Credit: Data Science Central
It wasn’t too long ago when somebody said to me, “You do reports when you get to doing them.” To me, this position is most defensible if the reports are for bookkeeping purposes. I pointed out one day that my reports are for management purposes; and for this reason timeliness is important. For instance, when one is driving a car, and it is necessary to turn at the next right, turning at the next right five lights later is fairly relevant. Timing counts. The “active” process of driving requires timely information. The “passive” process of putting records away might not require the same level of timeliness. When an accounting company handles financial records after the fact, in all likelihood it is too late to alter the day-to-day routine to significantly alter the unfolding of events. In this blog, I will consider a more pressing use of data.
First it is important to think of data in relation to systems theory. Data is an aspect of the “feedback mechanism.” When I was doing my degree in environmental studies, the term used was “biofeedback mechanism.” Think of predators in a forest causing a decline in the number of prey. Due to the lack of prey, there is a decline in the number of predators. With the number of predators lower, there is an increase in the population of prey. Then of course at some point the predators return. There is a biofeedback mechanism regulating the populations. In a company, data regulates production. If data is not collected and brought into the decision-making process, a company might keep producing unwanted products; never correct for defects in products; might hire employees even if the company is overstaffed; might never accommodate changing market needs and expectations; and might maintain process that are ineffective or inefficient.
Although not always associated with systems theory, there is the idea that the system itself can become more sophisticated as it responds to the feedback. Rather than risk localized extinction by decimating the prey population, the predators might develop responsible culling behaviours: maintaining its own population at sustainable levels; killing the weakest and sickliest prey; and eating the dumbest offspring. (So yes, I would have difficulty surviving in this vicious cut-throat ecosystem.) In a company, the question is how to improve operations in response to the data. I guess even before reaching this point, it is necessary to determine how to design the data such that operational developments can be actively managed. Some changes will lead to positive outcomes – others will do just the opposite. Some changes might only work in specific contexts; this makes it necessary for the data system to track these contexts.
An area of organizational management that I feel requires more coherent management is in relation to behavioural development. Some might use terms such as “behavioural modification” or “behavioural control.” My premise here is that good workers are built (my term is “Social Ergatigenesis”). Good workers don’t just appear out the blue perfectly aligned with the needs of the organization. Not only are they built, I suggest that they need to be constantly rebuilt depending on the changing needs of the organization. Humans construct their own habitats. Societies build their labour forces. Every worker usually has at least two identities: as a member of the work group; as a unique individual. Behavioural development must therefore be regarded in relation to these identities. “Development” necessitates the collection and analysis of conditioning outcomes on these different levels of identity.
As recent grass roots movements have demonstrated, workers also have an identity as a vulnerable and exploited group. The absence of feedback mechanisms to drive policy can lead to remarkably intense reactions – e.g. to build a wall around the United States to prevent migrants from taking jobs; to separate Britain from Europe for greater control over access to local jobs. It is a level of protectionism that seems to defy analysis after the fact – sometimes being described as the rise of the extreme right or ultra-nationalism. For me however there is simply a high level of misalignment due to the lack of systems feedback and systems development in response of that feedback; and perhaps there has been an extended persistence of that under-developed system – being insulated from realities faced by workers. Workers can become stressed if they are misaligned with operations. They can become alienated when that misalignment occurs at a deeper social-economic level.
Data therefore shouldn’t be regarded as something stale and stuffy possibly gaining voice in accounting records after the fact. Rather, data is an integral part of how an organism responds, survives, adapts, and evolves. Data logistics is about managing the data so it can be used for decision-making. Analytics is about systems development. And for those interested in Social Ergatigenesis, this is a data-driven perspective on the management of human resources. Human resources management has been about “finding” or “selecting” the best employee: this of course creates a need to replace employees that don’t fit. My position here is that “best” is not an absolute, but rather it is relative. Because of changes in the business environment, the best employee is never static. It certainly takes much more data to dynamically build workers rather than simply replacing them. In terms of the focus on non-development metrics (such as price), this is indicative of an organization lacking feedback mechanisms and therefore the ability to change. Companies preoccupied with austerity, attrition, and shrinkage can’t really claim to be part of the future of a growing industry; but rather they merely acknowledge that they are part of a past in permanent decline.