Productivity, optimization and efficiency were the name of the game for the past few decades. Enterprises went all-in on business models, supply chains and IT infrastructures hyper-focused and lean as can be. Not a penny wasted, needless data stored nor a second lost. Redundancy became kind of a bad word. Then the world shut down.
Businesses that had been solely focused on the road they’re on were stopped in their tracks. All of that productivity, over-optimization and efficiency didn’t matter much when there was no business to be had on that road.
Lack of awareness (read: data) of anything else made it hard to choose which way to turn in what felt like darkness.
Workforces scrambled toward remote models, and organizations tried to refresh their business models in the same vein. But pivoting isn’t simply a matter of transforming your business experience or product into a Zoom call.
As Harvard Business Review puts it: “pivoting is a lateral move that creates enough value for the customer and the firm to share.” Doing that now requires a keen (read: data-driven) understanding of what “value” means in real-time. No doubt it’s a moving target.
Short-term Pivots and Long(er) Runways
Pivoting helps a business survive in the short-term — and gives its leadership the runway to build a foundation for resilience in the long haul.
Amazon quickly retooled its retail operations to automatically delist price-gouging PPE listings, while prioritizing essential supplies for healthcare and managing logistics for the wallop of stay at-home orders. Meanwhile, Ford shifted gears to manufacture medical supplies, and Tesla evolved from making ventilators for patients to molecule printers for vaccine developers.
“None of these businesses pulled these levers as shots in the dark,” says Sarma Musty, Technical Director.
In fact, companies like Amazon, Ford and Tesla were able to use real-time data they collect beyond their regular scope of operations to shine light on which direction to go.
Model of a Modern Major Data Architecture
Companies equipped with a modern data architecture that rapidly enables them to access, query and compare historical data and streaming analytics, LAST NAME explains, can call their shots, anticipate demand and lead the way into new economies with strategic elasticity.
Flexibility in data architecture is critical for analytics, artificial intelligence and navigating new norms, according to a recent report by MIT Technology Review Insights. That means being able to store, structure and analyze data, solve business problems and continuously improve the value you deliver to customers amid that short-term pivot and the long-haul recovery.
Demand for that kind of data architecture is nothing new.
In a 2019 big data survey, 54% of executives named the inability to be nimble and compete on data as a major threat to their businesses. Close to 80% said they feared disruption from their data-driven competitors. Imagine how those executives are feeling right now.
To the Future of Data and Beyond
Data, analytics and AI have always had the potential to spell out the future for businesses — but only if you have the right architecture to support it.
Flexibility and nimbleness give data teams the ability to adapt to growing capabilities and keep up in a world without precedent. Yet it also requires a mindset shift from the more pragmatic, over-optimized business models that had previously known no “waste.”
Take streaming analytics — this is live analysis on pools of in-motion data via constant queries on measurable events happening around a business. Executed well, streaming analytics helps businesses spot opportunities in an instant and highlight risks before they strike.
Streaming analytics, however, first requires real-time data feeds and an investment in the cloud storage capacity to move data wherever it needs to go in a flash. Businesses that stick to collecting and analyzing data on an as-needed, as-used basis will miss opportunities that they weren’t already tracking. They’ll stay stuck on that dark road.
That’s the thing about the post-pandemic world: we won’t know what we should’ve been tracking until it’s here. A modern data architecture creates space to predict those unknowns and builds a foundation for evolving data practices, pivoting through dynamic markets and adopting new technologies.
Resilience in the long haul will depend on data-driven decisions and the kind of strategic elasticity that prioritizes performance over the pure cost savings of not being flexible at all.