Great Startup Business Ideas For Successful Low Cost Startup

Best New Business IDEAS for 2014

New Business / February 10, 2017

The search for new business ideas and new business models is hit-or-miss in most corporations, despite the extraordinary pressure on executives to grow their businesses. Management scholars have considered various reasons for this failure. One well-documented explanation: Managers who are skilled at executing clearly defined strategies are ill equipped for out-of-the-box thinking. In addition, when good ideas do emerge, they’re often doomed because the company is organized to support one way of doing business and doesn’t have the processes or metrics to support a new one. That explanation, too, is well supported. Without a doubt, if you tackle business innovation systematically—rather than hoping people will get creative during an “innovation jam” or a special offsite—you improve the odds of success (and decrease the chances you’ll be left staring at a blank sheet of paper). Traditional, tested ways of framing the search for ideas exist, of course. One is competency based: It asks, How can we build on the capabilities and assets that already make us distinctive to enter new businesses and markets? Another is customer focused: What does a close study of customers’ behavior tell us about their tacit, unmet needs? A third addresses changes in the business environment: If we follow “megatrends” or other shifts to their logical conclusion, what future business opportunities will become clear?

We’d like to propose a fourth approach. It complements the existing frameworks but focuses on opportunities generated by the explosion in digital information and tools. Simply put, our approach poses this question: How can we create value for customers using data and analytic tools we own or could have access to? Over the past five years, we’ve explored that question with a broad range of IBM clients. In the course of that work, we’ve seen advances in IT facilitate the hunt for new business value in five distinct—but often overlapping—patterns. Those patterns form the basis of our framework. We believe that by examining them methodically, managers in most industries can conceive solid ideas for new businesses. (To learn about the underlying technical trends, see the sidebar “Why Are These Patterns Emerging Now?”)

None of the patterns depends on bleeding-edge technology. The first one, in fact, is very familiar: using data that physical objects now generate (or could generate) to improve a product or service or create new business value. Examples of this include smart metering of energy usage that allows utilities to optimize pricing, and devices installed in automobiles that let an insurance company know how safely someone drives. The second pattern is also familiar: digitizing physical assets. Fifteen years ago you could have read this article only in a printed magazine; now you can read it on half a dozen different digital platforms, send it to friends, and say what you think of it via social media. The third pattern is somewhat more recent: combining data within and across industries. (Here we start to enter the realm of “big data.”) An example of this would be a smart-city initiative like the one in Rio de Janeiro, where private utilities, transportation companies, and city agencies consolidate information so that they can deal with natural disasters more effectively. The fourth pattern is trading data; here, a company whose information is valuable to another company sells it, as when a cell phone service identifies traffic jams by seeing where customers in cars are slowed down and shares the information with a navigation-device company. The fifth pattern, codifying a capability, allows a company to take any process in which it is best-in-class—managing travel expenses, for instance—and sell it to other companies, using cloud computing.

The new businesses we’ve seen run the gamut from incremental to game-changing. Some simply enhance the current business (they’re sustaining innovations, in Clay Christensen’s terminology). Others are more disruptive: They require a new business model—and often a separate business unit—to support them. Still others evolve or could evolve into platform-based businesses—in which a stable core technology is surrounded by complementary products and services, typically provided by other companies. (Think iTunes and song and video recordings.)

In this article we’ll take you through each of the five patterns, providing examples drawn from our clients’ and our own experience. We’ll also provide a set of questions that can help you figure out whether a pattern is relevant to your business.

Pattern 1: Augmenting Products to Generate Data

Because of advances in sensors, wireless communications, and big data, it’s now feasible to gather and crunch enormous amounts of data in a variety of contexts—from wind turbines to kitchen appliances to intelligent scalpels. Those data can be used to improve the design, operation, maintenance, and repair of assets or to enhance how an activity is carried out. Such capabilities, in turn, can become the basis of new services or new business models. A classic example is Rolls-Royce’s engine health management (EHM) capability. In the mid-2000s new sensor technology and data management allowed Rolls-Royce to identify airplane engine problems at an early stage, thereby optimizing maintenance and repair schedules, and to improve engine design. The ability to control costs encouraged the company to adopt a business model in which it retained ownership of the engines and provided maintenance and repairs, charging airlines an all-in fee based on actual hours flown, as part of a “power-by-the-hour” offering. The new data from the sensors also facilitated other services, such as parts inventory management and flight efficiency reporting.

One could imagine Rolls-Royce extending this capability further—to engines for cruise ships and turbines—and even building a platform around it. The company could develop an IT-based system with the capacity to handle large volumes of sensor-generated data, and open it up to third-party applications geared to particular industrial contexts.

A more recent augmented product is SKF’s intelligent bearings, which contain miniaturized, self-powering sensors that continuously communicate their operating conditions. With this technology, bearings can be monitored in situ, which was previously impossible or impractical. SKF provides the data as an additional service that allows customers to see the extent of any damage within a bearing and take remedial action—for example, adding lubricant or mitigating overloads—well before a failure occurs. Machinery thus becomes more reliable and less vulnerable to downtime. The sensors also measure the load the bearing actually experiences—information that can be used to improve system and bearing design—and can detect problems outside the bearings, such as significant vibrations within the equipment.

There’s no reason nonindustrial companies couldn’t take a page from this playbook. Indeed, Progressive Insurance now offers a service called Snapshot, whereby the cost of insurance is based in part on how the customer drives the car. Progressive sends the customer a device that plugs into the car; it records things like mileage, night driving, and heavy braking.