Data Integration: Why it is essential for predictive maintenance

The evolution of industrial maintenance increasingly depends on the ability to transform scattered information into actionable intelligence. In this context, data integration goes beyond being just a technical step and becomes a strategic pillar for predictive maintenance and asset management.

In this manner, by consolidating information from IoT sensors, supervisory systems (SCADA), ERPs, CMMS, and other sources, it is possible to create a unified view of operations. So, eliminating informational silos enables more accurate analyses and increases the reliability of technical decisions.

Moreover, data integration connects machines, processes, and teams, enabling everything from predictive diagnostics to automated work order generation. In practice, maintenance data is analyzed collectively, anticipating failures, optimizing intervention windows, and improving operational efficiency.

In this article, you will learn what data integration is, how it works in the industry, its main challenges, and how the Dynamox ecosystem applies this strategy to deliver real value to industrial operations.

What is data integration?

Data integration is the process of gathering and combining information from different sources so it can be analyzed and used collectively. In an industrial environment, this practice aims to transform fragmented data — originating from sensors, management systems, maintenance software, and equipment — into a single, coherent base capable of supporting advanced analyses and strategic decisions.

This approach is essential for creating a complete operational overview, allowing managers and industrial maintenance teams to visualize critical variables, identify patterns, and anticipate problems before they become failures.

Difference between connectivity, integration, and interoperability

Although often used as synonyms, these concepts have distinct meanings in the context of industrial data management:

• Connectivity: The ability of devices, systems, or sensors to exchange information with each other. It represents the first step but does not guarantee that data will be understood or used effectively.
• Integration: Goes beyond simple connection. This way, it involves combining data from different sources into a standardized format, organizing it in a central repository to facilitate analysis and decision-making.
• Interoperability: The most advanced level. It refers to the ability of different systems to work together automatically and efficiently, exchanging information in real time and executing coordinated actions without manual intervention.

Meaning, in practice, system connectivity creates the communication “channel,” integration organizes and unifies the data, and interoperability ensures that this exchange continuously and automatically generates value.

What are typical data sources?

Data integration in the industry relies on multiple sources of information. Each source captures a specific aspect of operations, and real value emerges when all are combined to generate more complete and reliable analyses. The main sources include:

• IoT Sensors: Devices such as Dynamox’s DynaLoggers collect essential variables like vibration, temperature, and electrical current.
• ERP Systems (Enterprise Resource Planning): Gather administrative and operational information, including purchase orders, inventory management, and production planning.
• SCADA Systems (Supervisory Control and Data Acquisition): Enable real-time monitoring and control of industrial processes, providing operational data from lines, systems, and plants.
• CMMS (Computerized Maintenance Management System): Centralize maintenance history, work orders, parts inventory, and intervention planning.

Individually, each source contributes to industrial management. However, integration among them provides a holistic view of operations, eliminating information gaps and increasing decision-making accuracy.

How does data collection, processing, and delivery occur?

ndustrial data integration is not a single process but a continuous flow that connects operational reality to the analytical environment. Thus, for information to be truly useful in predictive maintenance and asset management, three critical stages must be followed: accurate collection, careful processing, and structured delivery.

Each stage requires specific technologies, protocols, and best practices to ensure data integrity from origin to final application. Below are the details of each stage:

Collection

Data collection in the industry involves continuous or periodic capture of information from multiple sources, such as IoT sensors installed on rotating assets, SCADA systems, ERPs, CMMS, and manual operation records.

For example, Dynamox’s DynaLogger sensors monitor variables such as vibration and temperature, transmitting data via DynaGateway using protocols that ensure integrity and end-to-end encryption.

This stage also requires proper configuration of measurement points, device calibration, and sampling frequencies aligned with asset criticality, ensuring no relevant event is missed.

Processing

After collection, raw data undergoes filtering, cleaning, and standardization to eliminate noise, duplicates, and invalid readings. Temporal synchronization allows correlation of variables from different sources, while normalization converts heterogeneous formats into a unified data model.

