A manufacturer's roadmap:

from machine data generation to successful machine analytics.

In today’s evolving industrial landscape, it isn’t enough to simply maintain a fleet of performing machines. Manufacturers should embrace a digital transformation which includes the use of machine data. With key concepts such as Industry 4.0, industrial IoT (Internet of Things), and real-time analytics, measuring machine data is not just simply an option, it’s necessary for staying competitive in the market.  

Our latest roadmap, from machine data generation to successful machine analytics, offers manufacturers a  roadmap. It covers the reasons why manufacturers should measure machine data, how they should measure machine data and more.

What you’ll learn from the white paper:

In this white paper we’ll discuss the complete roadmap for manufacturers to go from the measuring of machine data to successful machine analytics with the help of dashboarding. The following 6 questions will be answered:

1. Why measure machine data as a manufacturer?

In this section, we give 6 reasons why manufacturers should measure machine data. By capturing (real-time) machine data, manufacturers can optimize machine uptime, reduce downtime, and streamline production cycles while detecting early signs of issues—like abnormal vibrations, temperature fluctuations, or energy spikes. Measuring machine data not only supports product development but also boosts potential cost efficiency and unlocks new revenue streams through value-added services such as remote monitoring, maintenance contracts, and performance-based guarantees.

2. Which machine data metrics to measure as a manufacturer?

In this section we outline the key metrics that manufacturers should measure and collect from their machine data. These metrics include performance parameters such as vibration, temperature, energy consumption, and production throughput. We also explain and emphasize the importance of monitoring both direct machine metrics (like runtime hours and energy usage) and indirect indicators (such as environmental conditions) to develop a comprehensive understanding.

3. How to measure machine data as a manufacturer?

In this section, we describe the importance and various measurement techniques to accurately capture machine data. This section talks about the importance of choosing the appropriate sensors, calibrating them regularly, implementing edge devices to pre-process data, and employing standard protocols (MQTT, OPC UA) for secure, timely transmission with event-based triggers.

4. How to Store Machine Generated Data as a manufacturer?

In this fourth section, we address the challenge of safely and efficiently storing captured machine data. We explore various storage solutions, including time-series databases for high-frequency sensor data, relational or NoSQL databases for structured information, and data lakes for heterogeneous sources. We discuss how to choose the optimal storage option based on scalability, performance, and retention needs, and emphasize the critical importance of security and compliance measures.

5. How to visualize machine data?

This section explains how visualizations transform raw machine data into actionable insights. Manufacturers can leverage specialized tools like Grafana, Power BI, or Tableau to develop dashboards. We discuss various visualization techniques—charts, heatmaps, scatter plots, and geospatial maps—and provide concrete examples of when to use each method, ensuring that complex data is presented clearly to drive informed decision-making.

6. How to Ensure Machine Analytics Dashboards Are Actively Used?

The last section talks about the true effect of machine data dashboards, when they are integrated into daily decision-making. It emphasizes aligning dashboards with user needs by centering design around their challenges. Comprehensive training, continuous feedback collection, and monitoring usage analytics are crucial to ensure that dashboards consistently drive proactive maintenance, enhance efficiency, and support strategic actions across the organization.

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    StriData's tools ensure seamless connectivity for 1500+ machines. This empowers machine builders to enhance overall equipment effectiveness (OEE), significantly reduce downtime, and instantly share machine data with their customers.

    Martijn van Dijk

    Founder & Data Engineer