If you really want to see the health of your business, watch what happens when a market shuts down.
What did you wish you knew?
What should you have been tracking?
If you knew these things, could you have prepared differently?
Could you have recovered differently?
If your response is "How could anyone predict a pandemic?" I say, think again.
Peering into enterprise technology investments and implementations, the story is clear. Instead of data leading strategy, plans, and processes, it followed the centuries-old industrial practices of lean automation. Lean automation is linear, defined, rigid. The only lever to pull is the one that drives profitability and does more with less. The data, even in the age of machine-learning-driven automation, is simple and contained to a handful of data points and 1–2 metrics. Thedemonstrated immediately from shutdown to treading water to reopening that our over-optimized businesses failed. There was no elasticity because the process defined the data, rather than data defining the process.
Not every enterprise stumbled, nor did every government. The defining factor wasn't how digital these public and private entities were. These surviving enterprises and governments embraced the data both pre-COVID-19 and during COVID-19.
Test and trace: In April, German Chancellor Angela Merkel announced to the world how she was going to balance opening up the country and economy based on infection rate and hospital capacity. Chancellor Merkel was able to state not only the metric (R0) for infection rate but she also communicated specifically how the change in the infection rate of .1 would indicate what would be opened, closed, and the social distancing orders to follow. Additionally, this rate indicated the capacity to treat COVID-19-positive patients as well as provide emergency and critical care to the rest of citizens. To do this, Germany had to put in place a broad testing and tracing approach to track infection and spread. This served as a model for New York, as Governor Andrew Cuomo is replicating much of this data-driven strategy.
Master the supply chain: Amazon was at the edge of the personal protective equipment (PPE) panic purchasing. Analysis of its partners quickly showed how demand spikes were translating into price gouging and PPE outages for medical workers. Additionally, Amazon was tuned into the broader purchasing patterns as governments implemented stay-at-home orders and business shutdowns. Analysis of data gave Amazon a window into quickly delisting price-gouging vendors, segmenting PPE sales to the needs of care communities, and optimizing sales and shipping to support essential products. It retooled its retail and logistics operations in a matter of days and scaled.
Master the market shifts: On the surface, stepping away from car manufacturing and sales to trucks only in the US seems radical. But Ford has pushed forward on a business transformation driven by data and analytic excellence. Recognizing changing customer needs as well as the nuances of various markets let Ford expand car production and sales in some global locations and still be confident of pulling back in the US. After COVID-19, Ford is practicing elastic manufacturing in response to new coronavirus safety practices and cases emerging on its plant lines, as well as bottlenecks from parts partners. It is able to model production to manage and predict the output to match economic opening, anticipate and measure contraction from shutdowns, and optimize practices to get plants open and running again after shutdowns.
Build everything for anything: Tesla and Dyson are innovation- and science-driven companies. For every product they engineer, they take into account how parts and engineering may transfer to new products and solutions. Tesla expanded its knowledge of batteries to its solar power products. Dyson has moved from vacuums to hand dryers and hair products, all on the same suction engineering. During COVID-19, each company was able to immediately translate its engineering knowledge to building ventilators. That doesn't seem like a data story, but it is. Properly capturing, maintaining, and sharing engineering specs, tests, and versions of emerging, older, and existing products allows these companies to more rapidly transition and build solutions for new needs and opportunities.
The post-COVID-19 world is a data-driven world. It is also a much more complex world, going against the grain of the "simple is best" aspect of the pre-COVID-19 viewpoint. Capturing all the data, in all its forms and formats, whether enterprises know they need it or not, is the new normal to survive. While businesses put plans in place for any global, economic, industry, and biological events they can imagine, it is the data they collect and can access that will bring strategic elasticity when needed for the things the C-suite doesn't see.
This post was written by VP, Principal Analyst Michele Goetz, and it originally appeared here.