Turning IoT Data into Business Value: A Practical Guide

Adam Mayer, senior manager at Qlik, reflects on the evolving conversation around the Internet of Everything (IoE) and its roots in the Internet of Things (IoT).

When new technology arrives, the instinct is “more, more, more.” Yet adding sensors to every light, door, or toilet without a clear strategy can lead to data overload without real ROI. This mirrors the early days of Big Data, where volume did not automatically equate to insight. The key to unlocking IoT’s value lies in how that data is explored, visualised, and turned into actionable decisions.

The Breathe London Project, run by C40 Cities in partnership with the Greater London Authority, illustrates this principle. A network of 100 sensor pods mounted on lampposts and buildings, coupled with mobile sensors on Google Street View cars, captured air‑quality readings across London in real time. While the dataset is compelling, the true benefit emerges when policymakers use it to target pollution hotspots.

Barriers to Analyzing IoT Data

Integrating IoT data is challenging for many organisations. Qlik’s research with IDC shows that 37% of companies cite difficulty in converting disparate data into standard analytics formats as a major hurdle. The IoT layer amplifies this issue by introducing a proliferation of sources, often in unstructured formats that require transformation before analysis.

Second, the sheer volume and velocity of sensor outputs can overwhelm traditional pipelines. Continuous readings generate data at a rate that exceeds the capacity of many visualisation tools, meaning insights arrive only after a delay. When the lag between data generation and decision‑making is significant, organisations miss opportunities for real‑time intervention.

Keeping Pace with Data Velocity

To harness IoT effectively, businesses need a data supply chain that ingests and transforms information swiftly. Classic Extract, Transform, Load (ETL) approaches are too slow and labor‑intensive, especially when 31% of global organisations struggle to find skilled staff for data processing.

Change Data Capture (CDC) offers a practical alternative. By continuously replicating incremental updates, CDC delivers data to warehouses or lakes in near‑real time, boosting throughput without demanding extensive coding. This capability enables downstream analytics platforms to visualise up‑to‑date information and surface actionable insights.

Moreover, the next generation of business intelligence tools will embed proactive alerts and cognitive engines—termed Active Intelligence—to not only inform users of emerging trends but also recommend concrete actions, accelerating decision cycles.

A Data Pipeline That Delivers IoT’s Promise

Companies must avoid the trap of chasing raw data for its own sake. Successful IoT implementations hinge on a pipeline that prioritises transformation and analysis, allowing organisations to learn, act, and react in real time. When the entire data ecosystem—from ingestion to insight—moves at the speed of the business, IoT can transition from a collection of sensors to a strategic asset that drives measurable outcomes.

Author: Adam Mayer, senior manager at Qlik.