Business analytics
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Template:Wikify 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.[1] In contrast with Business intelligence, business analytics focuses on developing new insights and understanding of business performance whereas business intelligence traditionally focuses on using a consistent set of metrics to both measure past performance and guide business planning.
Business analytics can make extensive use of data, statistical and quantitative analysis, explanatory and predictive modeling [2], and fact-based management to drive decision making. 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 the questions: what happened; how many, how often, where; where exactly is the problem; what actions are needed. Business analytics can answer the questions: why is this happening; what if these trends continue; what will happen next-i.e. predict; what is the best that can happen- i.e optimize.[3]
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Examples of application of analytics
Capital One uses data analysis 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.[3]
History
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.[3]
Challenges of analytics
Analytics is dependent on data. The most important factor in being prepared for analytics is the sufficient volumes of high quality data. The difficulty in ensuring data quality is integrating and reconciling data across different systems and deciding what subsets of data to make easily available.[3]
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, in general, fact based decision making and, specifically, analytics
- Widespread use of not just descriptive statistics, but 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.
See also
References
- ↑ Beller, Michael J.; Alan Barnett (2009-06-18). "Next Generation Business Analytics". Lightship Partners LLC. http://www.docstoc.com/docs/7486045/Next-Generation-Business-Analytics-Presentation. Retrieved 2009-06-20.
- ↑ Galit Schmueli and Otto Koppius. "Predictive vs. Explanatory Modeling in IS Research". http://www.citi.uconn.edu/cist07/5c.pdf.
- ↑ 3.0 3.1 3.2 3.3 3.4 Davenport, Thomas H.; Harris, Jeanne G. (2007), [Expression error: Missing operand for > Competing on analytics : the new science of winning], Boston, Mass.: Harvard Business School Press, Template:Citation/identifier
- Davenport, Thomas H.; Jeanne G. Harris (March 2007). Competing on Analytics: The New Science of Winning. Harvard Business School Press.
- McDonald, Mark; Tina Nunno (February 2007). Creating Enterprise Leverage: The 2007 CIO Agenda. Stamford, CT: Gartner, Inc..
- Baker, Stephen (January 23, 2006). "Math Will Rock Your World". BusinessWeek. http://www.businessweek.com/print/magazine/content/06_04/b3968001.htm?chan=gl. Retrieved 2007-09-19.
- Davenport, Thomas H. (January 1, 2006). [Expression error: Missing operand for > "Competing on Analytics"]. Harvard Business Review.
- Pfeffer, Jeffrey; Robert I. Sutton (January 2006). [Expression error: Missing operand for > "Evidence-Based Management"]. Harvard Business Review.
- Davenport, Thomas H.; Jeanne G. Harris (Summer 2005). [Expression error: Missing operand for > "Automated Decision Making Comes of Age"]. MIT Sloan Management Review.
- Lewis, Michael (April 2004). Moneyball: The Art of Winning an Unfair Game. W.W. Norton & Co..
- Bonabeau, Eric (May 2003). [Expression error: Missing operand for > "Don’t Trust Your Gut"]. Harvard Business Review.
- Davenport, Thomas H.; Jeanne G. Harris, David W. De Long, Alvin L. Jacobson. [Expression error: Missing operand for > "Data to Knowledge to Results: Building an Analytic Capability"]. California Management Review 43 (2): 117–138.
- Ranadive, Vivek (2006-01-26). The Power to Predict: How Real Time Businesses Anticipate Customer Needs, Create Opportunities, and Beat the Competition. McGraw-Hill.
- Zabin, Jeffrey; Gresh Brebach (February 2004). Precision Marketing. John Wiley.
