We all realize that significant societal disruptions – local, regional and global – are a part of our lives. Have been. Will be. Some will be health related. Others will be economic, environmental, societal or political. They occur regularly and often. Some are predictable; others, like the coronavirus, take us by surprise. As we continue to fight to save lives and reduce suffering, we are beginning to look for signs of the end (or at least slowing) of this pandemic and how recovery will take shape.
At SAS, we’ve developed our approach to mitigating these widespread disturbances, and we want to share that approach with you in hopes that your organization might find it useful, too, as you consider how to exit this disruption on your feet and still in the game.
Phase 1: Respond
In the earliest stages of any disruption, after you realize that something is amiss, your first steps are determining how severe/widespread/fast-moving a disruption, like coronavirus, will likely be. Let’s call that situational awareness.
Once we determine how the disruption will affect us, we look for immediate ways to respond. For instance, striking the alarm bell to the wider community, determining the next best steps to address the situation, allocating people and resources, etc.
We’ve learned with coronavirus that early and decisive action is crucial. Here are four things to consider when you respond to these types of destabilizing events.
COVID-19 resource hub
SAS can help all types of organizations with their response to the coronavirus pandemic. Our resource hub is full of helpful information, with access to a free dashboard report, data discovery modeling environment, blog posts and articles, educational resources and more.
Assess situation, understand, gather and collect
Leaders cannot make good decisions without a solid understanding of the current situation. Accessing and organizing the best data and then being able to create visualizations of the analyses of the data creates a common understanding and early consensus among stakeholders and helps improve downstream decision making and communication. At SAS we created a COVID-19 dashboard to share what the data has been telling us..
Initial efforts to identify some of the affected areas during the upheaval are based on intuition and are predictable, sourcing food and medical supplies for example. Other factors and forces may only become apparent over time or they may be wholly unexpected. In an environment that can change hourly, decision makers need methods to test, model and evaluate the potential costs and benefits of mitigation actions and policies – and need to implement them as quickly as possible. With the coronavirus, when it became clear that social distancing was an important mitigating factor, many governments quickly established distancing guidelines.
Optimize supply chain
Making sure that the supply chain remains intact for critical goods, and anticipating future surges (for example, ventilators and related respiratory treatments) is critical to the management of a large outbreak as it transitions from initial stages toward more serious levels of impact. An important activity in this stage is assessing the state of the supply, whether it’s food, medical services, consumer goods, etc. While the demand during these types of disruptions is likely to be “Give me everything you have!”, the supply needs to be addressed in terms of suitability and level of readiness. We have seen with the COVID-19 response that sometimes equipment is not fit for purpose (protective gear that may not provide the needed level of protection) or is not ready for use (in the case of medical equipment that was not properly maintained while in storage).
Maximize resource capacity (or how to make sure I have what I need)
Nearly every industry is experiencing shortages as demands across people, infrastructure and assets are continuing to grow to critical levels. Identifying areas where workforce optimization is needed – and how to best distribute limited resources such as ICU beds, ventilators, etc. – is not always immediately obvious. Sometimes, it’s not the first requestor who will need the resources the most, and you need to be careful about allocating resources to help ensure the most critical needs are met. Analytics can help by using all available data to give better guidance and more accurate projections.
Phases of disruption
One way to overcome the effects of a pandemic or other crisis is to evaluate the types of decisions and actions you need to take based on a framework that’s built on a three-phased approach: respond, recover and reimagine. This framework can help you mitigate COVID-19 disruptions by enabling you to focus on specific issues over time, rather than being overwhelmed by trying to do it all at once. Data analytics helps guide your decisions along the way.
Phase 2: Recovery
When you consider recovery, there is the short-term focus, patients recovering from their illness, and long-term focus, the economic and social recovery.
Many COVID-19 patients will fully recover, but there may be a subset for whom there are long-term chronic health effects. This will affect both providers and payers. Analytics can help ensure good health outcomes, while helping forecast and manage future costs and resource impacts.
As we begin to emerge from isolation and businesses and governmental functions begin to resume their efforts, it’s unlikely that the restart will be a smooth one. For instance, how will apparel manufacturers and sellers deal with inventory that now has moved into clearance territory? For health care, how do hospitals and doctors handle the huge backlog of routine visits to the doctor and elective surgeries that were postponed?
Every aspect of society and every industry will have challenges as everything is rebooted. This makes the use of AI technologies such as machine learning models even more important to be able to match supply (of goods and services) with demand – and to understand how the demand patterns have shifted and will continue to shift in the days, weeks and months to come.
Phase 3: Reimagine
Reimagining is about future-proofing your organization by building resilience. New waves of COVID-19 may occur later this year, or in 2021, and it will retest our response/recovery capabilities. Analytics can greatly advance our ability to prepare for future waves of COVID-19 or other pandemic threats by identifying and understanding vulnerable populations and their risks.
It's important to understand that for businesses, organizations and governments these disruptions can occur with some frequency and may be more localized. Having an incident response plan at the ready is crucial for every level of government and for every industry.
But it is not just an effort to rebuild. It’s an opportunity to reconsider how you’re operating and applying lessons learned during this outbreak and how you can be more agile and responsive in the future. Clearly, data and modeling have been used to answer lots of questions and will play an even bigger role as we begin to move forward.
In an environment that can change hourly, decision makers need methods to test, model and evaluate the potential costs and benefits of mitigation actions and policies – and need to implement them as quickly as possible.
- Analytics tackles the scourge of human traffickingVictims of human trafficking are all around us. From forced labor to sex work, modern-day slavery thrives in the shadows. Learn why organizations are turning to AI and big data analytics to unveil these crimes and change future trajectories.
- Understanding capital requirements in light of Basel IVMany financial firms are already using a popular 2012 PIT-ness methodology for internal ratings-based models. This article examines eight ways the industry is successfully using the methodology – and why this approach can bring synergies for banks, value for regulators, and major competitive advantages.
- How health care leaders deployed analytics when crisis hitDuring the COVID-19 pandemic, some health care providers were well-positioned to respond to rapid changes in demand. The factor that most distinguished them was that they already had a strong capacity in place for using data to inform decisions. Read about three key takeaways from their experiences.