What is Datafication in Data Science?

Datafication is an information technology-driven sense-making process. Business Intelligence (BI) is becoming of growing importance as it has broadened to encompass the applications, tools and best practices required for data science. The increasing availability of big data most typically characterised by 3 Vs – Volume, Velocity and Variety. Big Data Analytics is tying up the loose ends about our routines and customs. A growing number of companies are working around the new paradigm to better understand their users´ behaviour to offer a much more personalized experience. Datafication relies to a significant degree in machine learning.

Datafication is turning many aspects of our life into data. Datafication is concept standing on dematerialisation, liquefication and density. Dematerialisation is the ability to separate the information from the physical world. Once dematerialised, a piece of information can be manipulated allowing resources to be unbundled and rebundled – this is Liquification. Density is the outcome of the value creation process by a combination of resources. Since the year 2013, datafication is associated with the analysis of our lives captured through data and predictive analytics. Sense-making is the processes of using technologies to identify and regularise memories and habits into plausible explanations. There are some interesting challenges outside of typically defined in-and-around BI. Sense-making can be approached in three stages:

1. Conceptualisation and codification
2. Algorithmic treatment
3. Re-representation of the world

Data obtained from mobile devices and social media can identify specific characteristics of a person. This can replace different personality tests which traditionally measures the analytical power.

In the same way, data can be used to understand a person’s risk profile for approval of a loan. Different industries are using datafication knowingly or unknowingly to understand customers and plan offerings based on personality and behaviour. In the case of a smart city, the data obtained from numerous sensors may optimize transportation, waste management, logistics, and also energy.