The exponential growth of IT systems has given researchers, governments, corporations and fund managers the ability to identify correlations and patterns from a combination of previously unlinked data sets with incredible speed. “Big data” often refers to the use of predictive analytics, which extract value from these data sets. Raw data can be collected from a variety of sources, including user interactions on the internet, satellite images, consumer transactions and industry trends. Although only a small minority of fund managers comprehensively capture value from this data, spending on big data continues to increase with fundamental-driven investors seeking to enter the environment. Building an internal infrastructure to acquire and process raw data is a time-consuming and expensive undertaking. As a result, most fund managers look to third-party data vendors in an effort to not only generate alpha, but to respond to new regulatory requirements; reduce costs; and assist with other operational and managerial functions. The first article in this three-part series explores the big-data landscape and how fund managers can acquire and use big data. The second article analyzes issues and best practices surrounding the acquisition of material nonpublic information; web scraping; and the quality and testability of data. The third article discusses risks associated with data privacy, the acquisition of data from third parties and the use of drones, as well as ways fund managers can mitigate those risks. For more on big data, see “How the GDPR Will Affect Private Funds’ Use of Alternative Data” (Jun. 14, 2018); and “Tips and Warnings for Navigating the Big Data Minefield” (Jul. 13, 2017).