External data can be valuable to many organizations for a variety of reasons. It can be used by planning and operations teams to benchmark an organization’s financial or operational performance. It can enrich the data an organization collects and analyzes about its customers, prospects and partners. It can augment machine learning analyses to produce more accurate models. For the CPG and retail sectors, demographic data is essential to understanding customers’ buying behaviors. In financial services, market data and economic indicators are essential to formulating financial strategies. For insurers, weather and geospatial data can be essential to minimizing claims from damaging storms.
Our research shows that more than three-quarters (77%) of participants consider external data to be an important part of their machine learning efforts. The most important external data source identified is social media, followed by demographic data from data brokers. Organizations also identified government data, market data, environmental data and location data as important external data sources. External data is not just part of machine learning analyses though. Our research shows that external data sources are also a routine part of data preparation processes with 80% of organizations incorporating one or more external data source. And a similar proportion of participants in our research (84%) include external data in their data lakes.
