I tend to examine the different roles played by data. For instance, when I work on computer code, I often ask myself what the presence of data is meant to accomplish. Sometimes the analysis is not at all straightforward or simple. In society and organizations, people exist and persist in the records as data. The data survives even as employees come and go. I therefore consider it important to regard the data and its environment as a system in itself, something that has a life all of its own. The data exists beyond the individual sentiments of managers, administrators, and customers. Data contributes to the process of reasoning and the expansion of knowledge. Consequently, I find it a bit disturbing to read about “big data” often in rather vague terms. I notice that discussions tend to lack a theoretical basis. In this blog, I will attempt to provide a conceptual foundation for big data as it relates to organizations. However, the underlying purpose of the blog is really just to stimulate discussion. I expect that some readers will immediately disagree with some of my points and offer their own perspectives. In the end, I am sure that the foundation for any field of study develops over time through collective and collaborative efforts.
Perhaps when the boiler was first marketed, some companies looked at the technology and thought, why would any organization need one of those things? Can the ability to heat fluid and move it to different places be useful? Think about the amazing feats of production made possible by being able to have many people comfortably occupying a shared space full of capital resources regardless of the time of year. Discussions about big data tend to suffer from the irony of being marketed in small terms; this makes it possible to dismiss a rather big idea by its apparent lack of congruence to isolated applications. Yet I notice no shortage of vague generalities. “You say this boiler will change the way we do business, but I don’t understand how it might help me sell more hogs.” I consider the following a worthwhile approach: start with a conceptual foundation and then build up a case. Trying to sell something complicated without any firm reference points can be problematic: generality plus peculiarity equals implausibility.
In order for me to offer a setting for big data, it is first necessary to examine the role of data in organizations. I have chosen to approach the challenge of establishing placement through systems theory. But rather than chop up the usual liver, I will go ahead and add my spin. Although the systems model seems to explain the flow of materials through organizations, it is also possible to interpret the model in relation to the flow of instructions, information, and data. In this blog, I will formally separate the structural complexities of an organization from its informational complexities. Many people are familiar with the basic components of systems theory. I want to point out that the theory holds the idea of progression or direction: the movement is from inputs to outputs and not the reverse. Direction and flow are particularly evident in organizations once the delegation of authority is assumed: organizational control tends to emanate from “the top.” Just to make my point, I made a minor addition to the systems model diagram.
The so-called “top” in relation to my flow diagram is at the left. I call the emanation of authority “projection.” Projection creates data by imposing specific requirements that can then be used to determine degrees of adherence. I call these requirements the metrics of criteria. There is a saying that goes, “What gets measured gets managed.” I suggest the truth is slightly different: “Managers measure what they want to manage.” My sentence is probably rather awkward to say; but anyways this is not a blog on English grammar. A long time ago in simple work environments, oversight and control were probably more direct.
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