I recently had a pleasurable discussion with a Dutch gentleman who had decided to take a sea change and renovate the French chateau where we were staying.
He had recently sold his business that had been in his family for three generations. The business manufactured simple concrete collars for reinforcing bars.
It had withstood the challenge of plastic substitutes and is still a healthy business today.
He related that when his grandfather started the business, all the collars were manually cast. On average, a single worker could make about 5,000 collars each day.
When his father took over the business, he introduced hydraulic machinery to assist the workers. A single worker was then able to make 25,000 collars per day.
When he himself ultimately took over the business, he used his electrical engineering training to introduce electronic control systems into the process. With this in place, each worker could now make 125,000 collars per day.
There is something nice and symmetrical about the manufacturing processes. When humans are acting as a piece of the larger suite of machinery, everything appears simple, logical and impressively linear and predictable.
It’s little wonder that the processes and practices learnt through generations of manufacturing experience have pervaded our thinking.
Our human staffed call centers are encouraged to behave like the concrete collar maker and follow the standard procedures and take advantage of the automated aids made available to help them be more productive.
Our hospitals and care centers are staffed by people who are now having the time they spend on tasks compared and benchmarked against scientifically created "best practice" benchmarks.
It’s the enterprise business intelligence (BI) systems that are responsible for collecting the data to assess the performance of the "machine" in the same way that our Dutch gentleman’s electronic control systems were inevitably collecting data from the concrete collar production line and feeding it up to himself and his operations managers to react to any unexpected variances in performance.
The problem is that services now employ something like 85 percent of the workforce, and they have grown largely at the expense of the manufacturing sector.
Services are human centered. Some services indeed lend themselves to codification as standard manufacturing like processes, but it is these types of jobs that are most at risk to automation.
What will remain are the higher skilled jobs, most of which require interaction and collaboration with others.
So if the future workforce is to be dominated by independent thinking and judging individuals working in collaboration to create unique, high value outputs, what place does a traditional hierarchical, activity monitoring enterprise business intelligence system play in our future?
I recently published an article that contained some compelling data showing how traditional activity measures exhibited absolutely no association with how the organization was collaborating.
In this article I challenged the traditional target for enterprise business intelligence being limited to line management i.e.. those charged with managing others.
There is an implicit assumption here that those managers’ having identified what they believe to be a performance issue will be able to instigate a simple intervention to put things "right."
The organization of the future is likely more comparable to the complex ecosystem of a Brazilian rainforest than the concrete collar production line my Dutch acquaintance had developed.
The following diagram shows a typical structure of a traditional Business Intelligence system that has hardly changed since the 1970s.
In essence they are reporting tools for managers. No doubt the small number of end-user managers that access these reports are learning something, otherwise they would not have made the investment.
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