The age of digital transformation is truly upon us and going strong. Every day, we hear of a new company with an exciting business model that is set to shake up the status quo and disrupt traditional business in their industry. We are familiar with the leading examples of these transformative companies, such as Uber and Airbnb, which have disrupted their respective industries.

Digital transformation, however, is not restricted to consumer businesses. Traditional Business-to-Business (B2B) enterprises are waking up to the potential and looking for ways to tap into it to deliver new services to their customers. For example, John Deere is moving beyond simply selling farm equipment and plans to use data from sensors on its equipment along with external data on weather and soil conditions for optimizing farm output and operations. This is an excellent example of data being collected from multiple sources and analyzed to dramatically improve farm operations-from optimizing equipment maintenance schedules to minimize downtime to crop cycle planning to maximize yields.

Another example is The Climate Corporation, which began as a start-up in Silicon Valley in 2005 (and was subsequently acquired by Monsanto in 2013) and has disrupted the U.S. crop insurance market, reducing farmers' financial risks. The company collects information on weather patterns, local climate conditions and soil characteristics. Using these insights, it offers farmers highly customized insurance policies against damage from weather events.

The ability of The Climate Corporation to monitor weather and related conditions in real-time results in timely assessments of weather-related damage, and significantly streamlines the process for farmers to file and receive payments against claims. Similar examples abound in financial services, manufacturing, supply chain, public sector and other traditional industries.

If you peer under the covers of these new services, a common theme emerges-the transformative use of data. Data from customers (e.g., both drivers and end users in the case of Uber), data from newly digitized processes and assets (e.g., tractor sensors), and, of significant importance, data from external sources (e.g., weather data).