Business Continuity is perceived as a challenge because since the XVIII century we constantly prized (the illusion of) absolute knowledge.
Since the advent of “scientific management”, we tried to “bean count” any event, often adopting the un-scientific approach of excluding information that did not fit our carefully designed models.
As our technology improved, adding more and more layers between every day, intuitive activities that we can carry out and the working of instruments and processes in our complex societies, we developed a defence mechanism to avoid accepting our impossibility to cope with a gazillion of details: we “layered” our approach to reality, assuming that layers we do not cope with are managed elsewhere.
While the increased fragmentation and specialization increased the efficiency, it reduced the strength of our governance, as we were unable to have a comprehensive view of the reality, and nobody had real operational responsibility.
An excessive focus on individual trees, with almost nobody caring even for her or his own forest: for an urbanized population, it is normal to assume that there are experts readily available for any need.
Our companies extended supply chains and increased complexity by outsourcing to third parties- often forgetting that maybe also our suppliers would apply our approach, and that a chain (including a supply chain) is as strong as its weakest link.
Using a spreadsheet we de facto outsource to the hardware and software supplier our computational skills: how many people are still able to carry out basic business computations in their own mind?
Most people trying to cope with Business Continuity focus quite often on something akin to an asset logging system.
What they try to do is not to control the purpose and identify alternative processes, but instead to maintain the current level of support and activity- crystallizing the “status quo”.
Our suggestion is to recover the way to have a grasp of the overall picture, partitioning the organization like a puzzle, and focusing on the knowledge interfaces between parts.
The adoption of this knowledge-fractioning ideas leads to the ability to define alternative paths to produce the same results, while stating the minimal level of activity that is required to cope with the unforeseen loss of a piece of the puzzle.
Except the military and organizations that are required by law to add redundant resources to ensure business continuity (e.g. banks, utilities), few organizations can afford the luxury of adding more than minimal disaster recovery facilities.
A technique that we used in various “knowledge and organizational mapping” assignments is to first recover the capability to visualize information, before we ask to start to collect and chart data.
As an example, for organizational design and database design in the early 1990s we applied some simple tests to see if managers and others were still able to think visually (nowadays, white-collar staff is mainly exploiting logical and verbal capabilities).
If not, we asked them to bring a pair of scissors, a pencil, and a notepad; after identifying some relevant idea, we asked them to write on a piece of paper each idea, and then cut them out, and try to rearrange them physically.
Once the optimal positioning was found, then the first draft was drawn on paper or using software tools for organizational design and mind-mapping.
Alternatively, in more recent times, a whiteboard and phone camera replaced paper-and-scissors.
Without these kindergarten-level exercises, endless time would have been spent drafting and re-drafting, due to the inability to think visually.
Eventually, the people involved recovered the ability to visualize knowledge and connections, and therefore to see each part of the corporate puzzle.
A visual approach enables to spot discrepancies faster than with the typical bean-counter approach.
But even while coping with uncertainty there are times where actually some number crunching (e.g. radar charts to compare “organizational maturity/compliance profiles”, scatterplots to identify “behavioural clusters) enables to “visualize” the interactions of dozens of entities.