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Business analytics

 

Business analytics

Business analytics (BA) refers to the skills, technologies, 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 statistical analysis, including explanatory and predictive modeling,[2] 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, 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 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 1
  • Types of analytics 2
  • Basic domains within analytics 3
  • History 4
  • Challenges 5
  • Competing on analytics 6
  • See also 7
  • References 8
  • Further reading 9

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.[3]

Types of analytics

Basic domains within analytics

History

Analytics have been used in business since the management exercises were put into place 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 to command more attention 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.[3]

In latter years the business analytics have exploded with the introduction to computers. This change has brought analytics to a whole new level and has made the possibilities endless. As far as analytics has come in 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.

Challenges

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.[3]

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.[4] 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

  • Bartlett, Randy (February 2013). A Practitioner’s Guide To Business Analytics: Using Data Analysis Tools to Improve Your Organization’s Decision Making and Strategy. McGraw-Hill.  
  •  
  • McDonald, Mark; Tina Nunno (February 2007). Creating Enterprise Leverage: The 2007 CIO Agenda. Stamford, CT: Gartner, Inc. 
  • Stubbs, Evan (July 2011). The Value of Business Analytics. John Wiley & Sons. 
  • 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. 
  • Baker, Stephen (January 23, 2006). "Math Will Rock Your World". BusinessWeek. Retrieved 2007-09-19. 
  • Davenport, Thomas H. (January 1, 2006). "Competing on Analytics". 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.  

Further reading

  1. ^ Beller, Michael J.; Alan Barnett (2009-06-18). "Next Generation Business Analytics". Lightship Partners LLC. Retrieved 2009-06-20. 
  2. ^ Galit Schmueli and Otto Koppius. "Predictive vs. Explanatory Modeling in IS Research". 
  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.  
  4. ^ "Choosing the Best Storage for Business Analytics". Dell.com. Retrieved 2012-06-25. 

References

See also

  • 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

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