Introducing the Time Series Buying Guide for IIoT

By Jessica Wachtel / Use Cases
Jan 29, 2025

All machinery and equipment, including their controls and sensors, tell a story through the data they collect. This data, or Industrial Internet of Things (IIoT) data, provides a detailed narrative about the machines, offering actionable insights to improve operations. IIoT data empowers businesses to optimize and enhance industrial processes by detailing operational status, performance metrics, usage patterns, health diagnostics, and environmental conditions.

The value of leveraging IIoT data

Harnessing IIoT data can significantly improve operational and business efficiency. When fully utilized, it transforms raw information into tangible benefits, such as:

• Real-time anomaly detection and intervention
• Maximized productivity, minimized waste and downtime
• Reduced unplanned outages and improved maintenance forecasting

Extracting the full value from machines, equipment, industrial controls, and sensor data drives increased revenue by reducing outages, optimizing processes, and lowering error rates. However, achieving these benefits requires tools that preserve and enhance the quality of datasets, ensuring they remain actionable and accurate.

The challenges of managing IIoT data

IIoT data captures changes over time, from subtle fluctuations to catastrophic shifts. This temporal context makes it “time series” data—a sequence of data points collected or recorded at regular intervals. Managing time series data poses unique challenges due to its scale, speed, and complexity:

• Massive scale: Continuous high-speed, high-volume data streams
• Real-time action: The need for immediate analysis and response within data streams
• Data cardinality: High numbers of tags collected result in high cardinality, which can strain system performance

Businesses can easily generate billions of IIoT time series data points per second, demanding ingestion and storage solutions that keep pace with real-time analytics.

Why high-fidelity data matters

Successfully managing these challenges results in high-fidelity data—complete and accurate datasets that provide precise insights. High-fidelity data enables businesses to detect issues early, build accurate analytics, and create reliable models.

Conversely, failing to address these challenges results in incomplete datasets, which lack the necessary precision to capture early-stage anomalies or build actionable insights.

Safeguard and maximize value from your data

“Time Series Buying Guide for IIoT” explores how manufacturers can identify the tools and architectural practices necessary to achieve high-fidelity data sets. Readers will gain insight into the unique characteristics of IIoT time series data compared to other data types. The guide highlights which tools can maximize the value of IIoT data and warns against those that may appear beneficial but prove detrimental over time. Additionally, it offers practical advice on seamlessly integrating specialized IIoT tools into existing architectures and environments, eliminating the need for costly, large-scale system overhauls.

Download the “Time Series Buying Guide for IIoT” now.