What is Data Analytics?
To learn more, read about analytics fields listed below and then click on “Careers in Analytics.”
Data Analytics, Big Data, Informatics, Data Science….What Does it All Mean?
What is Big Data? What is the difference between data analytics, informatics and data science? If you are new to analytics, these terms might be confusing. Data analysts transform large amounts of raw data into useful information. In the past, data was structured and easier to manage. Big Data makes this job more challenging.
Data comes from many sources (social media, online shopping, wearable devices.) It is massive in terms of volume. It is varied (images, texts, audio, video, sensor data) and it is generated at a high velocity. Data analysts call this the volume, variety and velocity challenges of Big Data.
A growing number of professionals are needed to meet the demand for expertise in analytics. These experts capture, categorize, clean, store, explore, and interpret data for industries and organizations.
This field investigates data generated by commercial processes. It describes past performances helping organizations apply new management or marketing strategies. Analytics gives companies a significant competitive advantage. Market basket analysis and accounting analytics are two examples of business applications.
Bioinformatics employs sophisticated statistical and computational methods and models to understand biological processes. DNA, disease and other biological and physiological processes are studied with insights utilized by the healthcare and pharmaceutical industries to diagnose and treat patients.
Text data is largely unstructured and represents about 80% of all data. This type of analytics not only attempts to categorize and interpret the meaning of text but also studies the sentiments or opinions it conveys. This field may employ techniques like web scraping and natural language processing gain information about text data.
Geoinformatics processes and explores spatial information generated by sensors, satellites, mobile phones, or global positioning systems. Data is studied using geocomputation and geovisualization. Its applications include meteorology, defense, agriculture, logistics, aviation, criminology and environmental studies.
Analytics in health care creates efficient processes for patient care, finances, and hospital management. Data is acquired from pharmaceutical research, clinical trials, insurance claims, and electronic medical records. Health care analytics also focuses on patient behaviors and sentiment analysis which provides important information about customer satisfaction and patient compliance.
Sports analytics involves two major areas : on-field analytics and off-field analytics. On-field analytics collects and interprets data about team play and individual fitness to improve competitive strategies and athletic performance. Off-field analytics analyzes data pertaining to business practices within the sports industry to improve efficiency and profitability.
Learning analytics focuses on educational activities and institutions and the data they produce. By investigating testing trends, the output of electronic educational platforms, online learning programs, academic records and other measures of student behavior, learning analytics gains information to improve educational material and to differentiate instruction for students.
This fast growing field captures gaming data in real-time to enhance player experiences. Analysts use information to know when to adjust game processes that are either too easy or difficult for the majority of players or to adjust prices of game items that may be too expensive within a particular game.
With the growth of streaming music, movies and other forms of video entertainment, usage data is used for optimizing profit and the creation of new products. In almost every area where media is used to distribute entertainment, the analysis of data is essential for personalizing user recommendations, promoting music and shows, and investing in new material.
Fraud and Risk Analytics
Economic criminal activity impacts large industries and particularly banking and insurance organizations. Data analytics is utilized to detect the unauthorized use of stolen credit card numbers or fraudulent insurance claims. Banks and insurance companies employ risk analysis to evaluate the profitability of loans or insurance policies extended to customers.
Social Media Analytics
Social media analytics utilizes complex yet fascinating new tools to understand data in text form using natural language processing and machine learning. Sentiment analytics can gauge consumers' feelings towards business or other organizations so that companies can understand how to effectively interact with the public.
Cyber or security analytics ensure that hacking activity is quickly recognized and repelled when monitoring internet traffic. These threats and breaches are detected by processes which track data and patterns in internet usage and note unusual changes. Since these processes are deployed in real time, alerts can be sent to users the moment a concern is raised.