Business analytics ( BA ) refers to the skills, technologies, practices for continuous iterative exploration and investigation of past business performance to insight gain and drive business planning.  Business analytics focuses on developing new insights and understanding of business performance based on data and statistical methods . In contrast, business intelligence traditionally focuses on a set of metrics to measure both performance and business planning, which is also based on data and statistical methods. [ quote needed ]
Business Analytics makes extensive use of statistical analysis, including explanatory and predictive modeling ,  and fact-based management for driving decision making . It is closely related to management science . Analytics can be used as input for human decisions or fully automated decisions. Business intelligence is querying , reporting , online analytical processing (OLAP), and “alerts”.
In other words, querying, reporting, OLAP, and alert tools can answer questions such as what happened, how many, how often, where the problem is, and what actions are needed. Business analytics can answer questions like what is happening, what if these trends continue, what will happen next (that is, predict), what is the best that can happen (that is, optimize). 
Examples of application
Banks, such as Capital One , uses data analysis (or analytics , as it is also called in the business setting), to differentiate among customers based on credit risk , and other characteristics and then to match customer characteristics with appropriate product offerings. Harrah’s , the gaming firm, uses analytics in its customer loyalty programs. E & J Gallo Winery quantitatively analyzes and predicts the appeal of its wines. Between 2002 and 2005, Deere & Company saved more than $ 1 billion by employing a new analytical tool to better optimize inventory.  Telecom company that purses efficient call center
Types of analytics
- Decision Analytics: supports human decisions with visual analytics that the user models to reflect reasoning. 
- Descriptive Analytics: Insights from historical data with reporting , scorecards, clustering etc.
- Predictive Analytics : employing predictive modeling using statistical and machine learning techniques
- Prescriptive Analytics : recommends decisions using optimization, simulation, etc.
Basic domains within analytics
- Behavioral analytics
- Cohort Analysis
- Analytics Collections
- Contextual data modeling – supports the human reasoning that occurs after viewing “executive dashboards” or any other visual analytics
- Cyber analytics
- Enterprise Optimization
- Financial services analytics
- Fraud analytics
- Health care analytics
- Marketing analytics
- Pricing analytics
- Retail sales analytics
- Risk & Credit analytics
- Supply Chain Analytics
- Talent analytics
- Transportation analytics
Analytics have been used in the business of management by Frederick Winslow Taylor in the late 19th century. Henry Ford measured the time of each component in his newly established assembly line. But analytics began in the late 1960s when computers were used in decision support systems . Since then, analytics have changed and formed with the development of enterprise resource planning (ERP) systems, data warehouses , and a large number of other software tools and processes. 
In later years the business analytics have exploded with the introduction to computers. This exchange has endless possibilities. As far as analytics has come into history, and what the current field of analytics is today, many people would never think that analytics started in the early 1900s with Mr. Ford himself.
Business analytics depends on sufficient volumes of high quality data. The difficulty in acquiring data quality is different, and then deciding what subsets of data to make available. 
Previously, analytics was considered a type of after-the-fact method of forecasting consumer behavior by examining the number of units sold in the last quarter or the last year. This type of data warehousing required a lot more storage space than it did speed. Now business analytics is becoming a tool that can influence the outcome of customer interactions.  When a specific customer is considering a purchase, an analytics-enabled enterprise can modify the sales pitch to appeal to that consumer. This means the storage space for real-time data.
Competing on analytics
Thomas Davenport , professor of information technology and management at Babson College, argues that businesses can optimize a distinct business capability through analytics and thus better competition. He identified these characteristics of an organization that are apt to compete on analytics: 
- One or more senior executives who strongly advocate fact-based decision making and, specifically, analytics
- Widespread use of not only descriptive statistics , but also predictive modeling and complex optimization techniques
- Substantial use of analytics across multiple business functions or processes
- Movement towards an enterprise level of data management, data management, and organizational skills and capabilities
- Business analysis
- Business analyst
- Business intelligence
- Business process discovery
- Customer dynamics
- Data mining
- Test and learn
- Jump up^ Beller, Michael J .; Alan Barnett (2009-06-18). “Next Generation Business Analytics” . Lightship Partners LLC . Retrieved 2009-06-20 .
- Jump up^ Galit Schmueli and Otto Koppius. “Predictive vs. Explanatory Modeling in IS Research” (PDF) . Archived from the original (PDF) on 2010-10-11.
- ^ Jump at up to:a b c d e Davenport, Thomas H .; Harris, Jeanne G. (2007). Competing on analytics: the new science of winning . Boston, Mass .: Harvard Business School Press. ISBN 978-1-4221-0332-6 .
- Jump up^ “Analytics List” . Retrieved 3 April 2015 .
- Jump up^ “Choosing the Best Storage for Business Analytics” . Dell.com. Archived from the original on 2012-07-18 . Retrieved 2012-06-25 .