time that the first jobs of data analysts appeared.

In 1965, the United States plans to build the first data center buildings to store millions of tax returns and fingerprints on magnetic tape. This was the beginning of the digitization of government. 

The 80s and 90s

It was at this time that business intelligence appeared. How to see data at different levels of aggregation? Rather than seeing the data at a city level, we want to be able to see it at a city level, but also at a regional level, and then at a country level. This is the appearance of "drill up and down". From that point on, requirements began to grow. Can we see a temporal evolution? For people named Jacques? 

It was then necessary to denormalize the data to bring flexibility in their understandings. The company's executives can therefore be in possession of sophisticated reports to help them make decisions in the right direction. The democratization of the internet in the 90's started to multiply the sources of data, the tools were no longer sufficient. 

According to R.J.T. Morris and B.J. Truskowski in "The Evolution of Storage Systems."(2003), it was not until 1996 that it became cheaper to store data on digital media rather than paper.

The 21st century

The explosion of data has caused the number of analyses around it to explode. It had to be stored and analyzed. This is the appearance of Big Data. Tools such as Hadoop have appeared to facilitate the analysis of high volume data. We will come back to this subject in part 3 of this history of data. 

In the 2000s, the Cloud appeared, allowing to centralize through an organization, the storage of data. 

In 2010, data science surfaced, allowing for in-depth improvements in analysis and prediction. We will come back to this topic in part 4 of this story of data...

And the future of data analysis? Edge analytics is part of it for so-called sensitive data. How to decentralize data analysis directly on the devices? Another subject of extension is the IoT (Internet of Things) which will allow data collection to move to a new stage. This will keep data analysts and other data professionals busy for many years to come!

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