The term "critical thinking" is often found in job postings. Some would argue that this essentially means, "Thinking outside the box." Karl Marx, who asserted that labourers represent a class of people, has been described as a critical thinker. Regardless of how a person feels about Marx, it goes without saying that the phenomena of social classes is well-established. Politicians for instance fight for the support of the "middle class." How precisely does such an observation by this sociologist make him a critical thinker? Why would it be important at all from a business standpoint and especially in relation to data? Well, organizations greatly depend on the market demand for its products and services. It can be tempting to insulate the organization from emerging external conditions. For instance, Ford was surprised when the demand for Model Ts started to decline. Kodak was reluctant to enter the digital camera market. I hesitate to use more recent examples as it might seem like I am being critical about the organizations specifically. Actually I am focused on the nature of data.
Using the systems model in its linear format as a convenient template (input - process - output), consider an organization from left (input) to right (process) to right (output) to right (impacts) to right (consequences) to right (entrenchment). Here is a possible profile for an organization where terrible things have happened: strategic planning; operational planning; design; development; materials; assurance; production; quality testing; distribution; storage; sales; shipping; delivery; support; product failure; product dissatisfaction; injury and liability; regulatory action; enforcement of remedial standards. Of course in practice there are frequently both parallel and serial flows. There are also counter flows, which I will discuss a bit later. Consider how the nature of the data changes from left to right. To the left, the data is perceptual, intangible, and generally disembodied (free of form). As one moves towards production, distribution, and sales, physicality and embodiment become more common. Going further right, entering areas of impact and consequences, the information tends to indicate how the bodies have been adversely affected. "Your product has ruined my business." "Your product has harmed our family." "Your practices are socially reprehensible." In short, the nature of data changes contextually - from perceptual, to productive, and then consequential.
I also want to emphasize the prescriptive nature of data. For instance, along the perceptual domains where planning and design occur, there are also formal assertions - e.g. of what constitutes good products, bad products, successes, failures, good employees, and bad employees. There is definition of when things become recognized as particular events such as mistakes. I mean, most of us probably take it for granted that a mistake is a mistake. I have had the experience of contributing to criteria that establish the exact parameters leading up to the recognition of mistakes. If the criteria are changed, the underlying phenomena might not count as a mistake. Thus, to "count" something within the context of a prescriptive regime is to make use of the metrics of criteria; the outcome is highly reductive data - that is to say, counts and characterizations primarily serve to convey events within the prescriptive context. Given this context, it might be difficult for the data to properly reflect social consequences and perhaps, a bit closer to production, even consumer dissatisfaction. The metrics of criteria are pervasive, ensuring production irrespective of, say, changing consumer preferences. Sales tell us when people are buying less but not necessarily why and how to adapt to the situation.
At the moment, I have not found evidence of "critical data" in the business environment. But having started with Marx, I want to point out at this point that being critical usually means focusing on the consequences particularly over long periods of time. There is intent. There is action. There there are consequences. Whereas decision-making in an organization is somewhat confined, consequences expand: there are immediate outcomes, impacts, and then consequences that reach out like rings from a heavy object dropped into a lake. These developments can affect society for long periods of time. I am not saying that consequences are necessarily negative. However, when politicians start talking about "giving the middle class a raise," they actually mean far-reaching consequences from lengthy interactions between labour and production. What does this have to do with producing, say, widgets? Using prescriptive data from the production of widgets, actually there would appear to be minimal social impact. Similarly, when a company "discovers" that people have stopped buying its products - that preferences have turned to different types of products - the underlying truth of the production system might not be apparent. The invisible truth goes a bit like this: organizations have to find a place within the changing lives of people. It is highly instrumental to suggest just the opposite - that people exist to power the business model. Companies produce. People buy. That is the deal, right?
As a closing point to this blog, I also want to share certain dynamics that I have personally noticed. In relation to the data, the further the reach to the right, the further back it can be thrown. For example, talking about these widgets again, it is quite easy to notice defects near the production process. That short reach means that the problem can be thrown back just a bit for correction purposes perhaps to the people near the point of production. If one reaches further right, talking about a pattern or history of mistakes, the throw has more energy. If the objective is to completely realign thinking near the top, regulators and stakeholders might reach quite far right of the organizational construct by raising human rights violations and social justice issues. Many might point out, that sort of stuff in the consequential domain is hardly recognizable as data: complaints full of feeling and emotion, court cases, critical discourse, angry boycotts, move to competing products - generally things that appear non-substantive from a production standpoint. In summary, I would say that data can insulated from consequences, causing the organizational construct to be deprived of worthwhile information.