Early adopters of the Internet of Things (IoT) are leveraging the Enterprise of Things (EoT) to enhance business scalability. Industry leaders are embracing an innovation-first approach to explore the opportunities IoT presents and its positive impact on business outcomes. However, integrating IoT into IT infrastructure presents several challenges.
Key Challenges in Enterprise-Wide IoT Implementation:
Handling Large Volumes of Unstructured Data
One of the main obstacles in IoT adoption is managing the extensive amounts of unstructured data it generates. IoT systems require well-structured data with clearly defined categories for effective data organization. Without precise categorization, enterprises risk creating a chaotic data environment, making it difficult to derive actionable insights. Data scientists must analyze data considering its volume, type, and speed, and ensure that clean data is ingested into a data lakehouse for efficient analysis.
This challenge is compounded by manual data entry points, which are prone to human error, compromising the accuracy of machine-readable data. As a result, CIOs should prioritize data quantity, type, and speed when integrating IoT into their IT infrastructure. Hiring skilled data scientists or outsourcing data management can help enterprises model their data more effectively.
The Necessity for Real-Time Data Processing
Modern businesses require devices capable of processing data in real-time, even during connection losses, to support business scalability. The demand for real-time data analysis is another significant challenge when incorporating IoT into IT infrastructure.
Comments