What is Datafication in Data Science?

What is Datafication?

In this article we will be considering “What is Datafication in Data Science?”

According to Wikipedia, Datafication is simply transforming everything in our life into devices or software powered by data. Datafication is the modification of human chores and tasks into data-driven technology. Example of the concept Datafication can be found in smartphones, industrial machines, and office applications to AI-powered appliances.

According to MayerSchoenberger and Cukier, Datafication is the transformation of social action into online quantified data, thus allowing for real-time tracking and predictive analysis.

According to Techopedia, Datafication refers to the collective tools, technologies and processes used to transform an organization to a data-driven enterprise.

What Is Data Science?

Data science is the process by which a data scientist develops strategies for analyzing data, preparing data for analysis, exploring, analyzing, and visualizing data, building models with data using programming languages, such as Python and R, and deploying models into applications.

Data Science is a blend of various tools, algorithms, and machine learning principles with the goal to discover hidden patterns from the raw data.

Who Is Data Scientist?

Someone who specializes in the process of collecting, organizing and analyzing data so that the information therein can be conveyed as a clear story with actionable takeaways. As a general rule, data scientists are skilled in detecting patterns hidden within large volumes of data, and they often use advanced algorithms and implement machine learning models to help businesses and organizations make accurate assessments and predictions.

Duties Of A Data Scientist

• Identifying relevant data sources for business needs
• Collecting structured and unstructured data
• Sourcing missing data
• Organizing data in to usable formats
• Building predictive models
• Building machine learning algorithms
• Enhancing the data collection process
• Processing, cleansing & verifying of data
• Analyzing data for trends and patterns and to find answers to specific questions
• Setting up data infrastructure
• Develop, implement and maintain databases
• Assess quality of data and remove or clean data
• Generating information and insights from data sets and identifying trends and patterns
• Preparing reports for executive and project teams
• Create visualizations of data

Career Opportunities In Data Science

• Data Analyst
• Statistician
• Business Intelligence (BI) Develop
• Data Scientist
• Infrastructure Architect
• Data Architect
• Enterprise Architect
• Application Architect
• Machine learning Scientist
• Machine Learning Engineer
• Data Scientist
• Business IT Analyst
• Marketing Analyst
• Clinical Data Managers

Applications Of Data Science

• Fraud and Risk Detection
• Healthcare
• Internet Search
• Targeted Advertising
• Website Recommendations
• Advanced Image Recognition
• Speech Recognition
• Airline Route Planning
• Gaming
• Augmented Reality

Applications Of Data Science In Business

• Gain Customer Insights

Using data science to collect and analyze Data about your customers can reveal details about their habits, demographic characteristics, preferences, aspirations, and more. With so many potential sources of customer data, a foundational understanding of data science can help make sense of it. With this you can  determine the best way to satisfy our customers.

• Increase Security

An entrepreneur can use data science to protect vital/confidential information regarding his business.

• Inform Internal Finances

An entrepreneur can use data science to create reports, generate forecasts, and analyze financial trends.

• Streamline Manufacturing

Data science will help and entrepreneur to identify problems and issues in his manufacturing processes.

• Predict Future Market Trends

The ability of data science to collect and analyze data on a large scale can help an entrepreneur to identify business trends in his market.

Five – Stage Live Cycle Of Data Science

• Capture  data acquisition, data entry, signal reception and data extraction.
• Maintain data warehousing, data cleansing, data staging, data processing and data architecture.
• Process data mining, clustering and classification, data modeling and data summarization.
• Analyze data reporting, data visualization, business intelligence and decision making.
• Communicate exploratory and confirmatory analysis, predictive analysis, regression, text mining and qualitative analysis

What is Datafication in Data Science?

The concept Data Science is a blend of various tools, algorithms, and machine learning principles with the goal to discover hidden patterns from the raw data. While Datafication refers to the collective tools, technologies and processes used to transform an organization to a data-driven enterprise. With this in mind we can say that Datafication in Data Science is simply the use of collective tools, technologies and processes by a Data Scientist to discover hidden patterns from raw data and also generate information and insights from data sets and identifying trends and patterns.

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