Return on … Infrastructure???
Knowledge infrastructure
After the Southwest Airlines incident, the flurry of discourse that immediately followed included a widely read social media post by a Southwest pilot who has been with the company for more than 35 years. Regarding Southwest’s IT infrastructure, he wrote about begging leadership to invest in upgrading technology, focus on customers rather than financial ROI, and measure the return on investing in infrastructure.
With regard to the NOTAM system incident, U.S. Department of Transportation Secretary Pete Buttigieg ordered an after-action review, including determining the root causes and steps to ensure it doesn’t happen again. It’s encouraging to know that KM best practices are being applied.
KM is about decisions. If an aviation information reporting subsystem experiences a glitch, do you halt all flights until the problem is fixed, or keep them up and running? One country chose the former, another the latter. Each choice has associated benefit/cost trade-offs. Ground all flights, and you have maximum safety but a high cost of travel disruption. Let the planes continue flying, and customers are happy, but the risk of a catastrophic accident due to erroneous or missing information looms. Either way, if things really go downhill, the costs can run into the millions, or even billions.
Ultimately, the quality of a decision depends upon the quality of the knowledge that’s applied toward making and carrying out that decision. That includes the quality of the input data and information. The objective is one with which we KM’ers are very familiar: Deliver the right knowledge to the right decision makers at the right time (which is when they need it). Incorporate the knowledge flows needed for making critical decisions into the early design stages of the infrastructure architecture itself.
A golden opportunity ahead
In the U.S., the Infrastructure Investment and Jobs Act of 2021 contains more than $550 billion in new infrastructure spending during the next 5 years. The act also contains numerous incentives that could result in more than $1.5 trillion in additional private investment. Globally, the World Bank Group pegs the needed investment, with emphasis on “smart infrastructure,” at around $1.3 trillion annually.
As a KM’er, you should be asking, “What good is pouring all of this money into infrastructure if we don’t address the underlying problems and their root causes and establish and maintain a formal cycle of innovation and learning in order to reduce repeated errors and reinventing the wheel?” This means keeping up with the pressures of a rapidly changing environment, including climate/weather extremes, pandemics, cyber/terrorist attacks, and other threats.
The good news is that we have a massive smorgasbord of KM-based decision- making affecting infrastructure. For example, designing a network of charging stations for EVs, deciding how many to deploy and where, and how best to minimize the impact on the energy grid. Or embedding AI/ML into smart roadways, rail, and the entire supply web. The same goes for locating pipelines, 5G networks, solar panel arrays, and wind farms with minimal impact on the environment and wildlife. Not to mention that there is a burgeoning need for optimizing the design, operation, and security of energy- gorging server farms.
All of these projects, many of which are interconnected and often interdependent, need KM to manage all of the moving parts, especially to improve resilience, reduce risk, and minimize costs. This applies to each and every one of the low-scoring infrastructure areas identified by the ASCE.
A fragile infrastructure can be very costly, even dangerous. But we can’t just throw trillions of dollars at it. Smart, knowledge-based infrastructure should maximize the benefit from the resources available, while minimizing the many risks and vulnerabilities. Given the massive scope of the planned upgrades coming over the next 5 years, when it comes to opportunities for applying KM, the sky’s the limit.