Template:M intro design System redundancy: Difference between revisions

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 [[System redundancy|The]] [[JC]] likes his pet management theories as you know, readers, and none is dearer to him than the idea that we have become been hostage to [[high modernism]].
-A distributed organisation is best controlled centrally, and from the place with the best view of the big picture: the top. All relevant information can be articulated as data — you know: “[[Signal-to-noise ratio|In God we trust, all others must bring data]]” — and, with enough data everything about the organisation’s present can be known and its future extrapolated. The organisation’s permanent infrastructure should be honed down and dedicated to  its core business, and its peripheral activity — operations, personnel, legal and ~ cough ~ strategic management advice — outsourced to specialist providers of administrative services which can be scaled up or down to meet requirements or switched out altogether.
+A distributed organisation is best controlled centrally, and from the place with the best view of the big picture: the top. All relevant information can be articulated as data — you know: “[[Signal-to-noise ratio|In God we trust, all others must bring data]]” — and, with enough data everything about the organisation’s present can be known and its future extrapolated. The organisation’s permanent infrastructure should be honed down and dedicated to  its core business, and its peripheral activity — operations, personnel, legal and ''~ cough ~'' strategic management advice — outsourced to specialist providers of administrative services which can be scaled up or down to meet requirements or switched out altogether.
-This philosophy, espoused as it is by ~ cough ~ strategic management advisors — can seem a touch self-serving, but recommends a maximally efficient allocation of company resources, and has seen a generational drift from inefficient business systems run poorly by humans to well-run machines: infinitesimally-sliced ''processes'', each triaged and managed by a pre-programmed, automated applications, with minimal human input, provided by external service providers. The unscientific conduct of commerce did not survive contact with “business-process-as-a-service”.
+This philosophy, espoused as it is by ''~ cough ~'' strategic management advisors — can seem self-serving. It recommends maximising the efficient allocation of company resources. It is responsible for a generational drift from inefficient businesses run arbitrarily by unionised humans to enterprises run like machines: infinitesimally-sliced ''processes'', each triaged and managed by a programmed, automated applications, with minimal human oversight, provided by external service providers. Business became “business-process-as-a-service”.
-It feels like we are in a new and better world but, at the same time, customer experience feels as grim as ever. Business-as-usual-as-a-service has streamlined and enhanced the great heft what businesses do, at the cost of sacrificing outlying cases that the model says cannot make out a business case. We call this effect “[[Pareto triage]]”. Great, for the huddled masses who just want the normal thing. But that long tail of oddities and opportunities is poorly served. Those just beyond that “[[Pareto triage|Pareto threshold]]” have little choice but to manage their expectations and take a marginally unsatisfactory experience as the best they are likely to get. Customers subordinate their own priorities to the preferences of the model. This is a poor business outcome.
+It feels like we are in a new and better world while customer experience feels as grim as ever. “BAU-as-a-service” has streamlined and enhanced the great heft what businesses do, at the cost of outlying opportunities for which the model says there is no business case. We call this effect “[[Pareto triage]]”. Great, for the huddled masses who just want the normal thing. But it poorly serves the long tail of oddities and opportunities. Those just beyond that “[[Pareto triage|Pareto threshold]]” have little choice but to manage their expectations and take a marginally unsatisfactory experience as the best they are likely to get. Customers subordinate their own priorities to the preferences of the model. This is a poor business outcome. And, unless you are McDonald’s, the idea that 80% of your customers ''want'' exactly the same thing — as opposed to being prepared to put up with it in, the absence of a better alternative — is a kind of wishful [[averagarianism]].
 Even though, inevitably, one has less than perfect information, extrapolations, mathematical derivations and [[Large language model|algorithmic pattern matches]] from a large but finite data set will have better predictive value than the gut feel of “[[ineffable]] expertise”: the status we have historically assigned to experienced experts is grounded in folk psychology, lacks analytical rigour and, when compared with sufficient granular data, cannot be borne out: this is the lesson of {{br|Moneyball: The Art of Winning an Unfair Game}}. Just as Wall Street data crunchers can have no clue about baseball and still outperform veteran talent scouts, so can data models and analytics who know nothing about the technical details of, say, the law outperform humans who do when optimising business systems. Thus, from a network of programmed but uncomprehending rule-followers, a smooth, steady and stable business revenue stream [[emerge]]s.