There is an idea that selecting the right analysis tool can compensate for a lack of managerial experience. The theory is that data analysis tools can answer questions that new managers cannot. Generally speaking, data analysis tools do provide a wealth of information regarding any facet of business operations. Unfortunately, data analysis tools cannot do all the work by themselves. It takes an experienced manager to not only understand what variables should be analyzed, but to also look beyond the analysis at the larger picture.
Conventional wisdom dictates that operational data analysis tools are only useful in the management of mundane details or financial and accounting applications. Data analysis tools can be configured to evaluate a myriad of variables ranging in complexity from the average size of ball bearings to the number of engineering changes in an engine design (Collier & Evans, 2008). The level of the individual measurements may be mundane, but the assembly of mundane measurements into a larger structure can lead to amazing operational breakthroughs. Obviously, some of these breakthroughs may be of great interest to the accounting department. The operations department should be equally interested in these breakthroughs, as cost cutting is a valuable endeavor for every employee.
All data analysis tools suffer when populated with inaccurate information. As John Hermansen (2004) points out, this problem has not gone away with time, but has actually become worse as data sets have grown larger. Although operations measurements are often gathered in an automated fashion, there is still an opportunity for mistakes, as some measurement collectors may not adequately account for errors. If the data behind the analysis is in error, then the entire analysis may be called into question. Therefore, the operations manager must seek to ensure the integrity of the data used in the analysis. Fortunately, software is available to help monitor the quality of data entered into the analysis tool (Hermansen, 2004). Since quality control is a routine part of most production processes, it stands to reason that the same principles should be applied to data acquisition.
Fortunately for those still interested in drawing a paycheck, data analysis tools will never replace experienced managers. Without the benefit of experience provided by seasoned managers, data analysis tools cannot hope to produce meaningful results. David Collier and James Evan (2008) offer the stipulation that “good performance measures are actionable” (p. 48). It is the role of experienced operations managers to define what measures are actionable and what actions should be taken. Although data analysis tools can automate much of the day-to-day decision-making in response to performance metrics, the ultimate authority on business tough decisions will always reside with managers.
Collier, D. A., & Evans, J. R. (2009). OM 2008 edition. Mason, OH: South-Western.
Hermansen, J. C. (2004). Data Quality: Is GIGO a Thing of the Past? InfoManagement Direct. Retrieved May 8, 2009.