A recent article in Harvard Business Review (HBR) began with the statement, “If companies want to avoid flying blind, they have to master data analytics, and increasingly, this requires tapping into data from outside an organization’s four walls.” It’s a belief that the APEX also espouses. In the age of the Internet of Things (IoT), artificial intelligence (AI), and machine learning, analyzing and operationalizing data from external sources is a competitive differentiator that will soon become an expected part of enterprise data strategies.

A key driver behind the move to incorporate third-party data is the fact that organizations are increasingly operating as a network composed of suppliers, resellers, channel partners, regulators, and other stakeholders. As the HBR article put it, “Analyzing external data can help companies see the risks and opportunities that they would miss with inputs limited to data generated from internal operations, customers, and first-tier suppliers…. [and also] illuminate how factors such as shifting consumer behaviors, competitor initiatives or geopolitical events can affect a business.”

An example of this is Uber’s Movement Speeds feature, a recently launched module that delivers traffic speed data by street and hourly. The anonymized data alone is helpful for analyzing urban roadway congestion and traffic patterns and can help cities prioritize projects for addressing these issues. But the feature’s greatest potential is unleashed when it is combined with data from other sources. As VentureBeat’s Kyle Wiggers wrote, “A metro might use a cyclist-pedestrian data set to identify corridors where high-speed bike accidents occur, for example, to justify investments in protected cyclist infrastructure.”

Social media data is another great example of the power of tapping into external information sources. Versed, a new skincare collection was created after Clique Brands’ founder turned to its online community of 16 million users to identify pain points and opportunities in the beauty industry. A key finding was that women were eager for non-toxic products that addressed some relatively consistent concerns at affordable prices. As a result of these insights, Versed is now available online and in all Target’s retail locations in the US.

These are just a few examples of how analytics can generate value from external data sets. The HBR piece outlines many other interesting use cases, including using geolocation and weather data in the agricultural sector to predict crop yields and companies that monitor data from LinkedIn and other networking platforms to predict job-seeking behaviors and patterns.

As data generated by consumers become more valuable to companies, it raises an interesting question: should people be monetizing their own data? Agnes Budzyn, managing director of global growth at Consensys AG, explored this theme in a recent World Economic Forum opinion piece. Arguing that blockchain and other emerging technologies allow us to “gradually take back control of our data,” she believes people will begin to view the data they generate online as an asset and, in so doing, be “a step closer to an equitable and empowering relationship with the internet.”

Whether her prediction comes true and how remains to be seen, but we can certainly expect to see third-party information becoming an increasingly vital component of big data analytics strategies. Really, if social insights can power entirely new products and categories, is it so hard to believe that people might want a piece of the data action?