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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″]
Individual performance measures should be constructed such that they are both individually and collectively informative. Using a predefined performance measure development sheet drives developers to think though both individual metric qualities as well as how it relates and supports the other measures within the overall system.
Performance measure development sheets define the individual qualities and interrelationships producing high quality metrics. These sheets support more rapid and thorough development, speed system integration, and allow technology enablement. Performance measure development sheets should include the following information:
Final Thought…
One of the easiest to use and most effective performance measure development sheet designs is one that mirrors the metric style sheet to be used. Metric characteristics and parameters are overlaid on the underlying stylesheet making their associations readily observable and consistently interpreted by those implementing and updating the metric.[/wcm_restrict][wcm_nonmember plans=”41747, 25542, 25653″]
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Additional Information
Additional information on the individual characteristics of quality performance measures and their construction can be found in the following StrategyDriven articles and documents:
Articles
Documents
About the Author
Nathan Ives is a StrategyDriven Principal and Host of the StrategyDriven Podcast. For over twenty years, he has served as trusted advisor to executives and managers at dozens of Fortune 500 and smaller companies in the areas of management effectiveness, organizational development, and process improvement. To read Nathan’s complete biography, click here.
A performance measure’s value evolves from its ability to instigate and/or influence action. To do this, the measure must accurately reflect materially important performance parameters and present that information in a timely, readily understandable manner. It is to this later characteristic that performance metric style sheets are critically important.[wcm_restrict plans=”41741, 25542, 25653″]
General Performance Metric Style Sheet Characteristics
Style sheets reflect the graphic manner in which a performance metric is presented. These sheets should be aligned among types of measures and general characteristics so to consistently represent information to their readers thereby minimizing the chance of misinterpretation. Furthermore, they should possess several best practice display characteristics to maximize the metric’s value including:
Types of Style Sheets
Performance measures tend to aggregated information relative to the organizational level of the individuals for whom they are intended. Thus, the metrics presented to senior executives tend to aggregate vast amounts of information where metrics intended for first line managers tend to be very granular. Consequently, the style sheets associated with the various aggregations of performance data reflect the above characteristics each in a different manner. The sections below illustrate three different types of performance measure style sheets reflective of increasingly granular metrics presentations.
Business Unit/Fleet Level Executive Dashboard Style Sheet
Business unite/fleet level executive dashboards are collections of ‘window’ type performance indicators typically provided to the most senior executives. These dashboard style sheets possess the following characteristics (see Figure 1):
Department/Facility Level Executive Dashboard Style Sheet
Department/facility level executive dashboards are simplified individual metrics collections typically provided to the directors and managers. These dashboard style sheets possess the following characteristics (see Figure 2):
Title Area
Graphic Area
Individual Performance Measure Style Sheet
Individual performance measures are the most granular representations of organizational performance typically used by those managers and supervisors directly responsible for the indicated performance. These dashboard style sheets possess the following characteristics (see Figure 3):
Title Area
Graphic Area
Tabular data for the months shown including subdivisions and totals if used (common for stacked bar charts)
Analysis Area
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About the Author
Nathan Ives is a StrategyDriven Principal and Host of the StrategyDriven Podcast. For over twenty years, he has served as trusted advisor to executives and managers at dozens of Fortune 500 and smaller companies in the areas of management effectiveness, organizational development, and process improvement. To read Nathan’s complete biography, click here.
All performance is relative and performance measures without contextual references are largely meaningless. Such measures provide a performance count without a value indicator. Without this indicator, managers cannot know what, if any, action is required.[wcm_restrict plans=”41734, 25542, 25653″]
Contextual References
There are several types of contextual references that help reveal performance value; providing managers with the added information dimension necessary for identifying the need for follow-up action. Contextual references include:
1. Internally defined performance goals
2. Internally and externally derived benchmarks
3. Measurement normalization:
The number of contextual references is directly related to the amount of data available to inform management decisions. Well-defined performance measures contain three or more contextual references (typically references 1, 2, and one from 3).
Adding Contextual References to Performance Measures
Incorporating contextual references into performance metrics is relatively straight forward. Once defined, references 1 and 2 are superimposed on the metric itself. Reference 3 requires an additional data point be added to the metric calculation, one that is relatively stable over time. Note that the first two parts of reference 3 can be easily incorporated into the metric calculation and the third part derived from the time-based nature of the metric itself (often the x-axis) or by adding a time-based trend line anchored on a secondary y-axis (often located on the right side of the chart). If more than one ‘like’ parametric category is being measured, these can be presented as a stacked bar chart so to allow for the addition of the time-based contextual reference.[/wcm_restrict][wcm_nonmember plans=”41734, 25542, 25653″]
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About the Author
Nathan Ives is a StrategyDriven Principal and Host of the StrategyDriven Podcast. For over twenty years, he has served as trusted advisor to executives and managers at dozens of Fortune 500 and smaller companies in the areas of management effectiveness, organizational development, and process improvement. To read Nathan’s complete biography, click here.
Data access frequently challenges metric developers. Consequently, they may resort to using the most readily available performance data; data that can be obtained through a user defined production application query and downloaded into a Microsoft Excel spreadsheet or Access database. While such practices may be appropriate when developing proof-of-concept metrics, direct use of production data typically leads to metric instability resulting in low metric confidence and driving unintended organizational behaviors.
Why Production Data is Unsuitable for Organizational Performance Measures
Production data possesses qualities making it unsuitable for use in developing longer-term organizational performance measures. Primary among these is the dynamic nature of the data itself. By its very nature, production data is transactional and subject to frequent ongoing change – additions, deletions, and revisions. Consequently, metrics monitoring a series of period-based performance (such as monthly performance periods reflected in one metric covering a rolling 12 month year) can have a given historical period’s performance change from one publication of the metric to the next.
Another significant issue associated with using production data is extraction timing consistency. To consistently measure performance within defined time intervals necessitates data to be extracted at the same end time for each period such as 11:59:59 pm (23:59:59) on the last day of the week, month, quarter, year, etcetera. Not only is it highly unlikely that a manually timed extraction could occur with such timing precision but few individuals work at midnight and fewer at midnight on weekends and holidays where a period will occasionally end.
Benefits of Using a Data Historian
Data historian applications solve the issues associated with using production data as an input to organizational performance measures. Such applications extract a ‘data image’ at a consistent, specified period end time (eliminating extraction timing issue). These moment-in-time data images preserve performance as it was known at the time of extraction. Subsequently, metrics can be derived from a collection of data images – one for each reflected period – ensuring indicated period performance remains constant from one report publication to the next (eliminating the data variability issue).
Final Thought…
As previously eluded to, use of production data benefits the development of proof-of-concept metrics. Production data’s ease of extraction enables developers to more rapidly and cost effectively create and test these pilot metrics. That said, use of production data based organizational performance measures should be limited and done with a full understanding of their potential variability defects so as to not diminish confidence and drive inappropriate behaviors before the metric is properly implemented.
About the Author
Nathan Ives is a StrategyDriven Principal and Host of the StrategyDriven Podcast. For over twenty years, he has served as trusted advisor to executives and managers at dozens of Fortune 500 and smaller companies in the areas of management effectiveness, organizational development, and process improvement. To read Nathan’s complete biography, click here.
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