What is data analytics?\n\nData analytics is a discipline focused on extracting insights from data. It comprises the processes, tools and techniques of data analysis and management, including the collection, organization, and storage of data. The chief aim of data analytics is to apply statistical analysis and technologies on data to find trends and solve problems. Data analytics has become increasingly important in the enterprise as a means for analyzing and shaping business processes and improving decision-making and business results.\n\nData analytics draws from a range of disciplines \u2014 including computer programming, mathematics, and statistics \u2014 to perform analysis on data in an effort to describe, predict, and improve performance. To ensure robust analysis, data analytics teams leverage a range of data management techniques, including data mining, data cleansing, data transformation, data modeling, and more.\n\nWhat are the four types of data analytics?\n\nAnalytics breaks down broadly into four types: descriptive analytics, which attempts to describe what has transpired at a particular point in time; diagnostic analytics, which assesses why something has happened; predictive analytics, which ascertains the likelihood of something happening in the future; and prescriptive analytics, which provides recommended actions to take to achieve a desired outcome.\n\nMore specifically:\n\nDescriptive analytics uses historical and current data from multiple sources to describe the present state, or a specified historical state, by identifying trends and patterns. In business analytics, this is the purview of business intelligence (BI).\n\nDiagnostic analytics uses data (often generated via descriptive analytics) to discover the factors or reasons for past performance.\n\nPredictive analytics applies techniques such as statistical modeling, forecasting, and machine learning to the output of descriptive and diagnostic analytics to make predictions about future outcomes. Predictive analytics is often considered a type of \u201cadvanced analytics,\u201d and frequently depends on machine learning and\/or deep learning.\n\nPrescriptive analytics is a type of advanced analytics that involves the application of testing and other techniques to recommend specific solutions that will deliver desired outcomes. In business, predictive analytics uses machine learning, business rules, and algorithms.\n\nData analytics methods and techniques\n\nData analysts use a number of methods and techniques to analyze data. According to Emily Stevens, managing editor at CareerFoundry, seven of the most popular include:\n\nData analytics tools\n\nData analysts and others who work with analytics use a range of tools to aid them in their roles. The following are some of the most popular:\n\nData analytics vs. data science\n\nData analytics and data science are closely related. Data analytics is a component of data science, used to understand what an organization\u2019s data looks like. Generally, the output of data analytics are reports and visualizations. Data science takes the output of analytics to study and solve problems.\n\nThe difference between data analytics and data science is often seen as one of timescale. Data analytics describes the current or historical state of reality, whereas data science uses that data to predict and\/or understand the future.\n\nData analytics vs. data analysis\n\nWhile the terms data analytics and data analysis are frequently used interchangeably, data analysis is a subset of data analytics concerned with examining, cleansing, transforming, and modeling data to derive conclusions. Data analytics includes the tools and techniques used to perform data analysis.\n\nData analytics vs. business analytics\n\nBusiness analytics is another subset of data analytics. Business analytics uses data analytics techniques, including data mining, statistical analysis, and predictive modeling, to drive better business decisions. Gartner defines business analytics as \u201csolutions used to build analysis models and simulations to create scenarios, understand realities, and predict future states.\u201d\n\nData analytics examples\n\nOrganizations across all industries leverage data analytics to improve operations, increase revenue, and facilitate digital transformations. Here are three examples:\n\nUPS delivers resilience, flexibility with predictive analytics: Multinational shipping company UPS has created the Harmonized Enterprise Analytics Tool (HEAT) to help it capture and analyze customer data, operational data, and planning data to track the real-time status of every package as it moves across its network. The tool helps it keep track of the roughly 21 million packages it delivers every day.\n\nPredictive analytics helps Owens Corning develop turbine blades: Manufacturer Owens Corning, with the help of its analytics center of excellence, has used predictive analytics to streamline the process of testing the binders used in the creation of glass fabrics for wind turbine blades. Analytics has helped the company reduce the testing time for any given new material from 10 days to about two hours.\n\nKaiser Permanente reduces waiting times with analytics: Kaiser Permanente has been using a combination of analytics, machine learning, and AI to overhaul the data operations of its 39 hospitals and more than 700 medical offices in the US since 2015. It uses analytics to better anticipate and resolve potential bottlenecks, enabling it to provide better patient care while improving the efficiency of daily operations.\n\nData analytics salaries\n\nHere are some of the most popular job titles related to data analytics and the average salary for each position, according to data from PayScale. \n\nGive your analytics career a boost with big data and data analytics certifications.