The situation the world finds itself in with coronavirus is unprecedented in many ways. One example from a data science perspective is that the nature of available data and approach to testing for the virus is changing as we try to measure its impact. This is a nightmare scenario for many traditional methods in data science, as there is no baseline; “ground truth” does not exist. This lack of context impacts decision-making and the types of methods that can be effective.
Of course, the coronavirus is impacting economies, supply chains, and geopolitics. As it moves, the pandemic will affect different parts of the integrated value chain of the world in different ways and at different times. Even if unintentionally, governments will impact one another’s domains, making for compound disruption. This compound disruption also complicates efforts to use data to make informed decisions.
Learn and Evolve
If we draw a parallel with business, there is usually a point at which there’s enough information to make a decision, but not necessarily enough information on which to make a good decision. In this type of situation, one must constantly revisit what must be understood in order to improve the decision based on new data. As data changes, decisions and reactions must be adjusted. One must use methods that are learning in the moment – based on actions and reactions – since usually in a crisis, one doesn’t have historical data from which to learn.
Scientific thinking is largely being shown in the current crisis, with examples of briefings referencing what was said yesterday, what is believed to be true, and therefore what is being done. Such “grounding” of research is one of the hallmarks of good science. The point was made that science is always learning (whether in times of crisis or not). We will continue to learn more about the coronavirus impact on the world. Good leaders (government and business) must communicate authentically; for example, “I’m giving you the best guidance based on what I know today and I’m going to continue to learn and to adapt my response based on what I learn.” Test and learn is essential. “Failure” = the first attempt in iterative learning.
Get the Questions Right!
Data does not necessarily “speak”; it is the interpretation of the data that speaks. Yet anyone interpreting data will have biases. Different methods and techniques have preconditions. Methods such as machine learning often require some training or examples. Right now, we don’t have examples. We need to make inferences, move forward, and draw new conclusions. Data is important, but it is the preconditions, the critical thinking, the questions we ask, and the way we challenge bias that will get us through.
Use questions to guide thinking: What data is needed? What predictions are we trying to make? What is being learned? How does it keep moving us forward?
Finding Truth?
When asked about finding “truth,” in any crisis situation, the impact of truth really is more about what one believes at any one time and why. There will be many competing “truths” which come from different perspectives including latency of data, perspective of the observer, intentional perturbation or suppression of data, and other factors. During any crisis, there might not be an absolute truth. The advice was to focus on what is believed and why, and how those beliefs impact decisions taken. Use questions to guide thinking: What data is needed? What predictions are we trying to make? What is being learned? How does it keep moving us forward?
What is the best way to assess what data to believe?
- Triangulate – look to get the same data from another source (that doesn’t cite the same origin).
- Always ask the question What do I have to believe in order to believe this number? (e.g., if a country publishes data on infection rate, the first things you have to believe are that the country measures infection rate the same way you do, that the number is current, that information hasn’t been altered, etc.).
Leading in a Time of Crisis
For leadership in crisis, authenticity, communication, and collaboration will win. The more authentic one can be about what we know and don’t know, the better. Of course, this advice can be tricky in a world where everyone holds a smartphone. The term “infodemic” has been used to describe the state of being awash in conflicting data; with so much information (and sometimes misinformation) around, it becomes overwhelming (for leaders as well). The point was also made that not everything that is true stays true over time. This can be especially true in a time of a highly dynamic crisis, making authenticity, communication, and collaboration harder, yet even more important.
The Only Way Through This Is Together
What guidance can business leaders have for using data to navigate a crisis?
- Communicate authentically.
- Collaborate – there is no way any one person or organization can know everything about the situation.
- Think beyond the crisis – put some portion of mind share to what strategies you want to implement once we emerge from a crisis. Businesses want to be able to “run out” of the situation, not “fall out.”
Additionally, in a crisis, many organizations create two teams:
- Alpha team – the first responders dealing with immediate crisis issues.
- Beta team – responsible for thinking about what is being missed, what is needed to support the alpha team when they get to later stages of crisis support, and other longer-term issues.
It is essential that people, businesses, and governments work together both locally and globally through the immediate crisis and directly after. The world has changed, and the world order will not look the same after the coronavirus crisis.
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