Companies continue to find new ways to generate business value from data. Below we look at some examples that prove the possibilities are endless and ideas can come from the most unexpected places.

Whether it’s unlocking operational efficiencies that save money or uncovering new opportunities to make money, data is driving business value and improved experiences more than ever before. This innovation is being fueled by the ongoing proliferation of Internet of Things (IoT) devices and the trillions of gigabytes of data they are generating. The increasing influx of data is being driven by longer range connectivity between IoT devices and lower cost sensors that are becoming more and more pervasive, collecting data from just about anywhere.

Yet, we are only seeing the tip of the iceberg when it comes to turning this newfound wealth of information into business value. In fact, according to a recent Knowledge @ Wharton article, “A vast amount of data that is discarded—the so-called ‘data exhaust’—actually hold a lot of value and could be tapped to create new competitive advantages.”

The article makes the case that for product development and design data is an ”afterthought” for most companies, and this needs to change. Instead, data and the value it provides should be thought of as a driving force behind why the product even exists.

One example of this mindset in practice comes from a start-up called Propeller Health, which is transforming how patients use asthma inhalers. By embedding sensors into its asthma inhalers, Propeller Health is delivering a transformative product that collects data on how, when, and where people are using their inhalers, offering intelligence that helps customers better manage their respiratory health. According to the article, “After integrating detailed usage data across patient populations with external data like weather and air quality to provide real-time, personalized coaching, Propeller has shown a 50 percent reduction in unplanned asthma attacks with the potential to save billions in health care costs.”

Another example comes from Progressive Insurance, which is turning sensor data on driving behaviors into new risk models to help lower premiums for good drivers. The new model already accounts for 20 percent of the company’s direct channel revenues, according to the article.

In both cases—and this is important—customer value is being created.

Feeling inspired?

Below are some tips from an HBR article, offering five essential elements for data strategy success:

1. Quality Data: It should come as no surprise that good data is critical. But simply having it is not enough. Your company, and more specifically your people, need easy access to the right data at the right time, combined with the latest tools to turn insights into actions that benefit the business and its customers.

2. Means to Monetize: Like any profitable product, data needs a business model in order to successfully generate revenue and profits. This includes selling it directly, factoring it into new and existing products to drive growth, and using it to improve internal processes to reduce costs.

3. Organizational Capability: According to the HBR piece, “Talent, structure, and culture” all come into a play for successful data monetization strategies. The advice: Start with talent. Make sure you have the right people for the job, which often requires you to look externally. Look beyond traditional data scientist skills set for individuals that have good business sense, are good communicators, and can integrate the new and old alike.

4. Technology: In addition to the basics of computing and storage, the need for new technologies is required for effective data monetization. This includes “sophisticated architectures, analysis tools, and cognitive technologies that are the engines of monetization.”

5. Defense: With data emerging as a company’s most valuable asset, there is more need to protect it than ever before. This includes cyber-security and keeping your data safe from bad actors, but it does not end there. Companies need to be compliant with data privacy laws and regulations, like GDPR, as well as transparent with external parties on how data is used and why, especially with customers.

To learn more, read the complete HBR article.

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