The quality of the output depends on the quality of the incoming data 

To understand the relationship between income data and a company’s output, it is necessary to know the history of industrial processes. In other words, to understand how methods and strategies have been perfected or evolved according to the reality in which they were inserted. 

For example, do you know where the metal used to build the Eiffel Tower comes from? Or the origin of copper in the Statue of Liberty? You might not know this information, but you can surely find out the source of the eggs you ate this morning. 

This shows how contemporary industry has evolved related to data collection and analysis. The history of technology is the history of productivity, and productivity is a reflection of control. Control over processes, inventory, input, output, and work.  

The history of the output

In the 18th century, the American Manufacturing System appeared, which succeeded in reorganizing factories and production lines, as well as expanding the capacity for data collection and management. The idea was to use data to improve production processes.  

At that time there was a spirit of improvement, in which Taylorism proposed a reductionist and “scientific” approach. It divided tasks into subtasks (a division of labor), which allowed for more detailed studies, such as the time a task took and the efficiency of the methods used. Thus, it was possible to acquire more quantity and quality data, culminating in the Fordist organization reorganizing the workflow of the American industries of the time. 

At the same time, statistics began to be applied to the assembly lines, reducing inspection costs and improving control. 


Investments in data technology are increasingly becoming a requirement for any company, no longer a differential. In 2022, defensive strategies were the objective of 35,7% of the investments in technology while in 2019, this number reached only 8.3.%.  

A greater data revolution was coming with automation methodologies initiated throughout the 1970s and 1980s, as well as Agile Manufacturing and Six Sigma. Once again, there was an obvious dependence on collecting, analyzing, and handling data in the supply chain, in order to develop and increase productivity.


An example of this innovation in technology is related to mastering “Big Data” with new computational methods, such as A.I. and Machine Learning. These new technologies allow leaders to obtain real-time data from the production chain, for example: 

If a ship is taking raw material to a factory and the company has its data integrated in a data warehousing, it will be able to handle information, such as: 

-The real-time position of the shipment; 

-The speed of the vehicle, aircraft, or vessel; 

-The route it is taking; 

-Alternative routes to make it deliver faster; 

-The time the ship will arrive; 

-The conditions of the cargo. 

This information will also be easily displayed through dashboards and reports. Therefore, today, thanks to the latest technological innovations, companies are able to continuously collect, summarize, evaluate and process a quantity of data every second. This provides leaders with the ability to maximize productivity, improve service quality and meet the needs of their demand.