Data analytics (DA) is the process of examining data sets in order to find trends and draw conclusions about the information they contain. Increasingly, data analytics is done with the aid of specialized systems and software. Data analytics technologies and techniques are widely used in commercial industries to enable organizations to make more-informed business decisions.


Data analytics can also be separated into quantitative data analysis and qualitative data analysis. The former involves the analysis of numerical data with quantifiable variables. These variables can be compared or measured statistically. The qualitative approach is more interpretive — it focuses on understanding the content of non-numerical data like text, images, audio and video, as well as common phrases, themes and points of view.
Advanced types of data analytics include data mining, which involves sorting through large data sets to identify trends, patterns and relationships. Another is predictive analytics, which seeks to predict customer behavior, equipment failures and other future business scenarios and events. Machine learning can also be used for data analytics, by running automated algorithms to churn through data sets more quickly than data scientists can do via conventional analytical modeling.
Mobile network operators examine customer data to forecast churn; that enables them to take steps to prevent customers from defecting to rival vendors. To boost customer relationship management efforts, companies engage in CRM analytics to segment customers for marketing campaigns and equip call center workers with up-to-date information about callers.
At the application level, BI and reporting provide business executives and corporate workers with actionable information about key performance indicators, business operations, customers and more. In the past, data queries and reports typically were created for end users by BI developers who worked in IT. Now, more organizations use self-service BI tools that let executives, business analysts and operational workers run their own ad hoc queries and build reports themselves.
Data analytics initiatives support a wide variety of business uses. For example, banks and credit card companies analyze withdrawal and spending patterns to prevent fraud and identity theft. E-commerce companies and marketing services providers use clickstream analysis to identify website visitors who are likely to buy a particular product or service — based on navigation and page-viewing patterns. Healthcare organizations mine patient data to evaluate the effectiveness of treatments for cancer and other diseases.
Data analytics applications involve more than just analyzing data, particularly on advanced analytics projects. Much of the required work takes place upfront, in collecting, integrating and preparing data and then developing, testing and revising analytical models to ensure that they produce accurate results. In addition to data scientists and other data analysts, analytics teams often include data engineers, who create data pipelines and help prepare data sets for analysis.


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