Business analytics


Business analytics

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] 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,[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 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).[3]

Contents

Examples of application

Banks, such as Capital One, use 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]

Types of analytics

  • Reporting or Descriptive Analytics
  • Modeling or Predictive analytics
  • Data-Driven Strategy
  • Clustering
  • Affinity grouping

Basic domains within analytics

  • Retail Sales analytics
  • Financial Services analytics
  • Risk & Credit analytics
  • Marketing analytics
  • Collections analytics
  • Fraud analytics
  • Pricing analytics
  • Telecommunications
  • Supply Chain analytics
  • Transportation analytics

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

Analytics is dependent on data. The most important factor to the successful implementation of business analytics practices is 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.[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:[3]

  • 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

See also

References

  1. ^ 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. 
  2. ^ Galit Schmueli and Otto Koppius. "Predictive vs. Explanatory Modeling in IS Research". http://www.citi.uconn.edu/cist07/5c.pdf. 
  3. ^ 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. 

Further reading

  • 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). "Competing on Analytics". Harvard Business Review. 
  • Pfeffer, Jeffrey; Robert I. Sutton (January 2006). "Evidence-Based Management". Harvard Business Review. 
  • Davenport, Thomas H.; Jeanne G. Harris (Summer 2005). "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). "Don’t Trust Your Gut". Harvard Business Review. 
  • Davenport, Thomas H.; Jeanne G. Harris, David W. De Long, Alvin L. Jacobson. "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. 

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