Industrial DataOps Platform
Bridge the gap between Operational Technology and Information Technology to improve efficiency and reliability in your daily operations.
On a typical production line, hundreds of data points will be generated every second. The main challenge this poses is structuring and analyzing this data in a way that produces meaningful operational insights.
To address this, operational technology (OT) and information technology (IT) are integrated, which enhances efficiency through reduced downtime, predictive maintenance, and more timely AI/ML deployment. Modern DataOps solutions assist organizations in automating and scaling this process to ensure that data flows seamlessly across all systems.
#what
What is an Industrial DataOps Platform?
An industrial DataOps platform is a software solution that automates the collection, contextualization, and orchestration of data gathered from different OT and IT systems.
These platforms are valued for their ability to turn raw data into more structured insights that support industrial analytics and valuable decision-making. Within manufacturing industries, huge volumes of data are generated every single day. A common challenge is actually making this data usable across operations. This is due to issues relating to structure, consistency, and interoperability.
Industrial DataOps addresses these challenges by collecting all information from different sources, such as sensors, machines, and control systems, and creating a reliable pipeline that takes the data from these industrial systems and moves it to the tools that need it.
Many organizations rely on enterprise DataOps platforms to manage this process across multiple plants and operations. Solutions such as Orise eStreams provide a workflow-driven environment in which data exchange, validation, and approvals are managed across multiple layers.
How Does DataOps Differ From Traditional Data Management?
Businesses used to rely on traditional approaches to data analytics, which often involve a sequence of linear steps. This includes gathering requirements and designing systems, then implementing these systems and maintaining them accordingly. Within this approach, each stage relies on the completion of the previous one, which often means delayed insight and long project cycles. This rigid structure can also mean that when requirements change or errors are found, it is difficult and often costly to adapt accordingly.
It is no secret that today’s industrial world calls for quick, data-driven solutions that prevent these types of problems from arising. As such, Industrial DataOps platforms are designed specifically with operational technology in mind. This allows them to integrate more easily with industrial devices and process operational data in real time. By utilising these platforms, businesses can depend on agility, speed, and effective collaboration across all stages. These data operations solutions help teams constantly improve data pipelines while upholding high levels of reliability and governance.
#benefit
Core Components of an Industrial DataOps Platform
Data Connectivity
In an Industrial DataOps platform, data connectivity refers to the key capability to securely connect, standardize, and transport data from OT to IT systems and cloud applications. At this stage, data silos are broken down, and access to real-time data is granted without the requirement for custom coding for every new connection.
A key aspect of data connectivity is IT/OT convergence, in which the gap is bridged between operational technology (machines, sensors, PLCs, and SCADA) and information technology (cloud, ERP, and data lakes).
Data Quality
Within these platforms, data quality is a crucial requirement that will ensure the data from operational technology is accurate, reliable, and able to be used in analytics, AI, and business systems. Industrial data tends to differ from traditional IT data in that it is often siloed, unstructured, and time-sensitive.
Taking these challenges into account, it is clear to see why prioritizing high-quality and contextualized data is key. This will ensure that manufacturers avoid scenarios that could lead to failed AI models and misinformed business decisions.
In an Industrial DataOps context, data quality typically focuses on accuracy, completeness, consistency, and timeliness. There is also an understanding that any data quality issues that arise are best addressed at the source. Industrial DataOps platforms, therefore, clean and validate data at the edge to enhance security measures and improve overall response times.
Raw data from sensors often lacks meaning without context. Industrial DataOps systems add context, such as the machine ID, location, and unit of measure, to actually make the data interpretable and applicable for decision-making.
Data Conditioning
While data quality defines the standards of the information using measures like accuracy and completeness, data conditioning provides the methods that achieve these standards. This typically involves the processes of cleaning, normalization, and transformation.
In industrial DataOps, data conditioning is key as it performs these processes on raw data from industrial machinery such as sensors and PLCs, turning it into standardized and usable information. It helps ensure that the data is accurate and consistent, which enables fast and reliable analytics as well as accurate insights driven by industrial AI.
