“A Black Swan is an unpredictable event that is beyond what is normally expected of a situation and has potentially severe consequences. Black swan events are characterized by their extreme rarity, their severe impact, and the widespread insistence they were obvious in hindsight.”

Investopedia

If you’re anyone working in the industry or going through classwork in a school, chances are you’ve heard the cacophony about “Data-driven Insights”. At its simplest form, data about past behavior of customers could be extrapolated to tell about consumer behavior in future. From there, it could be made more interesting with Artificial Intelligence and Machine Learning. Theoretically, you could program a computer or train a machine to behave like a human being under given situations. With all the zillion bytes of data being generated every day by people hooked to internet, companies came up overnight who claimed to have years of experience into AI/ML. A whole new industry came up managing, curating and supplying the zillions of bytes of data. Watertight use cases were proffered, and those who didn’t buy it were doomed to fail. A cursory look at LinkedIn would reveal many of your old colleagues were suddenly “Data Scientists!” To be fair, companies needed to incorporate Data Science & Analytics, and people needed to upgrade their skills. There would always be hot trends, and people taking advantage of it. Business as usual? Maybe!

Then Covid-19 came along! All the zillion terabytes of data, and associated data driven fortune telling could neither predict the crisis nor the extent of it. Most of the economic activities across the globe came to a crashing halt. So, what happened? Did data fail us? Or we failed ourselves? The answer could be explained by 4 Cognitive and 2 Statistical Biases. Any of these biases would throw data driven predictions out of the whack, and their mix could be potently more dangerous.

What are these biases? The four Cognitive Biases at play here are – Anchoring Bias, Attribution Bias, Status Quo Bias, and Recency Bias. The two Statistical Biases are – Reporting & Social Desirability Bias, and Selection Bias. This is not in the scope of this article to describe these biases; intelligent readers would google that. Important thing here is, despite all kinds of data being available, there were 4⁴ X 2² = 1024 ways to ignore data that might have pointed to an upcoming pandemic.

So, is it really that shocking that Covid-19 brought things to a crashing halt, affecting every part of economy and sending two thirds of humanity into lockdown? Science publications have actually been warning about such a pandemic. Data about climate change destroying food chains and natural habitat of species have also been out there for a while. Right now, we are living through the 6^th mass extinction phase of species. By some estimates, Planet Earth has lost out 70% of land species and 40% of water species just since 1980! But unlike the previous 5 mass extinction phases, this sixth one is caused by human. Since 1970 till today, Humanity has wiped out 60% of all animal population.

“No wonder, with their food chains and habitat destroyed, bats might be eating things that made them pathogenic. And when such bats were consumed directly or indirectly by human, pathogens might have crossed over to humanity“

A Biomedical Scientist whom we consulted for this article

Amazon wildfire, Great Pacific Garbage Patch, air pollution making many cities unlivable, wildlife vanishing around us, manufacturing plants being far far away from the point of consumption, falling income levels, rising inequalities, insufficient access to healthcare especially to poor, insufficient R&D funds allocated to drug development, long drug development cycles, cramped spaces in large cities and on flights, populist and dictatorial governments who were totally ineffective, people eating anything, natural disasters getting more severe & more frequent, and a social media avatar based lifestyle that was totally oblivious to real life – none of these were easy to miss. Even pandemics were not so inconceivable; many pandemics have happened over the last hundred or so years. But nobody factored-in any of these in their data models. The aforementioned biases blinded the decision makers in industries and societies. They were “oh-so-focused”!

And we are not even beginning to talk about the special interest groups, lobbies, and religious preachers who would only strengthen these biases. Worse, they would push self-serving data that would contradict sensible findings.

In the hindsight, it’s obvious that a pandemic of the magnitude of Covid-19 was coming. It is rare, it has sent the whole world into abnormality, and it has severe consequences. It’s hard to deny that Covid-19 crisis has met all the criteria of a Black Swan event.

What is the way out then!? First and foremost, people need to go back to Sustainable Living. Consume more and more, experience this and that, devour all sorts of things, tie things like love and togetherness to material possessions, social stature being determined by wealth, not worrying about destruction in your trail, and likewise need to go away. While it might take some time to accomplish, we have to start practicing it NOW. It’s already too late. For those who would not follow it, we need to practice social distancing from them just like we are practicing with Coronavirus currently.

Second, understand that nature would provide for your need but hit back on your greed with all its fury.

Third, rely on data that come from authentic sources. Piecing relevant data together, cleansing it, and making it ready to use is a painstaking process that requires expertise, time and (lots of) money. Find out anything and everything about the data quality before you build or buy. Question the underlying assumptions. Question the degree of confidence with which the data could be relied upon. Broaden your scope, factor-in some unknowns. Human behavior is tough to predict, what would it cause is tougher to foretell.

Finally, realize that Artificial Intelligence would never replace Real Intelligence. Once you have assured you have the right data, you want to entrust intelligent employees and advisors with this data to derive actionable insights. While building a team, look for individuals with intellectual curiosity and right value systems. Besides, make sure the job duties of your organization do not resemble those of flour mill operations, where one pours wheat at Point A and takes flour out from Point B without ever applying one’s brains or opening one’s mouth. Neither should your ego determine who gets opportunities and promotions to perform more meaningful tasks. Organizations should embrace, even promote, a culture where they’re challenged with facts and qualified opinions. Intelligent people in organizations would see things on the horizon, make sure they got factored in business plans and figure out a way to keep going. They’d be averse to any biases. And they’d ask right, even uncomfortable, questions. For sourcing talent, application management systems should not be a graveyard of resumes. We would leave you with these iconic scenes from “The Big Short” about how NOT to shortlist candidates, and how to encourage questioning.