Getting a good forecast and understanding of the situation is crucial for any scenario, but it is especially important to players in the investment industry. Being able to get an early indication of how a particular retailer’s sales are doing can give an investor a leg up on whether to buy or sell that retailer’s stock even before the earnings reports are released. The problem of forecasting economic activity or microclimates based on a variety of data beyond the usual retail data is a very recent phenomenon and has led to another buzzword—“alternative data.” A major mix in this alternative data category is satellite imagery, but it also includes other data such as social media, government filings, job postings, traffic patterns, changes in parking lots or open spaces detected by satellite images, mobile phone usage patterns in any given location at any given time, search patterns on search engines, and so on. Facebook and other companies have invested in satellites to try to image the whole globe every day so that daily changes can be tracked at any location and the information can be used for forecasting. Many interesting examples of more reliable and advanced forecasts have been reported. Indeed, this activity is being led by start-up companies. Tartar (2018) cited several examples.
Here are some of the examples identified by them and many other proponents of alternative data:
• RS Metrics monitored parking lots across the United States for various hedge funds. In 2015, based on an analysis of the parking lots, RS Metrics predicted a strong second quarter in 2015 for JC Penney. Its clients (mostly hedge funds) profited from this advanced insight. A similar story has been reported for Wal-Mart using car counts in its parking lots to forecast sales.
• Spaceknow keeps track of changes in factory surroundings for over 6,000 Chinese factory sites. Using this data, the company has been able to provide a better idea of China's industrial economic activity than what the Chinese government has been reporting.
• Telluslabs, Inc. compiles data from NASA and European satellites to build prediction models for various crops such as corn, rice, soybean, wheat, and so on. Besides the images from the satellites, they incorporate measurements of thermal infrared bands, which help measure radiating heat to predict health of the crops.
• DigitalGlobe is able to analyze the size of a forest with more accuracy because its software can count every single tree in a forest. This results in a more accurate estimate because there is no need to use a representative sample.
These examples illustrate just a sample of ways data can be combined to generate new insights. Of course, there are privacy concerns in some cases.
For example, Yodlee, a division of Envestnet, provides personal finance tools to many large banks as well as personal financial tools to individuals. Thus, it has access to massive information about individuals.
It has faced concerns about the privacy and security of this information, especially in light of the major breaches reported by Facebook, Cambridge Analytics, and Equifax. Although such concerns will eventually be resolved by policy makers or the market, what is clear is that new and interesting ways of combining satellite data and many other data sources are spawning a new crop of analytics companies.
All of these organizations are working with data that meets the three V's-variety, volume, and velocity characterizations. Some of these companies also work with another category of data-sensors.
But this group of companies certainly also falls under a group of innovative and emerging applications.

Questions for Discussion
1. What is a common thread in the examples discussed in this application case?
2. Can you think of other data streams that might help give an early indication of sales at a retailer?
3. Can you think of other applications along the lines presented in this application case?