Risk management is all about planning for the expected unexpected.
Natural events like hurricanes in the southeast, tornados in the midwest, or earthquakes on the west coast. Man-made events such as conflicts, strikes, and boycotts. Global pandemics.
Traditionally, this planning has relied upon historical data. This event happened last year and the year before that and three years before that, so we should plan accordingly.
But every couple generations or so, an event arrives that completely shatters every contingency. Right now, that event is COVID-19. All you have to do is take a look at the recent deep valleys in the stock market and skyrocketing unemployment numbers to know that many industries—and their risk management departments—are in uncharted waters.
So what happens when historical data is not only not relevant but non-existent? That’s where predictive analytics can come in.
Navigating uncharted waters
Boiled down, predictive analytics is about making decisions in the future based on the data of the past. In business, this means capturing and analyzing the unprecedented amount of data available today.
On the capturing front, we now have platforms like the public cloud to store the oceans of ones and zeroes created on the planet every single day. On the analyzing front, advances in Artificial Intelligence (AI) and Machine Learning (ML) are unlocking new and profitable insights in all that data.
Traditionally, the maxim for predictive analytics has been the more data that is available—data going back as far as possible—the greater the insight you can unearth. But in our current reality, where businesses are grasping at data from 1918 or even the 2008 housing crisis, there simply isn’t enough corollary information available.
So what now?
Even at this moment, when social distancing and absolute disruption is hitting industries globally, predictive analytics can still be valuable for businesses. You just need to know how to use it.
A faster future
When an unexpected event hits every corner of the globe, the businesses that survive are the ones that understand their exposure to that event before their competitors.
For an example of this, look back at June 29, 2007.
When the late Steve Jobs introduced the iPhone to the world, it turned out to be an earth-shattering disruption to the industry. Seemingly overnight, every other cell phone manufacturer was terminally behind the eight ball and forced to scramble to catch up.
With the benefit of hindsight, predictive analytics could have helped Apple’s competitors anticipate the iPhone’s revolutionary effect.
Analyzing online conversations about advances in touch-screen technology and more robust wireless networks, for example, could have flagged the innovation earlier.
Public WiFi usage on laptops could’ve revealed just how much people wanted—actually, needed—to always be connected.
All the signs for a massive explosion in the cell phone market were loud and present, but only a handful of analysts and tech bloggers were paying attention. And the rest is history.
Obviously, our current situation is much more dire comparatively, but the point still stands. Companies that are going to survive this crisis need the ability to pivot very quickly in order to limit their exposure, and one of the ways they can make that pivot is through predictive analytics.
The key, however, is to greatly accelerate your entire analytics process.
Speeding data to market
Traditional financial forecasting models can take months, if not an entire fiscal year, to be completed. In moments of uncertainty, however, organizations no longer have the luxury of time.
How do financial companies deal with thousands of low-risk loans that suddenly turned high-risk when thousands of people just lost their jobs? How do hospitals predict the needs of their supply chains when visits explode overnight?
The answer is to these and other questions is to abandon the traditional forecasting processes in the short term and focus your attention on ramping up predictive analytics models to run as data is coming in rather than after it arrives.
By doing this, financial institutions can run daily models that anticipate aid at the federal level to help people with loans, or take into count delays in monetary relief due to partisanship in Congress.
Hospitals can continually input evolving data to predict the needs of equipment based on crisis spread in manufacturing locations, or measure the response of foreign governments to highlight potential breakdowns in the delivery of supplies.
While such speed may not be sustainable for most enterprises, in the long run, the goal during a crisis such as this is to simply weather the storm by making many smart decisions as quickly as possible.
Get started today
Like every crisis our society faces, COVID-19 will eventually pass. Some businesses will survive, others will be reborn.
But as extraordinary as this global pandemic has been and will continue to be, there’s no reason to expect it will be a once-in-a-generation event.
Regardless of whatever form the next crisis takes, every enterprise should use this moment to build up its predictive analytics capabilities.
Unlike products and goods that can become severely constrained, as we’ve seen during an event such as this, data will always be available. The companies that not only survive but thrive during times of uncertainty will be the ones that are able to rapidly put that data to use.
If you want to learn more about leveraging predictive and advanced analytics, schedule a call with our team of experts. Otherwise, click here to read our in-depth guide to advanced analytics to learn more before diving in.
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