With the Internet of Things (IoT) integrated into Enterprise Resource Planning (ERP) systems, the complexity and volume of data generated can become unmanageable for software designers and database managers. IoT devices continuously collect and transmit data, leading to an exponential increase in the amount of information that organizations must process, store, and analyze.
As new devices are added to the network, they collect a range of data types—sensor readings, device logs, user interactions, etc. This influx not only increases the data volume but also adds variety, making it harder to manage and utilize effectively within an ERP framework. Traditional data management techniques may struggle to keep up with the real-time demands and scale required for handling such extensive and dynamic datasets.
In contrast, while transactions and storage may also present challenges, they do not encompass the broader issue of data complexity as effectively. Transactions are specific actions processed within the system, and while an increase in data can lead to more transactions, it’s the sheer volume and variety of data that primarily overwhelms management practices. Storage can also be scaled to accommodate more data, but the complexity of organizing, analyzing, and deriving actionable insights from this data flood remains a significant concern. Thus, the core issue highlighted here revolves around the unmanageable