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The critical part of critical infrastructure

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We’ve been hearing a lot about infrastructure lately. After all, it’s the fabric upon which much of our global economy depends. From raw material exploration and extraction to design, processing, assembly, and delivery, infrastructure is present at every step in the value chain. The number of moving, interconnected parts is staggering, and all need to be operating in sync.

Although the probability of systemic failure is low, the impact can be catastrophic. Notable single points-of-failure include both the physical and the digital. On the physical side, for example, much of the Northeastern U.S.’s passenger and freight rail traffic traverses the Long Bridge at the Potomac River in Washington, D.C. Originally built in 1808, the current version has been in service for more than 11 decades. A similar choke point on the digital side lies 30 miles west in Ashburn, Virginia, through which approximately 70% of the world’s internet traffic passes.

A recent example of a combined physical/digital failure occurred in May of this year when a cyberattack on the largest U.S. pipeline disrupted fuel supplies throughout much of the Southeast. Similar examples can be found across the globe. In today’s complex, tightly coupled world, physical infrastructure cannot function without the digital and vice versa. Both need to be up and running 24/7.

This massive system of systems doesn’t run by itself. Someone (or something) must keep track of it all, which requires a synergistic balance between human and machine knowledge. And therein lies the third and most frequently missing piece of the puzzle: knowledge infrastructure.

When knowledge pipelines get clogged

Sometimes we need to remind ourselves that the raison d’être for KM is decision making. Early in the recent pandemic, decisions were made to shut down large segments of the economy. This resulted in empty stadiums, auditoriums, schools, office buildings, restaurants, and even cruise ships. The rationale was to minimize the spread of the virus, at least until we could figure out a more permanent solution.

Such stopgap measures are important. They buy us time. Better yet, a more knowledge-based approach would be to come up with the right stopgap measures as quickly as possible, and then replace those stopgaps with the right systemic solutions, while putting measures in place to prevent the problem from recurring. Sounds as if it is a perfect match for KM.

Unfortunately, in many crises, there’s a strong tendency to reduce a complex problem to soundbite-compatible “quick fixes.” But as we’ve seen with quick fixes, the rules and conditions keep changing. The challenge is to connect the dots in ways that produce safer, more effective, long-term remedies.

As of this writing, the economy is slowly recovering. Yet we’re still struggling to find a workable and  economically viable solution. A significant part of the struggle has been the changing nature of both the problem and the solution. On the problem side, mutations of the virus are a major concern. The solution side includes the challenges and unknowns that arise when dealing with a novel approach to immunization using mRNA. This speaks volumes about the need for a wide range of knowledge about many complex, interrelated disciplines and the ability to effectively apply it.

Vast amounts of resources—financial, personnel, pharmaceutical, medical, facilities, equipment, and supply chain logistics—were diverted to address this problem. However, as with any major shift in priorities, it also created problems/shortages in other areas that had previously benefited from those now-diverted resources.

In all, the U.S. Cybersecurity and Infrastructure Security Agency lists 16 critical infrastructure sectors whose assets, systems, and networks are considered so vital that their incapacitation or destruction would have a debilitating effect on security, the economy, public health, and safety. Even though we’re doing a better job of integrating the physical and digital components, the knowledge about each still remains separated from the others.

The problem is equally present within each discipline. In the field of medicine alone, more than 2 million peer-reviewed articles are published every year. This is a perfect example of the five V’s of information operating at full force: volume, velocity, variety, veracity, and value.

This is where ontology comes into play. You can’t connect the dots in a massively complex system if people, organizations, and communities from the many disciplines involved have different meanings (even opposing goals) for the same dot.

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