How Open Data Standards Power Industrial Process Optimization

How Open Data Standards Power Industrial Process Optimization | SPONSORED

Data is more than a byproduct in today’s industrial landscape—it’s the backbone of smart operations.  From sensors and Programmable Logic Controllers (PLCs) to building management systems (BMS) and cloud platforms, manufacturers generate an enormous amount of data every second. But data alone doesn’t drive performance. The real value lies in how that data is structured, shared, stored, and analyzed in real-time.

That’s where open data standards come in. Open standards are vendor-neutral frameworks that define how industrial data  is formatted, accessed, and exchanged. Open data standards facilitate communication between  different tools and software created by different manufacturers (MQTT, OPC-UA, analytics tools, databases, etc.).  They help disparate systems speak the same language, making it easier and more secure to connect legacy infrastructure with cutting-edge technology.

Challenges in industrial process optimization

As Industry 4.0 continues to reshape manufacturing, the promise is clear: smarter factories, faster insights, and data-driven everything. While Industry 3.0 introduced automation through computers and programmable logic controllers (PLCs), Industry 4.0 goes a step further by integrating digital technologies, real-time data exchange, and connected systems to create intelligent, adaptive production environments. But turning that potential into progress isn’t always easy.

Legacy Systems and Modern Data Requirements

Let’s say a manager at a mid-sized automotive plant wants to eliminate downtime by predicting equipment failures before they happen. They start this process by installing IIoT sensors on stamping machines to monitor vibration and temperature.  Collecting data is the first piece—but there’s a problem. The vibration and temperature data is stored in a legacy Manufacturing Execution System (MES) built before IIoT and AI were common, so it only allows batch exports in outdated formats. These formats were designed for periodic reports, not real-time use, so the data can’t be accessed quickly or read by modern tools, leaving it stuck in the system. Without real-time access or modern integration options, the data is inaccessible to cutting-edge analytical tools like machine learning and AI. The result? Missed opportunities and continued reliance on reactive maintenance.

Data Silos

Manufacturing environments rely on a mix of SCADA systems, PLCs, and IoT devices—each producing data in different formats. Without a shared standard, this data remains siloed and fragmented, limiting visibility across operations. When systems can’t talk to each other, teams are forced to make decisions with incomplete information, slowing down problem-solving and making it harder to optimize performance.

Limited Real-Time Insight

Factory operators depend on real-time data to stop minor issues from becoming major disruptions.  When data updates at a pace slower than real-time, operators must make decisions based on outdated information, which can cause delays, costly mistakes, and even safety risks.  Think of a food and beverage manufacturer relying on a control system that only updates every 15 minutes. If a filler head breaks, thousands of underfilled bottles go out the door before anyone notices. Now, the manufacturer is facing wasted product, regulatory fines, damaged customer trust, and costly product recalls.

Locked Into One Vendor

Many manufacturers use proprietary systems that only work with one vendor, leading to vendor lock-in. This makes it costly and complicated to switch tools or adopt new technologies. Even minor upgrades can require major effort when data formats are incompatible, reducing flexibility and slowing innovation. As a result, manufacturers face higher costs and risk falling behind more agile competitors who can quickly adapt and adopt new technologies.

Inconsistent Data Quality and Formats

Machines, sensors, and software often produce data in different formats, like JSON, CSV, or XML. Without a shared data standard, merging this information into a usable format becomes a time-consuming task that demands significant resources. This manual cleanup process not only slows analysis but also increases the risk of errors, making it difficult for engineers to act quickly when issues arise. As a result, manufacturers face delays in decision-making, which hampers their ability to respond to market demands or equipment failures in real-time. Inconsistent data formats lead to disconnected systems, creating further bottlenecks in decision-making and maintenance processes. Over time, this drives up operational costs, reduces efficiency, and leaves manufacturers vulnerable to costly downtime.

What Are Open Data Standards?

Open data standards are vendor-neutral guidelines for structuring and exchanging industrial data, ensuring openness and interoperability across a wide range of devices, systems, and platforms.  Unlike proprietary formats that lock data into specific tools, open standards make it easier to connect legacy infrastructure with new technologies, such as cloud analytics, AI-driven applications, and real-time monitoring tools.

