Post 23: The History of Data — It’s Older Than You Think

Data science feels like a modern buzzword — but humans have been collecting, analysing and learning from data for centuries.

We talk about data as though it is a modern invention. Something that arrived with smartphones, social media, and Silicon Valley. But the truth is, data has been around for centuries. The tools have changed beyond recognition, the scale is almost incomprehensible compared to what came before — but the fundamental human instinct to collect, organise, and learn from information? That is ancient.

Let us take a walk through history.

Before the Digital Age — Data Has Always Existed

Long before computers, spreadsheets, or databases, people were gathering and analysing information to make better decisions. Governments counted populations. Merchants tracked trade. Astronomers recorded the movement of stars. The methods were manual, slow, and imperfect — but the intent was exactly the same as it is today: understand the world through numbers and patterns.

Today, organisations increasingly rely on information gathering and insights to drive business growth, make strategic decisions, and reach wider audiences. But this instinct did not start in a Silicon Valley boardroom. It started much, much earlier.

1660s — John Graunt and the Birth of Statistical Thinking

One of the earliest and most remarkable examples of data analysis belongs to John Graunt, a 17th century English merchant who had no formal scientific training — yet changed the way the world thought about numbers.

In the 1660s, Graunt began analysing the Bills of Mortality — weekly records of births and deaths published in London. What he did with that raw data was extraordinary for his time. He constructed the world’s first mortality table, using birth and death rates to estimate the number of men of military age, women of childbearing age, the total number of families, and even the approximate population of London — all from publicly available records.

He was also the first to document the phenomenon of excess deaths during epidemics, noticing patterns that others had simply not thought to look for. In doing so, he laid the groundwork for statistical association, statistical inference, and population sampling — concepts that sit at the heart of modern data science. He had curiosity, a pile of records, and the instinct to ask questions of the numbers in front of him. Sound familiar?

1880s — Herman Hollerith and the Era of Data Processing

Fast forward to the 1880s. The United States government faced a growing problem. The national census — conducted every ten years — was taking so long to process manually that by the time the results were ready, they were already out of date. The 1880 census took nearly a decade to tabulate.

Enter Herman Hollerith, a German-American statistician who found his inspiration in an unlikely place. Watching a train conductor punch tickets for passengers one day, Hollerith had an idea — what if data could be stored and read the same way, using holes punched into cards?

Building on a concept originally developed by silk weaver Joseph Jacquard in the early 1800s, Hollerith designed a tabulation machine that used punch cards to store and process data. Each card was a piece of stiff paper with holes punched in specific locations. The cards were passed between brass rods, allowing the data to be read electronically.

The result was transformative. The 1890 census — using Hollerith’s machine — was processed within the same year it was collected. A task that had previously taken a decade now took months. Hollerith later founded a company that would eventually become IBM.

This was the birth of mechanical data processing — the first time machines were used to do what humans had previously done by hand.

1928 — Fritz Pfleumer and Magnetic Storage

The next leap came from a German engineer named Fritz Pfleumer, who in 1928 patented a method of storing information on magnetic tape. His approach was elegantly simple — he coated very thin paper strips with iron oxide powder and sealed them with lacquer. The result was a medium that could store recorded information magnetically rather than mechanically.

What makes Pfleumer’s invention so significant is its legacy. The idea of storing information on magnetic surfaces directly inspired the development of floppy disks and hard disk drives decades later. Every time you saved a file on an old computer, or stored data on a server, you were benefiting from a chain of innovation that traces back to that strip of iron-coated paper in 1928.

1960s — Edgar Codd and the Relational Database

By the 1960s, computers existed — but the way data was stored and accessed was clunky, inflexible, and difficult to work with. Data was often stored in hierarchical or network structures that made it hard to query or reorganise without rebuilding the entire system.

That changed when computer scientist Edgar Codd introduced the concept of the relational database management system. His idea was elegant and powerful — organise data into tables, with attributes described in columns and their values stored in rows. Tables could be linked to each other through shared fields, allowing complex relationships between different sets of data to be expressed cleanly and queried efficiently.

This is the model that underlies virtually every database system used in the world today — from the small business running MySQL to the global enterprise running Oracle. When we talked about databases in Post 19, we were talking about the world Edgar Codd built. His model has remained the dominant paradigm for data storage for over half a century.

1990s — The Internet and the Data Explosion

Everything changed again with the internet. Sir Tim Berners-Lee’s invention of hypertext and hyperlinks in the early 1990s made it possible to share information and connect resources across the globe in a way that had never existed before. Data was no longer locked in filing cabinets or private databases — it could flow freely across networks, borders, and devices.

Then in 1997, Google arrived. And data became available to virtually everyone with access to a computer or mobile device. Search engines didn’t just make data accessible — they made it searchable, organised, and useful at a scale that would have been unimaginable to John Graunt sitting with his Bills of Mortality three centuries earlier.

With the rise of the internet came the rise of something we now call big data — volumes of information so large and so fast-moving that traditional data processing tools simply couldn’t keep up. We will come back to big data in more detail in an upcoming post.

Where We Are Now

Data has changed the way we look at the world and continues to reshape it. Industries from finance and healthcare to astronomy and retail now rely on analytical techniques to drive decisions, improve operations, and create new opportunities.

And the journey is far from over. With every innovation in technology — in data science, machine learning, and artificial intelligence — new ways of creating, storing, and learning from data continue to emerge.

What started with a merchant counting birth records in 17th century London has become the defining resource of the 21st century economy. The tools are unrecognisable. The instinct is exactly the same.