Lakhasly

For centuries, people have struggled to understand the meaning that's hidden in large amounts of data.Over 2000 years ago, tax collectors for Emperor Qin Shihuang used the abacus--a device with beads on wires--to break down tax receipts and arrange them into categories.It's something else to classify what species of trees they are, how they cluster at different altitudes, and what could be built with the wood they provide.From this, they could determine how much the Emperor should spend on building extensions to the Great Wall of China.To sort out unstructured data, humans have created many different calculating machines.After all, it's one thing to estimate how many trees grow in a million square miles of forest.That information can be difficult to extract from a very large amount of data.Because it's hard to see without help, scientists call this dark data.An abacus.

For centuries, people have struggled to understand the meaning that’s hidden in large amounts of data. After all, it’s one thing to estimate how many trees grow in a million square miles of forest. It’s something else to classify what species of trees they are, how they cluster at different altitudes, and what could be built with the wood they provide. That information can be difficult to extract from a very large amount of data. Because it's hard to see without help, scientists call this dark data. It’s information without a structure: just a huge, unsorted mess of facts.
To sort out unstructured data, humans have created many different calculating machines. Over 2000 years ago, tax collectors for Emperor Qin Shihuang used the abacus—a device with beads on wires—to break down tax receipts and arrange them into categories. From this, they could determine how much the Emperor should spend on building extensions to the Great Wall of China.
An abacus.
Charles Babbage's difference engine.
In England during the mid-1800s, Charles Babbage and Ada Lovelace designed (but never finished) what they called a “difference engine” designed to handle complex calculations using logarithms and trigonometry. Had they built it, the difference engine might have helped the English Navy build tables of ocean tides and depth soundings that could guide English sailors through rough waters.
By the early 1900s, companies like IBM were using machines to tabulate and analyze the census numbers for entire national populations. They didn’t just count people. They found patterns and structure within the data—useful meaning beyond mere numbers. These machines uncovered ways that different groups within the population moved and settled, earned a living, or experienced health problems—information that helped governments better understand and serve them.