What Is Datafication ? FutureUniverseTV Anthony Aries Presents Datafication In Data Science Practical Knowledge.
An information technology-driven process of making sense of data is called datafication.
As business intelligence (BI) has expanded to encompass a wide range of applications, tools, and best practices, it has become more and more important. In general, big data can be characterized by three Vs: Volume, Velocity, and Variety. Our routines and customs are being tied up through Big Data Analytics. Many companies are working around the new paradigm to better understand their users’ behavior so that they can provide a more personalized experience for them. Machine learning plays a significant role in dataification.
As a result of datafication
Many aspects of our lives are transformed into data which is subsequently converted into information realized as a new form of value. The term datafication was introduced to the broader lexicon in 2013 by Kenneth Cukier and Viktor Mayer-Schönberger. Up to this point, datafication had been associated with the analysis of representations of our lives captured through data, but not on this scale. A primary reason for this change is the impact of big data and the computational opportunities offered by predictive analytics.
Datafication has resulted in the transformation of many aspects of our lives into data.
In order to achieve datafication, three components are necessary: dematerialization, liquefication, and density. Information is dematerialized when it is separated from its physical form. As a result of dematerialization, information can be manipulated, allowing resources to be unbundled and rebundled – this is known as liquification. Value is created through the combination of resources. Datafication has been associated with the analysis and prediction of our lives based on data since 2013. By using technology, memories and habits are identified and regularized during the process of sense-making.
In the workplace, what does a data scientist look like?
This depends on the level of seniority and whether we are discussing the Internet/online industry specifically. Although data scientists need not be restricted to the tech industry, it is where the term originated; therefore, let us discuss what it means in that context for the purposes of this discussion. The chief data scientist should be responsible for setting up the company’s data strategy, which involves a wide range of tasks, ranging from designing the infrastructure and engineering for collecting data and logging, to addressing privacy concerns, to deciding what data will be user-facing, how data will be used to make decisions, and how data will be built back into the product. This individual should be responsible for managing a team of engineers, scientists, and analysts as well as communicating with senior management across the organization, including the CEO, CTO, and product managers.
He or She will also be responsible for patenting innovative solutions and setting research objectives.
An individual who is a data scientist has the ability to extract meaning from and interpret data, which may require tools and methods from statistics and machine learning, as well as the ability to be human. Because data is never clean, she spends a great deal of time collecting, cleaning, and munging data. As part of this process, persistence, statistics, and software engineering skills are required, as well as skills necessary for understanding biases in data, and for debugging logging output from code. The next step is exploratory data analysis, which combines visualization and knowledge of data.
In order to understand how the product is being used and its overall health, it will build patterns, models, and algorithms, and others will serve as prototypes that ultimately will be implemented into the product itself.
As a part of data-driven decision making, it may design experiments. It is her responsibility to communicate with team members, engineers, and leadership in clear language and with data visualizations so that they understand the implications of the data, even if they are not immersed in the data themselves. The purpose of this book is to help you understand the vast majority of what is presented at the high level.