Increasing access to alternative or “big data” sources has given rise to an explosion in the use of these data in economics-based research. However, in our enthusiasm to use the newest and greatest data, we as researchers may jump to use big data sources before thoroughly considering the costs and benefits of a particular dataset. This article highlights four practical issues that researchers should consider before working with a given source of big data. First, big data may not be conceptually different from traditional data. Second, big data may only be available for a limited sample of individuals, especially when aggregated to the unit of interest. Third, the sheer volume of data coupled with high levels of noise can make big data costly to process while still producing measures with low construct validity. Last, papers using big data may focus on the novelty of the data at the expense of the research question. I urge researchers, in particular PhD students, to carefully consider these issues before investing time and resources into acquiring and using big data.

We show that managers have a propensity to disproportionately report total revenues just above base‐ten thresholds (e.g., 10 million, 30 million, 1 billion) and examine motives for and consequences of this behavior. Focusing on base‐ten thresholds in revenues is important because, despite being unusually prevalent in revenue targets set in executive compensation contracts, analyst forecasts, and management forecasts, they have not been previously explored. We also show that pressure to beat these targets provides one explanation for the base‐ten bias in reported revenues. However, these incentive effects do not offer a complete explanation because base‐ten threshold‐beating is observed even in the absence of these explicit targets. We further find that when firms beat a base‐ten threshold for the first time, they experience increases in news coverage, institutional ownership, liquidity, and analyst following, even after controlling for whether they have beaten other common benchmarks. These results suggest that managers also beat base‐ten thresholds in order to increase their firms' overall visibility. Overall, we show that a preference for base‐ten numbers, which have no inherent economic meaning, has a measurable effect on the actions of market participants. These results open the door to a new range of managerial targets previously unexplored.