Why External Data Needs to Be Part of Your Data and Analytics Strategy

Chapter 4. Getting Started with External Data Today

As the COVID-19 pandemic grew worse, the rapid changes in the global economy and seismic shifts in consumer behaviors rendered thousands of datasets and analytical models useless overnight. Organizations worked tirelessly to find and source more relevant public and third-party datasets.

For example, a retail organization leveraged external data sources to enhance its workforce-availability analysis and contingency planning. It analyzed epidemiological model predictions and location-specific information—such as whether employees would likely commute to work via city buses, passenger trains, or subways for each zip code where it operates—in conjunction with its internal workforce data on employee segments.

Regional managers were now empowered to more accurately anticipate when and where they’d need to adjust their workforce plans (for example, hire or move associates) and institute contingency measures, such as shortening store hours.

Some people may read this and think, “Sure, that’s fine during COVID, but COVID is an extreme anomaly. Do we still need so much access to external data once the pandemic ends?” The reality is, when you are trying to solve almost any analytical problem, there is value—and a significant cost—in adding more layers of external data sources.

For example, if you’re trying to predict volume sales for a store and the road the store is on was closed for two to three months, or if a competing shop opened ...