Who Said Data is the New Oil?


The term “data is the new oil” was coined by British mathematician and data scientist Clive Humby in 2006. Since then, many people have used this idea or something similar.

But it wasn’t until 2017 that The Economist published an article entitled “The World’s Most Valuable Commodity Is No Longer Oil, But Data” got a lot of media coverage and this phrase became the new theme of the Fourth Industrial Revolution

In a way, this story perfectly demonstrates that data is perhaps the most coveted commodity in the modern world. Like oil a few years ago and perfume before it, heritage governs great collections or consumption.

And they have control over this thing, corporations are gaining influence and power to change results and facts, as those in charge of oil reserves did in the past.

However, this statement often fails to capture the essence of raw data. Therefore, organizations that fail to recognize the weaknesses of this statement may fail to fully harness the power of data to improve and transform their business. In this article, we’ll explore the value of data and why data isn’t like oil.


Quality Data Can be Recycled

Unlike oil, data can be recycled. high quality data can be reused or copied without degradation or loss, and some say it’s “useful” every time it’s used. This becomes apparent when existing data is used to create customized data formats such as ratios or data aggregations. Or when anonymized data is used to improve the predictive capabilities of machine learning.


Why Data is the New Oil

This analogy has proven true because data as a resource now powers entire industries and is of immense value – but virtually worthless if not refined.

But like oil, one of the downsides of big data applications is its environmental impact. This is a problem that every company that creates and uses data should be aware of and take responsibility for.

Two-thirds of the world’s data is currently obsolete. We call this “digital waste”. What we need to understand about data is that it has mass, that it must exist physically somewhere. This means that all data has a carbon footprint.

By 2030, the abundance of data centers will account for 8% of global energy consumption. Even today, data produced and never reused contributes to more carbon emissions than the airline industry. This is a problem that no company can solve, but everyone can play a role.

Businesses need to ask themselves a few simple questions: How much data do we have? Where is he? What do we use it for?

It’s about taking responsibility for your data and its impact on the environment.

So digital junk isn’t just a private sector problem, it’s a problem that affects everyone. Businesses need to understand that storing unused data only consumes energy. This is totally unacceptable.

The adoption of technologies like the cloud gives companies better insight into the data they need and the ability to store and process data more sustainably. Going back to the “data is the new oil” analogy, it’s not about eliminating data, it’s about gaining more miles per gallon.

In June 2020, the Scottish Government published guidance and resources to help public bodies adopt cloud services. This was followed by the release of a report in October 2021 which looked at how the potential of Scottish public service data could be unlocked.

Both show the positive progress the country has made towards becoming more sustainable. But it’s important not to give up. The government should regularly review its policies and ensure that data and cloud remain part of Scotland’s strategy. This will be key to the sustainability of our data.


Thirst for Data Analytics

Our thirst for modern data, like our thirst for oil, is historically imperial and colonial, and closely linked to the exploitation of capitalist networks. These empires first seized the natural reserves of their possessions, then exploited them, and the development of communication systems lives in a modern digital framework.

The Information Highway follows telegraph lines established to govern ancient empires

While the world’s fastest bus from West Africa still passes through London, the Anglo-Dutch multinational Shell continues to drill for oil in the Nigerian river. Undersea pipelines around South America are owned by a Madrid-based company, even as the country struggles to control its oil interests.

Fiber optic networks bring economic connectivity to extraterritorial territories that were quiet during decolonization.

The empire has cut off most of its territory, but only the empire continues to operate and maintain the power of its network.The data-driven government is once again racist, sexist and repressive politics, these prejudices and attitudes are deeply rooted.


Data Processing

When you run a business and want to do something with your high-quality data, the first step is to create the infrastructure needed to store and query that dataset.

Let’s say you have a travel booking site. New data is generated each time someone performs a search, books a trip, clicks on an ad, or otherwise interacts with content on the site.

To collect all this data, you need to hire data engineers, and set up something like a Hadoop cluster that allows flexible data storage and fast querying. This is a big investment that you need to make upfront but business intelligence can be very powerful in the data economy.



Enter Data Science

Data scientists are trained to look at noisy data in terms of:

  • What assumptions can you make about the data generation process?
  • How can you test this hypothesis against your data?
  • What insights can you gain from hypothesis testing?

Before moving on to data, note how the business model begins with a plan – this is because the data is too complex to provide any real valuable insights. The career of a data scientist rarely begins with the data itself.


Is Data More Valuable Than Oil

The data-driven approach is complex, requires careful planning, engineering, and analysis, and has many unknowns and pitfalls. Moreover, it is not always clear what to do with the data because the data itself is too noisy to provide value. Comparing data with oil ignores this messy and complex reality.