In addition, pre-processing algorithms enrich information with metadata, such as asset criticality, failure history, and environmental conditions, preparing the base for advanced analysis and integration with other platforms.

Therefore, this stage is essential to ensure data reliability and interoperability, enabling consistent analyses in predictive maintenance and asset management.

Delivery

With processed and standardized data, structured delivery to systems and end-users begins. On the Dynamox Platform, information is centralized and made available through configurable dashboards, asset health panels, technical alerts, and historical reports.

Additionally, APIs and connectors facilitate integration with ERPs, CMMS, and supervisory systems, enabling automation of workflows such as work order creation or process adjustments.

That’s why this stage ensures that data reaches the right teams, at the right time, in a format ready to support technical decisions that improve efficiency and operational reliability.

What are the challenges of industrial data integration?

Data integration in the industry brings significant gains for predictive maintenance and asset management, but its implementation is not free from technical and operational barriers. When these challenges are not properly addressed, they compromise the quality of analyses, system interoperability, and even information security.

Below are the main challenges:

Incompatible formats and data heterogeneity

Different sources produce data in varied formats, with distinct scales, units, and structures. Additionally, sensors may generate readings in milliseconds, while management systems record events by the hour or day. Thus, without standardization, correlating variables becomes inaccurate, making cross-analysis difficult.

Legacy systems and low interoperability

Many industrial plants still operate with outdated systems that were not designed to connect to modern platforms or share data via APIs. Therefore, this lack of interoperability creates informational silos and requires specific solutions, such as connectors or middleware layers, to enable data exchange.

Lack of governance and poor data quality

Without clear governance processes, data may contain gaps, measurement errors, or duplicate records. Furthermore, the absence of validation and update policies reduces the reliability of analyses, directly affecting critical maintenance and operational decisions.

Information security and risk of leaks

Integration concentrates strategic operational data in digital environments, often in the cloud. In this manner, this requires robust security measures, including end-to-end encryption, role-based access control, and compliance with standards such as ISO 27001 and ISO 27701 to prevent leaks or unauthorized access.

Lack of standardization in communication protocols

The diversity of industrial protocols — such as Modbus, OPC UA, MQTT, and proprietary ones — makes information exchange between devices and systems challenging. The absence of a single standard means integration must accommodate multiple communication methods, increasing project complexity, and cost.

Therefore, when addressed strategically, these challenges cease to be barriers and become points of strength for the data ecosystem, ensuring integration is robust, secure, and scalable.

How does data integration drive predictive maintenance?

The effectiveness of a predictive maintenance strategy depends directly on the quality and scope of the information analyzed. When data remains isolated in different systems, the view of asset performance becomes fragmented, limiting the ability to anticipate failures and optimize interventions. This way, integration of platforms and data eliminates these barriers and transforms decision-making.

Key benefits of this approach include:

Elimination of informational silos and data unification

By consolidating data from multiple sources — such as IoT sensors, ERP production records, and CMMS work order histories — maintenance teams gain a complete, correlated view of asset conditions. This makes it possible to identify patterns that would be invisible if each dataset were analyzed separately.

Support for predictive diagnostics and technical planning

With centralized and standardized information, analytical tools cross operational and historical data to generate more accurate predictive diagnostics. This allows technical teams to plan interventions based on reliable trends and projections, prioritizing critical assets and optimizing resources.

Support for automation and operational intelligence

Integration creates the foundation for events detected by sensors to trigger automatic actions, such as opening work orders, adjusting operating parameters, or notifying field teams. Moreover, it also enables the use of artificial intelligence for prescriptive analysis, suggesting not only when to act but also which action to take to maximize asset availability and efficiency.

Therefore, when applied consistently, data integration transforms predictive maintenance from a reactive approach into an intelligent, proactive process supported by reliable, actionable information.

