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Business analytics (BA) refers to the skills, technologies, applications and practices for continuous iterative exploration and investigation of past business performance to gain insight 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 using a consistent set of metrics to both measure past performance and guide business planning, which is also based on data and statistical methods.
Business analytics makes extensive use of data, statistical and quantitative analysis, explanatory and predictive modeling, and fact-based management to drive decision making. It is therefore closely related to management science. Analytics may be used as input for human decisions or may drive fully automated decisions. Business intelligence is querying, reporting, 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 why is this 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, use data analysis (or analytics, as it is also called in the business setting), to differentiate among customers based on credit risk, usage 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.
Types of analytics
- Descriptive Analytics: Gain insight from historical data with reporting, scorecards, clustering etc.
- Predictive analytics (predictive modeling using statistical and machine learning techniques)
- Prescriptive analytics recommend decisions using optimization, simulation etc.
- Decisive analytics: supports human decisions with visual analytics the user models to reflect reasoning.
Basic domains within analytics
- Retail sales analytics
- Financial services analytics
- Risk & Credit analytics
- Talent analytics
- Marketing analytics
- Behavioral analytics
- Cohort Analysis
- Collections analytics
- Fraud analytics
- Pricing analytics
- Supply Chain analytics
- Transportation analytics
- Contextual data modeling - supports the human reasoning that occurs after viewing "executive dashboards" or any other visual analytics
Analytics have been used in business since the time management exercises that were initiated by Frederick Winslow Taylor in the late 19th century. Henry Ford measured pacing of assembly line. But analytics began to command more attention in the late 1960s when computers were used in decision support systems. Since then, analytics have evolved with the development of enterprise resource planning (ERP) systems, data warehouses, and a wide variety of other hardware and software tools and applications.
With the recent explosion of big data and intuitive BI tools, data is more accessible to business professionals and managers than ever before. Thus there is a big opportunity to make better decisions using that data to drive incremental revenue, decrease cost and loss by building better products, improving customer experience, catching fraud before it happens, improving customer engagement through targeting and customization- all with the power of data. More and more companies are now equipping their employees with the know-how of Business Analytics to drive efficiency in day-to-day decision making.
Business analytics depends on sufficient volumes of high quality data. The difficulty in ensuring data quality is integrating and reconciling data across different systems, 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 type is considering a purchase, an analytics-enabled enterprise can modify the sales pitch to appeal to that consumer. This means the storage space for all that data must react extremely fast to provide the necessary data in real-time.
Competing on analytics
Davenport argues that businesses can optimize a distinct business capability via analytics and thus better compete. He identifies 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 toward an enterprise level approach to managing analytical tools, data, and organizational skills and capabilities
- Data mining
- Business Intelligence
- Test and Learn
- Business Process Discovery
- Customer dynamics
- cf. business analysis
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