Flood of data on the Internet of Things: How to secure your IoT information!

Flood of data on the Internet of Things: How to secure your IoT information!

In the world of the Internet of Things (IoT), data management is not a mere buzzword, but a core competence that decides whether companies retain the connection in the digital age. Today, on July 18, 2025, it is clear that IoT systems not only generate enormous amounts of data, but also place special requirements for the management of this data. According to techtarget it can be seen that even IoT projects with low data volume can provide decisive information that is important for the performance and safety of a company.

The focus of IoT data management is the responsibility for information, which ensures that data is stored, managed and protected in accordance with specified guidelines. Various disciplines are important-from quality assurance of the data to information lifecycle to compliance management. These aspects must be carefully coordinated in order to master the challenges of the IoT, such as the large number of systems and teams involved.

data quality in the IoT: a key to success

A frequently overlooked topic is the data quality (DQ) in the IoT, as in an article on PMC Data from IoT sensors come in different forms and requirements, be it structured or unstructured. In addition, the global amount of data from networked devices should increase to impressive 79.4 Zettabytes by 2025. This means that companies are faced with the challenge of managing this data in such a way that it not only records it, but also converted into valuable knowledge.

The quality dimensions such as accuracy, completeness and consistency are crucial to ensure that the data obtained is actually useful and do not result in any wrong decisions. Several research work, for example by Lee et al., Have identified the most common causes of DQ problems, including the complexity of several data sources, which should not be underestimated in the dynamic world of the IoT.

The challenges of integration

companies are often confronted with a variety of isolated solutions, as described in a contribution to Detecon The proof-of-concept phase is often the first step, which, however, does not take into account the integration across various IoT applications. Therefore, companies should develop a clear strategy in this early phase to design the architecture of their IoT solutions in such a way that redundancies can be avoided and synergies can be used.

The introduction of an "Architecture Thinking" can help to overcome these hurdles. This means seeing the end benefit and the necessary skills as an independent architecture and concentrating on the entire value chain. The challenges are enormous, especially when implementing. IoT skills are fundamentally different from traditional corporate skills and require a rethink in the operating model. The difference between classic and digital customer service is clearly visible here: automated systems require continuous communication with customers and react to their needs in real time.