Strategic advantages for asset reliability

When multiple sources of information work in a coordinated manner, the results translate into tangible gains for operational reliability. Key advantages include:

Failure reduction through cross-data analysis

Combining industrial data and management system information creates a solid basis for identifying correlations that indicate incipient failures. In this manner, cross-data analysis improves diagnostic accuracy and reduces unexpected downtime.

More accurate technical decisions

Integrated reading of historical data and operational trends strengthens decision-making, eliminating reliance on isolated perceptions, and increasing intervention accuracy.

Centralized view of asset health

Platforms that consolidate data into a single dashboard provide a comprehensive, up-to-date view of equipment conditions. Therefore, it facilitates prioritization of critical assets and monitoring across multiple units or plants.

Reduction of unplanned downtime and improved KPIs

The ability to anticipate failures and schedule interventions during strategic windows reduces unplanned stops, increases MTBF (Mean Time Between Failures), and decreases MTTR (Mean Time to Repair), measurably improving availability and operational efficiency.

Thus, well-structured data integration ceases to be just a technological resource and becomes a competitive advantage for companies seeking excellence in asset management.

How Dynamox enables data integration in the industry

The Dynamox ecosystem was designed to ensure that data from different sources is collected, transmitted, and analyzed continuously, reliably, and securely. At its core are wireless DynaLogger sensors, which continuously monitor assets, and the DynaGateway, which ensures automatic and secure transmission of this information.

All data is centralized on the Dynamox Platform, where it is consolidated and organized for visualization, analysis, and correlation with other information sources. Additionally, to maximize connectivity, Dynamox provides a public API that allows direct integration with ERPs, CMMS, and supervisory systems (SCADA), enabling customers to connect the platform to their own systems and workflows in a personalized and scalable way.

 

Moreover, information security is a central pillar of this operation. Dynamox is certified under ISO 27001, ISO 27701, ISO 27017, and ISO 27018 standards. These certifications ensure that all data processing and storage follow strict protocols, protecting information against unauthorized access, leaks, and tampering.

By combining industrial connectivity, open integration via API, and internationally certified processes, Dynamox delivers a robust solution to transform scattered data into practical intelligence, strengthening predictive maintenance and industrial asset management.

Would you like to understand how data integration can transform maintenance in your plant?
Talk to a Dynamox specialist and discover how to connect sensors, systems, and teams in a single secure and intelligent ecosystem.

Frequently Asked Questions about Data Integration – FAQ

Is data integration in predictive maintenance the same as industrial digitalization?

No. Industrial digitalization consists of converting analog processes and information into digital formats. Data integration goes further — it connects different digital sources and systems so that information flows in a unified and structured way. In practice, an industry may have digitized assets and processes, but if they are not integrated, there will still be fragmentation and difficulties in analysis and decision-making.

Which data is most important for predictive maintenance?

For predictive maintenance strategies, the most relevant data are those that indicate the operational condition of assets. That means, this includes variables collected by IoT sensors, as well as information from management systems such as work orders, maintenance history, parts inventory, and production operational data. Combining these sources increases the accuracy of analyses and diagnostics.

Is it possible to integrate data from legacy systems?

Yes. Integration of legacy systems is possible through APIs, connectors, and middleware solutions that convert formats and protocols. However, it may require technical adaptations to enable secure and continuous communication between modern platforms and older systems. This step is essential to avoid informational silos and allow comprehensive analyses.

How can integrated data security be ensured?

Information security must be present at every stage of the process — collection, transmission, storage, and analysis. In this process, encryption, authentication, access management, and data integrity monitoring are required. Additionally, it is crucial that the platform used has recognized certifications that ensure strict protection and governance practices.

Which sectors benefit most from data integration?

Data integration adds value to any sector that depends on physical assets and complex processes. Mining, pulp and paper, food and beverage, automotive, oil and gas, and agribusiness are among the segments that benefit most, as they operate with large volumes of data and high equipment criticality. In these cases, integration enables faster, more accurate, and well-founded decisions.