Dealing with Emergent Data

The recent and ongoing battle with COVID-19 has raised a goodly number of issues in and around getting surprised, arguments around emergent data, and slow/appropriate responses. The lessons learned so far are pretty rich, but I think there is more to discover. Scenario planning seemed to be lacking, the early warning systems seemed to have broken down, responses seemed slow and unpracticed once the denial hurdle was overcome. This was not a "Black Swan" event, so why did this pandemic seem to throw a monkey wrench into humankind's systems and processes? It may a bit early as all the dust hasn’t settled yet, but there are some obvious conclusions even now. What can we say now?

Scenario Planning

While pandemics are a practiced and expected scenario, the level of detail in this kind of scenario was tested in new ways. There were new discoveries in how trends in supply chains were working against us. With longer supply chains for medical supplies and equipment, the stress put on existing supply chains because of panic buying of many items, including food, and how do deal with rescinding demand in finely tuned supply chains. The detailed scenario planning and modeling really seemed off the mark this time on a worldwide basis. There were also some emergent geopolitical effects not completely thought through for sure. Scenario planning needs to handle models with more emergence in a fine-tuned fashion. Businesses and individuals need to up their game in this arena as well.

Early Warning

It is not surprising that less than effective scenario planning would lead to missing emergent data that was not expected, but as the emergent data morphed/changed, there were shadow events, signals, and patterns that took longer to recognize. Early warning needs to be able to recognize event patterns that go beyond expected events. These events and patterns need to participate in more complexity theory and real-time recognition that understands emergence in complex and interconnected systems and supply/value chains. This starts out at endpoint detection and merges with associated and real-time unassociated events to create emergent patterns. Agents/bots sniffing at the edge and event responding in the case of known situations and contexts is minimal, but merging and emergent complexities need to be tested, and models/scenarios need to be updated in near real-time.

Appropriate Responses

It seemed pretty clear that many responses were unpracticed, emergency stores were over-whelmed and tactical responses were being invented on the fly. Since appropriate responses are dependent on in scenarios and early warning, the compounding effect on responses were evident. There were some pretty impressive examples of human creativity/inventiveness and sacrifice to make up for the deficiencies, but can we plan on this always happening for the good of all? We saw governments taking over supply chains, people quarantined late and long, and decisions walking the razor edge between mortality and economic suffocation.

  

Net; Net:

We can do better.  We have to do better as more negative scenarios are emerging as nature deals out an accelerating frequency of earthquakes, hurricanes, volcanoes, pandemics, regional famines, and shifting geopolitical events. I suggest we invest in Emergent Data Recognition (EDR) tied to improvements in scenario planning and practiced responses from a strategic and tactical perspective. There are lessons in emergent data that we have to be ready to leverage. The more prepared we are, the better it will be for us all. It's worth the investment in terms of life and livelihood.