Elevating ROI: How Superior Data Quality Drives IoT Success

Global technology spending on the Internet of Things (IoT) is projected to reach $1.2 trillion (€1 trillion) in 2022, with discrete manufacturing ($119 billion, €108 billion), process manufacturing ($78 billion, €70.8 billion), transportation ($71 billion, €64.5 billion) and utilities ($61 billion, €55.4 billion) leading the way.

The Industry 4.0 market is poised for substantial growth, and by the end of the decade, more than 60% of manufacturers are expected to be fully connected, leveraging technologies such as RFID, wearables and automation, notes Ramya Ravichandar, VP Products at FogHorn.

Despite optimistic projections, IoT and IIoT initiatives still face hurdles that can erode customer trust and stall the transition from pilot to full‑scale deployment. While connectivity limits, security concerns and data bias are often cited, the quality of the data itself is a decisive factor in project success.

What is Data Quality—and Why It Matters for IoT

Data quality is the cornerstone of effective IoT implementation, influencing outcomes in three key ways:

1. Only accurate, relevant data can inform sound, data‑driven decisions.
2. Low‑quality data renders analytics and machine‑learning models ineffective, leading to costly errors and poor ROI.
3. The resurgence of AI and ML has amplified the classic “garbage‑in, garbage‑out” problem.

High‑quality data feeds train and refine ML models, empowering factories to make evidence‑based decisions. For example, predictive maintenance models built on clean datasets can detect impending steam‑turbine failures before they cause catastrophic downtime, protecting both power plants and the broader grid.

Conversely, dirty data—missing, incomplete or erroneous—forces organizations into time‑consuming, expensive mistakes. TDWI estimates that U.S. companies lose roughly $600 billion (€545 billion) annually to data quality issues. Data scientists spend about 80% of their time on data preparation to ensure models deliver actionable insights.

Future‑ready enterprises must adopt robust processes that guarantee completeness, validity, consistency and correctness across all data streams, thereby enhancing insight quality, driving successful IoT rollouts, and unlocking maximum ROI.

Edge Computing’s Role in Enhancing Data Quality

Industrial sensors generate vast volumes of varied data—video, audio, vibration, acoustic, and more. Aligning, cleaning, enriching and fusing these streams is essential to deliver a comprehensive view of operations. Edge computing excels in this environment by capturing and processing data in real time, structuring it for immediate value extraction.

Edge‑enabled devices locally cleanse and format raw data, improving the training and deployment of precise ML models. Industry research predicts that by 2025, 59% of IoT deployments will incorporate edge processing.

For instance, factories can use edge analytics to monitor sensor outputs in real time, flag values outside predefined thresholds, and automatically halt production of defective units—thereby boosting product quality and reducing downtime.

Edge solutions transform raw, low‑quality streams into actionable, high‑quality insights that operations managers can use to maximize yield, machine utilisation and overall efficiency.

Organizations are increasingly recognising that edge computing not only delivers real‑time analytics but also elevates data quality by cleansing and enriching information at its source, leading to better operational outcomes.

Author: Ramya Ravichandar, VP Products, FogHorn