Business– the business of daily operations – the business of change – the business of life – all business generates data upon which leaders rely. One might say that managers are in the business of measuring, analyzing, and then making use of data to support decision-making, tracking, projections, and maintenance constraints. One performs the inevitable and continuous analysis resulting in a selection of next steps while assuming the measures and metrics are unsullied. Thus, when the data cries foul, standards of integrity, professionalism, and of course, expediency require that a project leader self-report. However, is it true that data – when adequately operationalized – inherently lacks bias? Furthermore, can one genuinely expect a lack of bias in the data – or worse – is the data a result of the act of observation – thus removing the perception of free will in one’s decision?
One would expect, and business normalcy dictates, that one should report operationalized data that includes financial information, progress – and the combination of both – quality data and waste – and once again the combination of both. Additionally, the information provided often includes human resources information such as hiring and milestone achievements.
Many brilliant minds determined that to control a process, one must collect and analyze the data generated by the system in which the process operates. Almost ninety years ago, Shewhart, who is often considered the father of modern measurement, spearheaded the realization of the need for and implementation of process controls based on effective and specific standards to control critical specifications (Best & Neuhauser, 2006; Shewhart, 1933). Using statistical process management, businesses entered a new industrial age – one measured, managed, and controlled, and one in which improvements reached upwards of 230 percent increase (Deming, 1953).
Later, Ridgway decried the indiscriminate measurement of all things due to the wasted effort in the collection and analysis of inconsequential data (1956). Finally, in 1996 Kaplan and Norton blew the minds of leaders across the globe when they published The Balanced Scorecard giving purpose to the collection of data (Kaplan & Norton, 1996). The art of project measurement was back in vogue; thus, managers returned to the practice of generating and consuming data.
However, in the zeal for data, it seems management forgot, or returned to ignoring, the lessons of past generations. For example, between 1924 and 1933, while seeking ways to improve the efficiency of employees, Harvard researchers discovered that the act of observation often disturbs what one observes – the Hawthorne Effect (Cubbon, 1969; Mannevuo, 2018). Furthermore, in 1923 scientists discovered that one might show an object to be in one of two contradictory states depending on the state for which one tested (Rosenblum & Kuttner, 2011). Thus, as Rosenblum and Kuttner (2011) describe the phenomena, one may show an object as either “bigger than a loaf of bread or smaller than an atom” – it depends on the feature for which one tests.
Therefore, as a project leader, one must take caution when one realizes that one’s observations may produce the result for which one tests (Rosenblum & Kuttner, 2011). However, of more significant concern is the quantum effect that one’s very act of observations may cause the harm, for to observe is to intervene, and therefore, not only disturb the feature but to produce the observed effect (Rosenblum & Kuttner, 226). Thus, in response to this rediscovered revelation, the project leader must seek to mitigate the quantum effect.
To enact a quality plan for metrics from a project perspective, one must realize that the actions of the present may not have a linear effect. That is, the PM is first to understand the future and take present action with the intent of moving the end toward the present (Lord, Dinh, & Hoffman, 2015). In so doing, the PM then is free to decide regarding the necessary quality plan that proposes balanced observations prepared to alleviate potentially skewing the project trajectory.
Therefore, one’s quality measurement effort becomes one of preparation for the future rather than responsive to the past, thus avoiding the tendency to miss or ignore variation as an error (Lord, Dinh, & Hoffman, 2015). The effort of measurements then becomes:
- Searching for what is expected
- Searching for what should be expected
- Searching for what should not be expected
- Searching for what may be expected
The proposed four elements of project measurement are most easily understood from the perspective of an IT project. For example, in software testing, quality team members often plan to test the software in several ways.
- To ensure the software can do what it should do (what is expected)
- To ensure the software can properly handle all allowable alternatives (what should be expected)
- To ensure the software will not do what it should not do (what should not be expected)
- To ensure the software will gracefully handle unexpected entries or events (what may be expected)
As project leaders conceptually adopt the idea that one finds what one seeks, one better prepares to succeed. In addition, PMs are better able to control the outcomes of their projects when they realize the act of observation may itself determine the outcome.
Thomas Wise is Assistant Professor of Project Management at Harrisburg University of Science and Technology.
Best, M., & Neuhauser, D. (2006). Walter A Shewhart, 1924, and the Hawthorne factory. Quality & safety in health care, 15(2), 142–143. https://doi.org/10.1136/qshc.2006.018093
Cubbon, A. (1969). Hawthorne Talk in Context. Occupational Psychology, 43(2), 111–128
Deming, W.E. (1953). Statistical techniques and international trade. Journal of Marketing. Retrieved from http://www.marketingpower.com.
Kaplan, R. S., & Norton, D. P. (1996). The Balanced Scorecard; Translating strategy into action. Boston, Massachusetts: HBS Press.
LORD, R. G., DINH, J. E., & HOFFMAN, E. L. (2015). A Quantum Approach to Time and Organizational Change. Academy of Management Review, 40(2), 263–290.
Mannevuo, M. (2018). The riddle of adaptation: Revisiting the Hawthorne studies. Sociological Review, 66(6), 1242–1257. https://doi.org/10.1177/0038026118755603.
Ridgway, V. F. (1956). Dysfunctional consequences of performance measurements. Administrative science quarterly, 1(2), 240-247.
Rosenblum, B., & Kuttner, F. (2011). Quantum enigma: Physics encounters consciousness. Oxford University Press.
Shewhart, W.A. (1933). The role of statistical method in economic standardization. Econometrica, 1, 23. Retrieved from http://www.wiley.com.