Datafication explained with Netflix example

The term “datafication” has gained popularity over the past several years and is frequently used in the Big Data sector. Although the term “datafication” is one we frequently hear these days, you probably won’t find much relevant information about it if you search it on the internet. However, after examining the subject at hand, I could assert that a lot of us are familiar with the term’s meaning but may have given it a different name.

Datafication: What is it?

MayerSchoenberger and Cukier define datafication as the conversion of social action into online quantifiable data that enables real-time tracking and predictive analysis. Simply said, it involves converting previously unseen processes or activities into data that can be recorded, monitored, studied, and optimised. The most recent technology we employ have made it possible for many new ways to “datify” our routine actions.

In conclusion, datafication is a technology movement that transforms a variety of aspects of our life into computerised data utilising procedures to change businesses into data-driven ones by transforming this information into new types of value. 

When something is “dataficated,” it means that the routine interactions of living things can be converted into a data format and applied to society.

Examples:

There could be a lot more datification instances here. 

Let’s take social media platforms like Facebook or Instagram as an example. These platforms gather and monitor data about our friendships to market goods and services to us and provide surveillance services to organisations, which in turn alters our behaviour. We also see daily promotions on these platforms as a result of the monitored data. By using datafication to inform content rather than recommendation algorithms, this paradigm uses data to reinvent how content is created.

However, there are additional sectors where the datafication process is employed actively:

Insurance: Data used to update business models and risk profiles.

Banking: Information that determines a borrower’s creditworthiness and possibility of loan repayment.

Human resources: Information used to determine, for instance, employee risk-taking profiles.

Data is used instead of personality tests in hiring and recruiting.

Social science research: Datafication restructures social science research processes and replaces sampling methods.

One excellent example of the datafication process is Netflix, a provider of online streaming video. It has 33 million streaming subscribers and offers services in more than 40 countries. With DVD and Blu-ray disc rentals via mail order as its main business at first, activities were more tangible in character. The subscriber created and maintained the queue (an ordered list) of media items they wanted to rent, such as a movie, in simple terms. The content can be kept for as long as the subscriber wants if the total number of discs is limited. To rent a new disc, the subscriber must return the old one to Netflix, who will then add the next available disc to the subscriber’s queue after receiving the old one back.

Therefore, the disc rental model’s commercial objective is to assist customers in filling their turn. The business model has changed, and Netflix is now aggressively adopting datafication procedures to make their service into a smart one.