Your relationship with data is about to become one of your closest relationships (if it isn’t already). If you can make it core to everything you do, data becomes the new asset class: the fuel for an adaptable business strategy, the shortcut to productivity, and the route to entirely new opportunities.

We all know data volumes are exploding. In 2018, more than 50% of the world’s population was connected to the internet for the first time. This stat alone is a powerful proxy for the data pools that now exist. It’s never been cheaper or easier for human beings and businesses, governments and institutions to interact with each other.

With barriers to entry falling away, data-driven businesses are on the rise—with the data market in the UK (revenue from products or services derived from digitized data) already the largest in Europe. Meanwhile, the number of large-scale data centers is increasing by around 14% each year.

The traditional view of data is as a cost that must be managed—an overhead. But in a digital world, data is now an essential source of competitive advantage. In fact, data is now its own asset class, alongside traditional business assets (such as physical or human assets).

So who recognized its value first? Traditional companies are familiar with developing data strategies for their businesses. But digital natives were faster to understand the potential to develop a business strategy for their data. That is: using their data as an asset, a source of return on investment (ROI) in its own right.

The COVID-19 pandemic has demonstrated the need for companies to be on top of their data, with customer behaviors changing beyond recognition, coupled with crisis-driven innovation and immediate needs for employee safety and experience. More than ever, businesses must be relevant, and to do that, they must harness their data.

So what do companies need to do differently to compete? They must move on from business models that apply digital to traditional approaches and become digital at their core.

Fundamentally, that means doing three things:

Make data central and cyclical

How is it that companies like Amazon, Netflix and Google have business models that seem to shapeshift at the flick of a switch? Answer: it’s because their business strategies are driven by intelligence developed from thousands of curated, critical data points. Their data is the key to being able to spot market trends before the competition, and evolve strategies in close to real time.

The key to getting there: scale intelligence and deploy it to the front line (and customers). It sounds obvious, but the idea is that the AI experiments need to leave the lab.

Data and AI are—unsurprisingly—vital to establishing a data-centric business and well as creating productivity gains in the current model to invest in the new one. With automation projected to handle up to 45% of repetitive work, employees are freed up for value-add activities. And the productivity savings can be significant: Accenture analysis suggests up to 10% of tasks in the North American financial services industry could be automated by 2025, generating cumulative productivity savings of up to US$140 billion between 2018 and 2025.

But when you’re deploying these technologies, the real challenge is this: scaling them to the point of delivering the right results. It’s a development challenge rather than a procurement project—and therefore more cyclical in nature, like Amazon’s Flywheel model. It establishes a perpetual cycle of gathering data, analyzing, creating new products or services that entice more customers, creating more data…and on it goes. And every stage of the process is constantly monitored and optimized. Everyone at Amazon understands how it works and how to keep it fed with data. And it stands as an example of how to be a data-driven business.

Connecting customers through platform models

When data is fuel, you can use it to drive entirely new business models. One dominant example is the platform business model (which may also be a platform business). How is it different? While traditional businesses focus on delivering a specific product or service, platform businesses focus on connecting customers with what they need. They create value as networks, bringing an ecosystem of players together. And to do that, they must also treat data very differently. It’s their critical asset—and they organize their business to exploit it to the max.

This is no passing phase. Of the top ten most valuable public companies today, seven are platform businesses. And over the last decade, most incumbents have fallen out of that list. It’s clear evidence: value is migrating from traditional companies to new business models. And it’s decoupling assumptions about inputs and outputs: think of Alibaba (no inventory)…Uber (no cars)…Airbnb (no properties).

The delta: decoupling from linear benefits (the 10x, not 10%, mindset)

So why work so hard for these new business models? The reason is simple: it’s a new way to decouple inputs and outputs, and break through linear growth for exponential gains.

Businesses should be aiming to make 10X improvements—i.e., boost their business by a factor of 10, not 1 or 2. And AI, data and platform models offer those expansive new opportunities.

Example: a leading consumer packaged goods (CPG) company has created a platform play by turning to B2B ecommerce. The idea is: they provide the platform (a marketplace) to 1) sell directly to small retailers not covered by existing distribution networks (= more sales) and 2) make the platform available for other CPGs to sell their own products directly for a fee (= new revenues). This new strategy accounts for more than half of the company’s incremental growth. And it’s a great example of economies of scale and new routes to market: not just B2B, but also B2B2C.

One key tool for decoupling inputs and outputs is cloud. It offers flexibility like never before for data storage and managing workloads; plus better server utilization rates, and more energy-efficient infrastructure. And the delta is big, according to Accenture's research: Our analysis of the largest public cloud service providers shows average enterprise-owned-to-cloud migrations can lead to an impressive 65% energy reduction and 84% carbon reduction. And migrations to public cloud can result in up to 30-40% total cost of ownership (TCO) savings.

AI-fueled business models behave differently, because they’re able to move faster. This changes the nature of how you go to market and the cadence of product/service development. But you have to get your head around it.

It’s about working differently, with a more flexible mindset. For instance: it may be better to pursue a series of smaller bets than fewer, bigger bets—even though many (or even most) of those small bets will fail. It’s about capturing insights fast, acting on the signals generated by those insights, and trying more new things, more frequently. And that type of approach is the determinant of whether businesses can adapt, not get left behind.

By way of example: over the last couple of years, Amazon has tested myriad financial service propositions for its customers, from payments to insurance. This constant experimentation enables Amazon to make its business better benefiting both the company and its customers. Contrast that with traditional financial services/payments companies: many will have a single project running at a time, spanning several years. If it does eventually go to market, it may already be obsolete by its launch date.

The fail fast culture is already making big players much more agile, with GlaxoSmithKiline (GSK) explicitly acknowledging that 95% of experiments would be likely to fail, but those that were successful would be worth it.

But it all relies on the right skills and the right teams. We found only 25% of employees feel able to use data effectively. It’s time to rethink the workforce, and many businesses already have, with upskilling, and reskilling high on the agenda.