21st Century Gold

“Data is the new oil. It’s valuable, but if unrefined it cannot really be used. It has to be changed into gas, plastic, chemicals, etc. to create a valuable entity that drives profitable activity; so must data be broken down, analyzed for it to have value.” — Clive Humby, 2006

Clive Humby, a British mathematician and data science entrepreneur, originally coined the phrase “data is the new oil.” Since then, the idea has been echoed and expanded upon by many thought leaders across industries.

“Information is the oil of the 21st century, and analytics is the combustion engine.” — Peter Sondergaard, 2011

Data can be described as raw information about a variable or entity. It can be anything that can be captured, stored, and processed — from small datasets to massive volumes stored across cloud platforms such as Azure and other distributed systems.

Data is valuable across industries including business, education, information technology, and data science. It forms the foundation of analytics and artificial intelligence, serving as the raw material upon which insights and predictions are built.

While there may be undiscovered oil reserves in the world, oil is ultimately a finite resource. In contrast, data is practically infinite.

In 2019 alone, the United States consumed an average of 20.54 million barrels of petroleum per day. Meanwhile, as early as 2018, it was estimated that 2.5 quintillion bytes of data were being generated globally every single day.

With the number of internet users growing exponentially, it is safe to say that data will never truly run out. In fact, we will continue generating more data indefinitely.

Another key distinction between oil and data lies in consumption. Oil, once used as fuel, is consumed and destroyed. Data, on the other hand, is not destroyed when used for analysis.

In the information age, everyday human actions continuously generate data. Consider the following examples:

• Creating a Facebook profile generates personal and behavioral data.
• Accepting a friend request contributes to social graph data.
• Watching a movie on Netflix feeds recommendation algorithms.
• Purchasing a product on Amazon informs personalized recommendations.
• Searching on Google creates searchable intent data.

Data is an asset that does not lose value after use. Technology companies can collect and analyze customer behavior data over years to build increasingly sophisticated models.

Imagine how refined Amazon’s recommendation engine could become after another decade of learning from online shopping behavior. By continuously updating algorithms with fresh data, organizations can transform data into a long-term value-generating asset.

The effectiveness of analytics and AI solutions depends heavily on the quality of the data used. High-quality data produces reliable insights, while low-quality data leads to poor or misleading results.

If raw data contains missing or inaccurate information, it must be refined before being used for analytics. Unlike oil, where more fuel does not necessarily improve engine performance, more data has the potential to create more accurate and robust predictive models.

Systems that allow organizations to continuously collect, store, and refine data enable businesses to turn information into a strategic asset that compounds in value over time.

In the 21st century, data is not just the new oil — it is the fuel that powers intelligent decision-making, innovation, and sustainable growth.