A Brief History of Data: from 1945 to the Digital Age

Data has been at the heart of human progress. From the first markings carved in prehistory to its recent use in artificial intelligence, data has constantly evolved alongside human societies.

As those societies grew more complex, writing, mathematical notation, and the standardisation of measurements made it possible to store information beyond human memory and pass it down from one generation to the next.

In the modern era, computers and the Internet transformed the use of data, turning it into an essential asset for real-time decision-making and process automation.

In this article, we shall retrace together the remarkable journey of data since 1945, and explore what it reveals about the future.

The rise of electronic systems: from the Second World War to Apollo 11

Our story begins in 1945. The world has just endured the most devastating conflict in human history. The war was fought on every front — land, sea, and air — pushing nations to innovate in order to outpace the enemy. Information was a strategic asset, and the need for faster calculations proved crucial to winning.

One of the major breakthroughs was the creation of more compact electronic systems that could be integrated into military equipment. Computers at the time were enormous and could only perform specific tasks, such as decoding messages. By reducing their size, they replaced mechanical components and became capable of carrying out all manner of tasks, with the added advantage of being smaller and faster.

Once the war was over, research into data technology passed from military hands to those of governments and universities. From that point on, the technology attracted growing interest across many fields: business, science, and engineering. The Dartmouth Conference of 1956, where the term "artificial intelligence" was formally defined for the first time, is a fitting illustration of this emerging enthusiasm.

In the decades following the Second World War, a succession of innovations emerged: standardisation of data formats, creation of the first programming languages for data manipulation. British scientist E. F. Codd developed relational databases to facilitate the organisation and management of large quantities of data for analysis.

It was in this context that the Apollo programme became a landmark moment for data-driven systems. Over the course of its missions, data moved progressively closer to the point of action, providing astronauts with real-time assistance for complex manoeuvres and calculations. The Apollo 11 mission was thus a giant leap for mankind — and for innovation in the field of data.

The rise of personal computing and the early democratisation of data

The years following the Apollo programme marked a pivotal period in the history of personal computing. It was during this time that young engineers built the first personal computers in their garages, making them accessible to the general public for the first time.

Data technology was no longer confined to laboratories. Falling costs and the standardisation of components made the technology more accessible to individuals, who could now manipulate data themselves. Businesses gradually adopted personal computing to enable their employees to manage data, making day-to-day tasks related to administration and accountability considerably easier.

In the same momentum, data storage technologies evolved considerably. The introduction of external storage media, such as floppy disks, allowed users to save, transfer, and share data more easily, further reinforcing the role of personal computing in both professional and personal settings.

A key player in this transformation was Microsoft, founded in 1975 by Bill Gates and Paul Allen. Unlike the innovators who had preceded them — focused primarily on hardware — Microsoft concentrated on software, sensing that operating systems and applications would become central to the way users interacted with data.

With the introduction of graphical interfaces through Microsoft Windows, interacting with data became more intuitive, shifting computing from the mastery of command lines to visual, user-friendly systems. This shift reinforced the role of personal computers as everyday data management tools, mainstreaming the use of word processors, spreadsheets, and databases, and firmly embedding data technology into modern professional and domestic life.

The Internet revolution and the emergence of Big Data and AI

In the 1990s, a technology that is now ubiquitous changed everything: the Internet. Introduced by Tim Berners-Lee, it represented a genuine turning point for countless businesses and individuals. For the first time, data contained in documents could be linked together through hypertext.

Furthermore, individuals could communicate and collaborate on a scale never previously achieved. Data could be shared wirelessly, facilitating the exchange of information within organisations. For users, the Internet paved the way for social networks, fostering personal connections. Facebook alone, launched in 2004, counts 3 billion active users according to its 2024 figures.

Internet technology was also integrated into everyday objects to make people's lives easier: connected alarm clocks, home sensors — all new sources of data.

The rise of the Internet and social networks marked the beginning of the Big Data era, characterised by the unprecedented scale and diversity of digital information. Modern tools enable the large-scale collection, storage, and analysis of this data, giving organisations the ability to detect trends, generate insights, and optimise their decision-making processes.

In the 2010s, machine learning made a notable entrance into the data industry, enabling outcomes to be predicted through algorithms capable of learning from existing data without being explicitly programmed.

More recently, generative AI has expanded these capabilities, moving beyond prediction to create entirely new content — texts, images, music — from patterns learnt within data.

Opportunities and challenges in the age of AI

Over the past decade, data has evolved considerably. Advances are accelerating at an unprecedented pace, and it is becoming increasingly important for users to adopt new technologies. This raises numerous questions about the management and impact of data, as well as ethical concerns.

The widespread use of data has led companies to collect detailed information about users' online behaviour, often at the expense of privacy. Users are increasingly called upon to share personal information, whilst companies must invest heavily in securing their infrastructure to protect sensitive data and comply with constantly evolving regulatory requirements.

Since the AI boom of 2022, concerns around data privacy have intensified, alongside new strategic challenges. Artificial intelligence systems depend on large volumes of quality data to function effectively, obliging organisations to rethink the way data is collected, processed, and governed. Companies must put in place robust data pipelines capable of transforming raw data into reliable, actionable information for AI-driven applications.

Beyond privacy and governance challenges, data storage generates significant financial and environmental costs. Storing and processing data requires substantial amounts of energy, particularly in large-scale data centres.

In the United States, data centres consumed approximately 183 TWh of electricity in 2024 — around 4% of total national electricity consumption — according to the Pew Research Centre. This growing energy demand raises important questions of sustainability in an increasingly data-driven economy.

In conclusion, as we stand on the threshold of 2026, the technologies for creating and processing data are highly advanced. The challenge no longer lies solely in collecting data, but increasingly in creating value through analysis, interpretation, and decision support.

The development of agentic AI — systems capable of making decisions and executing actions autonomously — illustrates this shift clearly. This evolution marks a new chapter in the history of data technology.