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” and since then several others have repeated this phrase. In 2011, the senior vice-president of Gartner, Peter Sondergaard, took this concept even further.
“Information is the oil of the 21st century, and analytics is the combustion engine.” — Peter Sondergaard, 2011
Data is referred to as raw information about some variable or noun, it can be anything that can be captured and stored, anything that can be huge in size stored on Azure or any cloud server . It can be useful for business industries, education institutions, IT and Data Sectors alike. Data is everywhere, and data is the root of Data Science-it is the basic requirement on which all the analyses are made.
While there may be many undiscovered oil reserves in the world, there is a finite amount of oil left on our planet. At some point, we will run out of oil and be forced to transition to other forms of energy. In 2019, the U.S. on average alone consumed 20.54 million barrels of petroleum per day. However, sources from as early as 2018 claim that 2.5 quintillion bytes of data are produced each day globally.
With the number of internet users growing exponentially, we can safely say that data is practically infinite. We will never really run out of data. In fact, we will keep creating more and more indefinitely. This concept leads to the next point.
When oil is used as fuel it is consumed once and permanently destroyed. Data, on the other hand, is created and does not have to be destroyed even after we use it for analytics. In the information age, everyday human actions generate data every day. Here are a few examples:
When someone creates a Facebook profile, they are creating data.
When someone accepts a friend request on Facebook they have created data that Facebook can use for friend suggestions.
When you watch a movie on Netflix, you are creating data for the movie recommendation algorithm.
When you buy something on Amazon, you are creating data for Amazon’s recommendation system.
When you search for something on Google, you are creating data in the form of your search history.
What this means is that data is an asset that doesn’t have to go away and can remain useful for a long time. Technology companies can keep collecting data about customer behavior for years in order to build more robust models that can provide a better experience for customers. Just imagine how much more sophisticated Amazon’s product recommendation system will be after learning patterns in another ten years of online shopping. By updating and improving algorithms with the arrival of new data, companies can turn data into an asset that keeps adding value.
The quality of any practical analytics or AI solution is dependent on the data used to build it. High-quality data leads to high-quality analytics. Low-quality data leads to low-quality analytics. If your raw data contains missing or inaccurate information, you may have to refine it until it reaches the level of quality that you need for analytics.
While more oil will not necessarily make a combustion engine perform better, more data has the potential to produce more robust predictive models. Having a system that allows you to collect and store more and more data for training and refining models allows you to turn data into an asset that keeps adding value to your business.