Benefits of Open Data Standards:

• Seamless integration: Standardized formats make it easier to unify data from sensors, PLCs, and SCADA systems.
• Streamlined processing: When systems share a common data format, there’s less need for manual cleanup or conversion. Teams can focus on improving operations by reducing downtime, boosting throughput, and maintaining high-quality outputs, which supports better Overall Equipment Effectiveness (OEE).
• Vendor flexibility: Open formats help avoid lock-in, making adopting new tools and technologies easier.

 

Technologies that use open standards

• MQTT: A lightweight protocol ideal for IIoT data transmission, particularly in low-bandwidth environments.
• OPC UA: A widely used open standard for secure, reliable machine-to-machine communication.
• Apache Parquet: A columnar storage format designed for efficient data compression and fast analytics.
• Apache Arrow: An in-memory data format built for high-speed processing and cross-platform compatibility.

How open standards improve process optimization

Formats like Apache Arrow and Parquet unify data from PLCs, SCADA systems, and IoT sensors into a single, consistent structure, simplifying diagnostics, trend analysis, and cross-system insights without the need to switch between platforms. By organizing data in a structured, query-efficient way, these formats reduce the need for manual intervention, improving data quality and minimizing duplication, inconsistency, and missing data. This allows teams to access reliable information quickly and focus on optimization rather than troubleshooting messy datasets.

Using Arrow Flight, data can move between systems at high speeds, enabling real-time dashboards, alerts, and automated actions like rerouting production or triggering predictive maintenance. This rapid data transfer allows manufacturers to make timely decisions and proactively address issues before they escalate, reducing downtime and increasing efficiency. Open standards also eliminate the need for hardcoded integrations or manual CSV exports, which reduces engineering overhead and frees up resources for optimization efforts.

Moreover, open formats like Arrow and Parquet ensure that your tech stack remains future-proof by enabling seamless integration with new systems—from data historians to AI platforms—without locking you into a single vendor. This flexibility allows manufacturers to adopt emerging technologies like predictive maintenance, ensuring they stay ahead of the curve and continue to innovate as new tools and systems are developed.

InfluxDB 3: Built for Open Standards

InfluxDB 3 is a time series database designed to meet the demands of modern industrial operations. It brings open data standards to life with high-speed performance, scale, and interoperability.

Open Data Compatibility

InfluxDB 3 was built using open data formats such as Apache Arrow and Parquet to enable smooth, efficient communication between systems like data lakes, analytics platforms, and machine learning pipelines. Its standardized file formats allow for quick ingestion and sharing of time-series data, eliminating delays and interruptions caused by format conversions.  This ensures that clean, consistent data flows easily between dashboards, cloud services, and industrial tools, helping teams access real-time insights and accelerate data-driven decision-making.

Real-Time Insights

To enable high-speed, low-latency data streaming, InfluxDB 3 uses Arrow Flight, a high-performance transport layer built on Apache Arrow. This allows the platform to ingest and query data as it’s generated, making it ideal for time-sensitive applications like monitoring, alerting, and live analytics. With fast, low-latency access to real-time data, teams can detect anomalies sooner, respond faster to changes on the floor, and improve overall operational efficiency.

Scalable Storage

To manage large volumes of time series data, InfluxDB 3 leverages columnar storage formats like Apache Parquet. This structure compresses data efficiently and allows for fast, scalable querying, even as datasets grow into billions of points. The result is consistent high performance and faster access to insights, enabling engineers to make informed decisions without delay.

Seamless Integration

InfluxDB 3 is built to share data easily with other platforms. Its support for open standards means you’re not tied to a specific vendor. It integrates with IIoT devices, cloud platforms, and analytics tools without requiring custom connectors. This makes it easier to evolve your data infrastructure as your needs change, whether that means adding machine learning, scaling to new facilities, or building more advanced automation pipelines.

The verdict 

Modern industrial environments are evolving to meet the demands of real-time decision-making, improved efficiency, and better data access. The technology to support that evolution is already here.

With support for open data standards like Apache Arrow, Apache Parquet, and OPC UA, InfluxDB 3 makes it easier to connect, process, and analyze industrial data at scale. It enables teams to unify data from multiple sources, monitor systems in real-time, and take action faster.

Ready to learn more? Contact the InfluxData team or get started with a free download of  InfluxDB 3 Core OSS or Enterprise. 

Sponsored by InfluxData