Evaluation and Control Program Warning Flag 1 – The Illusion of Accuracy
“Measure with a micrometer, mark with a crayon, and cut with a chainsaw”
Author Unknown
Evaluation and control programs provide executives and managers with the critical information they need to make effective business decisions. However, an equally critical component of the decision-making process is the understanding that no data-set is a perfect reflection of reality. Therefore, it is important for business leaders to recognize the potential inaccuracies associated with their data in order to fully assess the risks these flaws pose to the achievement of desired outcomes.[wcm_restrict plans=”25541, 25542, 25653″]
The purpose of every evaluation and control program is to accurately represent the business conditions being monitored. Because of assumptions, averages, and/or approximations applied by evaluation processes and measurement systems, reality can never be perfectly represented. Leaders having an errant understanding of data accuracy will either over or underestimate the risk associated with decision options. Therefore, it is important for decision-makers to understand the accuracy of the data presented to them. Only with this information can the risks associated with each decision option be properly assessed and the decision-maker afforded the opportunity to select the best solution alternative.
The illusion of accuracy created by an evaluation method or measurement mechanism is a result of either the measurement process itself or the way in which the process is executed. While not all inclusive, the four lists below, Process-Based Warning Flags, Process Execution Warning Flags – Behaviors, Potential, Observable Results, and Potential Causes, are designed to help organization leaders recognize whether their evaluation processes and measurement systems unduly create the appearance of accuracy where less, little, or none exists. Only after a problem is recognized and its causes identified can the needed action be taken to move the organization toward improved performance.
Process-Based Warning Flags
- analytical processes don’t reinforce the application of mathematical principles for the use of decimals
- documents are not screened for either the use of absolute terms and/or non-observable adjectives
- procedures direct measurement beyond the limitations of prescribed measurement equipment, either out of range or less than one-half measurement increment
- lack of independent information verification through the use of alternate measurement devices
Process Execution Warning Flags – Behaviors
- conclusions stated as facts
- assignment of emotional labels
- lack of leadership challenge to the use of absolution terms or excessive numeric accuracy
Potential, Observable Results
- distorted perception of actual circumstances
- division between team members; often leading to deadlocks and infighting
- faulted decisions, either overly conservative or aggressive
- excessive use of decimal places
- use of absolutes, such as all, none, always, and never
Potential Causes
- undue desire for the feeling of security provided by having ‘hard’ data
- misunderstanding or lack of knowledge and/or experience in the application of sound mathematic principles
- lack of relevant situational experience resulting in excessive data focus
- inability to relate past experience with current circumstances
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[/wcm_nonmember]Additional Information
The following StrategyDriven recommended best practices are designed to reduce the likelihood leaders will receive data presented with an exaggerated accuracy.
- Self Assessment Best Practice 3 – Avoid Using Absolutes (article)
- Strategic Analysis Best Practice 3 – Identify the Hidden Drivers (article)
- Strategic Analysis Best Practice 3 – Identify the Hidden Drivers (continued) (article)
- StrategyDriven Podcast Episode 20 – Identify the Hidden Drivers, part 1 of 3 (podcast)
- StrategyDriven Podcast Episode 21 – Identify the Hidden Drivers, part 2 of 3 (podcast)
- StrategyDriven Podcast Episode 22 – Identify the Hidden Drivers, part 3 of 3 (podcast)
Dr. Rhian Silvestro is Associate Professor of Operations Management at Warwick Business School. Rhian has conducted service management research in a number of large, leading edge organisations including retail companies, banks, transport companies, health services and call centres. She has publications in over ten international journals in the fields of service design, performance improvement and supply chain integration.
Individuals naturally become edgy when monitored at work. Observation programs shrouded in secrecy further contribute to this sense of anxiety as observed subjects remain unaware of what is being documented and how it might affect their career. Consequently, observers can significantly reduce the observed subjects’ anxiety by making the observation, including observation documentation, as transparent as possible.[wcm_restrict plans=”42006, 25542, 25653″]
Effective performance measurement systems consist of high-quality individual measures associated with a strongly interrelated framework. Using this deliberately developed framework, leaders ascertain organizational performance quickly and accurately. The system itself should be economic to maintain and provide readily available updates typically necessitating a degree of automation. Quality systems present the same view of performance to a broad number of individuals within the organization concurrently. To achieve all of these qualities, each measure must be well thought-out and developed individually and then integrated into the collective system.[wcm_restrict plans=”41747, 25542, 25653″]
Managers translate leadership’s vision into the day-to-day actions of the workforce. They do this through their decisions, published standards, and operational procedures. They reinforce desired behaviors through organizational performance measures and management observations. But how do executives ensure their manager and supervisor direct reports understand and properly translate and reinforce their vision with the workforce? One method of doing so is through the conduct of paired observations.[wcm_restrict plans=”41999, 25542, 25653″]