The Internet of Things (IoT) is transforming how data is collected and used in sectors like logistics, manufacturing, and smart homes. Its main promise is the ability to generate real-time, granular data that can improve decision-making and boost operational efficiency. For instance, logistics firms such as DHL use IoT sensors on delivery vehicles to monitor location, fuel usage, and temperature, helping to streamline scheduling and reduce spoilage in temperature-sensitive shipments (McKinsey, 2015). The basic rationale is that more data leads to more value, but this value is only realized if the data is effectively managed and interpreted (Huxley et al., 2020).
Despite these opportunities, IoT also brings notable challenges and risks. One of the biggest is the scale and complexity of the data produced. IoT devices generate vast amounts of information in various formats, from JSON to XML, which makes integration and analysis difficult. This data is often inconsistent, noisy, or incomplete. Hashem et al. (2015) point out that advanced wrangling and cleaning methods are now essential for organizations that want to use IoT data for analytics and business intelligence. Without strong processes in place, the potential insights from IoT data may be lost or even misleading.
Security and privacy issues are also significant concerns. Many IoT devices lack strong security features, with some shipped using default credentials or without proper encryption. Such vulnerabilities have led to real-world incidents, including the hacking of consumer devices (Zhou et al., 2019). While architectures like Lambda and Kappa can handle high-volume, streaming data efficiently (Huxley et al., 2020), they do not solve the basic risks around device security, privacy, or the challenge of controlling access to sensitive personal or business data.
Overall, IoT creates new opportunities for real-time insight and improved operations but also introduces major data management, security, and privacy challenges. The benefits of IoT can only be fully realized by organizations that invest in robust cleaning, integration, and protection of the data they collect.
References
Hashem, I.A.T., Yaqoob, I., Anuar, N.B., Mokhtar, S., Gani, A. and Khan, S.U., 2015. The rise of “big data” on cloud computing: Review and open research issues. Information Systems, 47. https://doi.org/10.1016/j.is.2014.07.006
Huxley, J., et al., 2020. Big data architectures. [online] Microsoft Docs. Available at: https://learn.microsoft.com/en-us/azure/architecture/data-guide/big-data/
McKinsey Global Institute, 2015. The Internet of Things: Mapping the value beyond the hype. [online] Available at: https://www.mckinsey.com/industries/technology-media-and-telecommunications/our-insights/the-internet-of-things
Zhou, J., Zhang, R., Liu, C. and Dong, X., 2019. The effect of IoT new features on security and privacy: New threats, existing solutions, and challenges yet to be solved. IEEE Internet of Things Journal, 6(2) https://ieeexplore.ieee.org/document/8543246