Back in the 1970s, the founder of Federal Express rewrote all the rules of how you deliver documents. Now, new technologies like in-memory computing and connectivity are rewriting the rules of how you deliver data. The result: new companies with business models that don’t rely on physical inventory, but rather data and the insights derived from it.
Uber and AirBnB are the most famous consumer examples – and even Uber, with its ability to provide data on expense management, can also be counted as a business example. Either way, they’ve transformed the transportation and hospitality industries with new business models that put a premium on data and connectivity to provide a service.
But the change is by no means limited to digital-only companies. Manufacturers of everything from jet engines to in-home medical devices are building intelligence into their devices to create a constant feedback loop that ensures that they’re working as promised for customers.
For instance, Kaeser Compressors is a pump-machinery manufacturer, as traditional a company as you might find. It’s expanding its business model to offer services, such as pumping capacity, instead of just machines. It’s also using predictive analytics, fed by sensors built into the machines, to track maintenance under real-world conditions. That way, the company can better understand how machines respond when they’re used in certain temperatures or geographies (for instance, at higher altitudes).
For companies that want to harness digital efficiencies – not necessarily to tackle wholesale business models, but simply to make business processes better – can also look more granularly for ways to improve. One global company specializing in fragrances (such as those incorporated in detergents, shampoos, and cosmetics) must stay in compliance with regulations regarding chemical additives in hundreds of companies. It’s using Big Data in conjunction with connectivity to ensure that everything sold to its customers is compliant. The business process that benefits: compliance and risk prevention.
But at the same time, such insight can improve the entire manufacturing and supply-chain process. By understanding what additives must be limited to a specific market, the fragrance company can better determine how much of its product can be manufactured globally and then shipped to a particular region for localization.
Underpinning these capabilities is the ability to deploy all of this information in the cloud, where it’s easily accessible to customers, other geographies, or suppliers. Combine that with the ability to manage Big Data analytics in the cloud—thanks to data repositories running on high-speed in-memory systems—companies now have an option for fast deployment and fast insights.
The important thing for you to remember when faced with these opportunities is not to engage in technology for the sake of technology. Rather, you should think first about customer needs, and how to better fulfill them, and then link those to technological capabilities. If you start with the technology instead of the business problem, you’ll end up with the wrong solution.