How IoT and Cloud Computing Shape the Future of Enterprise Data

By 2022, an estimated 29 billion connected devices are expected to be in operation—and projections show that by 2025, the global IoT landscape will swell to over 75 billion devices. For forward‑thinking enterprises, this surge translates into a goldmine of data that can unlock powerful insights. As Shivnath Babu, Chief Technology Officer at Unravel Data, explains, the volume and velocity of IoT‑generated data will only grow, demanding robust cloud‑based solutions to capture, process, and act on that information.

Challenges of Managing IoT Data

IoT devices emit a diverse array of data—ranging from customer sales figures and GPS coordinates to environmental metrics like humidity, temperature, and air quality. Managing this heterogeneity and sheer volume is increasingly complex, often leading to sluggish, inefficient data pipelines, especially for real‑time, app‑driven services.

To meet these demands, modern enterprises are turning to personalized, real‑time streaming platforms such as Kafka, Spark, Kudu, Flink, and HBase. While these tools excel at handling big data workloads, they also introduce significant operational overhead: multi‑layer monitoring, cross‑system orchestration (e.g., Spark with YARN, HDFS/S3, Kafka/Flink), and repetitive preprocessing steps that multiply complexity.

Scaling IoT Growth with AI and ML

Current data‑management frameworks simply cannot keep pace with the explosive expansion of IoT devices. Many organizations are now integrating AI and machine‑learning (ML) into their data operations to automate insights, streamline data routing, and maintain real‑time performance.

ML algorithms can continuously audit application execution, pinpoint failure causes, and recommend performance optimizations—reducing operational costs while boosting reliability. When a streaming pipeline lags, AI‑driven diagnostics can quickly isolate bottlenecks, delivering the predictability and resilience enterprises need.

Tailor Solutions to Specific Use Cases

Successful IoT deployments hinge on a deep understanding of the unique challenges each use case presents. By mapping the specific environment and pain points, IT teams can accelerate the adoption of AI‑augmented data solutions, ensuring that automation keeps pace with the complexity of modern IoT ecosystems.

Author: Shivnath Babu, Chief Technology Officer, Unravel Data.