Boundary data, also known as edge data, is data that resides outside of a traditional data silo or database, causing it to be a difficult source of insight. It is often found at the intersection of different systems, and it can arise from a variety of sources, such as user activities, device logs, IoT sensors, or code-level error messages. Boundary data is typically “big” data due to the frequency or volume of the data points being collected, and can be especially valuable when used to analyze the behavior of customers and predict future outcomes.

Data collection and storage of boundary data can be challenging due to its wide variety and sources – structured, semi-structured, and unstructured types all living outside of traditional databases. Its quantity and speed can make processing and ensuring consistent accuracy a daunting task. Additionally, when boundary data is collected or transmitted from external systems or devices with questionable security, it is paramount that proper security measures are employed and data is handled with the utmost care to avoid breaches.

Boundary data is distinct from transaction data, which is located within a single system and often represents a single event. By contrast, boundary data typically represents dozens of events across multiple systems that may be related. This makes analyzing it a unique challenge as more granular insight is required in order to make meaningful observations.

Organizations can gain substantial insights from their boundary data if analyzed effectively. Doing so can enable companies to not only gain predictive insights on customers, but also to inform decisions on customer service, product innovation, resource forecast, and security and risk management.

Despite its complexity, big data solutions have made critical applications of boundary data more feasible. Through predictive analytics and machine learning, it can be used to address challenging questions and opportunities for improvement and innovation. Data engineers, IT professionals, and data scientists all play an important role in harnessing the power of boundary data.

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