DATA VALUE
"Data is the new oil," Clive Humby, a Sheffield mathematician declared. The analogy signifies how data can be used to power some of the "transformative" technologies we see today. There has been a number of arguments in literature for and against this analogy. Michael Palmer, of the Association of National Advertisers, expanded on Humby's quote: "Data is just like crude. 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, analysed for it to have value."
“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
In his article on Forbes, Bernard Marr, a technology and data expert, presented some of the reasons why data is not a new oil. Bernard highlighted some of the properties that data have that can not be compared to oil. He points out the re-usability and durability of data, the expense of transporting data around the world, and the usefulness of data.
Jocelyn Goldfein and Ivy Nguyen on TechCrunch also had their views on why Data can not be considered the new oil. Meanwhile, the two dealt much on other elements like algorithms, performance, stability, e.t.c that would build a "defensible data moat" that helps drive AI technologies.
Dr. Michael Mandel, a Chief Economic Strategist at Progressive Policy Institute, has also exciting points on why data should not be referenced as a oil in his paper "The Economic Impact of Data: Why Data Is Not Like Oil" with reference to privacy among others things. He states how regulations for the data-driven sector can have negative impact on the economy overall unlike for commodities like oil that seeks to protect individual rights to ownership of resources.
"Data should not be compared to oil – it is not a scarce commodity, is non-rival, and cannot be monopolized." - Dr. Michael Mandel
There are probably more authors who have written on the same. Now, in consideration to what Michael Palmer had said, expanding on Clive Humby quote, there are some things that are so relevant to understand. We might not entirely disregard the analogy. There are few things that can helps us understand what data is and what it can do.
Micheal is referring to data is crude. This points to the different variety of data that is around us. In Big Data, there are 4 V's that are commonly used. These are Volume, Variety, Velocity and Veracity. Variety refers to different forms that data take. Data can be structured or unstructured. In this state data can not be of any use, just like crude is. Raw data would not give you any insight for any business decisions.
For the data to be of use, processing needs to be done, just like crude has to be refined to produce valuable oils. This is the critical stage in making data sensible. There are so many processes and procedures to be taken to ensure that data talks to us. Data has to be collected from different sources, both structured and unstructured. Data cleaning is also necessary to ensure we are working with the collect and tiddy data, which sometimes might involve amputation for the missing data. Data amputation might also require special methodologies and models. Once data has been collected and cleaned, its now the job for data analysts to make sense of the data. There are basically four types of analyses that can be carried out to helps us get data representation. These includes descriptive, exploratory, inferential and predictive analysis.
"Data & Analytics may help us better understand individual customer needs, increase sales, find better ways to organize processes, predict criminal behavior, reduce traffic congestion or even lead to better focused cancer treatments" - Ueli Amberg & Gerben Schreurs (Clarity on Data and Analytics)
One last thing that I would like to point out is the consequences that data might bring in case our process above has been carried out erroneously. I usually give an example of health data. Assume, in the case of doing data cleaning or merging some data has been lost or mixed up, it might end up giving wrong results to a wrong patient, e.g giving a negative result to a patient who is supposedly positive. This might also happen if we misinterpret the results of our analysis. Hence care must be taken when dealing with data. This also relates to crude. What happens when crude burns?
By just looking at the process raw data goes through, we still can relate data with crude as Micheal has pointed out. But if we consider other properties that data has, then we can not surely compare it with oil.
