What Gets Measured Gets Done
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Introduction
The increasing role of IT places an emphasis on quantificational methods to perform the various management tasks. BPM (Business Performance Measurement [Blansfield (2003): 4]) and BI (Business Intelligence [Schlegel (2007)] as a set of IT-based management techniques and tools play a key role in our perception of a company and the performance of people. An entire branch of the software industry (SAP, Siebel, Oracle, SAS Institute, etc.) offers corresponding products. These products range from CRM- (Customer Relationship Management), ERP- (Enterprise Resource Planning) to KM- (Knowledge Management) – Systems. One has to keep in mind, however, that this approach of perceiving business is limited. In short, it relies heavily on quantification. This is due to the fact that
the data processed in these systems are data measured or processed by people or other machines.
Then it is categorized, evaluated, and synthesized, again by people or machines.
Finally, data processing entails interpretation and corresponding reaction. Also in this case, the actors can be people and / or machines.
Hence, the boundary of these concepts is measurability. On their background, only “what gets measured gets done”. In the following, this statement is critically reviewed. E.g. are there things that can not be measured properly but need to be – and are done – as well? What is the impact from the planning of the measurement process on the measured process? How feasible is measurement?
Definition
In itself, “what gets measured gets done” is a tautology, as we perceive (e.g. what is done) only what we measure – understood as processing sensory data in its broadest sense. Here, “what gets measured gets done” is interpreted as a promise to each business: If you measure the right things right, the right things are done right.
Measurement is understood in the context of a planned approach to observe, collect and evaluate data about business processes and its participants. In the direction of:
simple process data (e.g. production rates, failure frequency)
over more complex – often statistically processed – data on the actors in each business process (e.g. customer satisfaction)
and synthesized data (e.g. balanced scorecard [Kaplan (1996)])
to the general approach of company ethics [Schiebel (2000)],
measured data becomes more and more complex.
Measurement
From the managerial perspective, the key tasks performed in this context are: the planning of measurement, overseeing its process and evaluating the results.
Planning
The approach to planning is dependent on the process to measure and the definition of what the right things are. And it is an ongoing process. If, after measurement and evaluation, a business goal, e.g. high company performance, is not reached, a critical review on what has been considered as influence factor helps. E.g. if it turns out that simply increasing the output in a production process is not sufficient, one might have to consider quality assurance as well. Which can be measured, e.g. by a percentage of output that is quality checked. Still, it can be that not all “right things” are considered. Employee and customer satisfaction offer optimization potential as well. Both can be measured via e.g. regular surveys. And some companies even try to take their relation to their social and natural environment into account.
Usually, more complexity favours more automated processing of data. However, with more complexity, the information gained is more dependent on how the automated processing is customized and how people use it.
Overseeing
Overseeing is also an act of measurement. It is, in these terms, the measurement of the measurement process. Based on its outcome and evaluation, actions are taken to improve the measurement process. This can range from cancelling the entire process to adjustments e.g. of the definition of the criteria measured, or their scale, or clarification of the plan to the participators.
Evaluating
Overseeing leads then to evaluation – as evaluation is dependent on the validity of data, which is assured by overseeing. The data is interpreted in the broader picture of the process that was measured and its relation to the company. The evaluated data then becomes that what has been – or “gets – done”
Summary
Three requirements have to be met to measure the right things and to get the right things done.
The plan has to measure the right data the right way.
Right data means that the data must have a sufficient and appropriate impact on a business process and that its impact, corresponding implications, and cross dependencies are well understood.
Right measurement includes recording of all data that influence the measurement.
Second, to get the right things done, the last step, i.e. evaluation, has to be used as feedback to the planning-overseeing and evaluating -cycle. Hereby, the quality of the measurement process is improved.
Third, people participating in the measurement process must have access to the results. And they must be able to share the improved view on the business process. Provided that their (personal) goals do not contradict, they are hereby enabled to do the right things better.
Rйsumй
One indicator of the impact of “measurement” on businesses worldwide is the forecast of the market share of the above mentioned companies and corresponding systems [Goasduff (2007)]. Another is the media coverage on corresponding theories like Six-Sigma and back-end