For these platforms, the most important aspect of data conditioning is converting data into a more consistent format that will align units of measurement, normalize time intervals, and structure data to be compatible across different systems. For example, ensuring that temperature readings from different machines are synchronized and comparable for analysis.
Additionally, data conditioning focuses on contextualization, linking data points with asset hierarchies and providing relevant information. To minimize latency, data conditioning also involves real-time edge processing. This means that it happens as close to the data source as possible and occurs directly after quality checks.
Unified Namespace (UNS)
A UNS provides a clear structure and hierarchy with one central place for sharing data across industrial systems. It acts as a single source of truth where both OT and IT data can be accessed.
This replaces fragmented point-to-point connections with a publish/subscribe model that allows systems such as PLCs, SCADA, MES, and ERP to send and receive data from a single shared structure, allowing seamless data exchange.
In most implementations, this is carried out by leveraging the MQTT protocol, which enables effective data transmission in remote, resource-constrained devices.
Semantic Models
In an Industrial DataOps platform, the semantic models act as the intelligence behind OT and IT convergence. They help to ensure that raw data is presented in a structured and business-friendly manner. The disparate data points from sensors, PLCs, and SCADA systems are turned into contextualized information that offers real-time, actionable insights.
These models consist of quantitative attributes, such as flow rate, as well as descriptive attributes, such as location or machine type. They structure industrial data specifically around assets and measurements to allow for efficient querying processes. They also help to ensure consistent definitions across your company by centralizing calculations and KPIs for performance metrics.
Data Security and Governance
These are key components of an Industrial DataOps platform, as they ensure that the data from OT and IT systems remains secure and compliant when moving from the edge to the cloud. They also address the specific challenges posed by manufacturing environments, such as integrating data from siloed systems and organizing fragmented sources, by implementing a structured and automated framework that views data as a secure product.
With edge computing and local processing, data is processed closer to the source, which reduces the volume of sensitive data being transmitted over networks. This lowers the risk of interception and helps to keep operations secure.
Within data governance frameworks, Unified Namespace (UNS) is an example of a key concept that provides a single source of truth for all business data. This makes it far easier to manage data across different sites and systems.
Scalability and Templates
These are critical components of an Industrial DataOps platform, as they actively transform fragmented and site-specific IIoT projects into enterprise-wide, high-value data operations.
Making use of templates to standardize data models and leveraging scalable edge-to-cloud architectures can help organizations to replicate successful use cases across sites without the need for manual integration.
Benefits of Implementing an Industrial DataOps Platform
Improved Data Quality – Ensuring that your data is accurate is crucial in any organization, and DataOps supports this by providing a strong framework for precise and high-quality data. Through implementing automated testing and monitoring processes, you can make sure that data is always complete and consistent across your entire data lifecycle.
Faster Decision-Making – Having quicker data streaming and processing (often with edge processing), it is far easier to make immediate, informed decisions based on operational issues as and when they occur, instead of waiting for historical reports.
Optimized Performance – Industrial DataOps utilizes predictive maintenance to prevent issues before they arise, which is highly beneficial for extending asset life and minimizing downtime.
Scalability – Having reusable data models means that the process of deployment across multiple machines or new plants can be accelerated.
Real-Time Data Access – Raw, siloed machine data is turned into actionable insights, which gives an immediate view into how operations are performing. These platforms allow manufacturers to move from reactive to proactive decision-making, which is especially crucial for Industry 4.0.
Enhanced Collaboration – Collaboration is improved as an Industrial DataOps Platform is able to act as a central hub, bridging gaps between OT and IT. When data from these different systems is better contextualized, teams can work from a shared, real-time data source that upholds accuracy. This enables them to make decisions more efficiently and avoid miscommunications or duplicated work.
#step
A Step-by-Step Guide to Implementing an Industrial DataOps Platform
1. Define Business Goals and Use Cases
It is important to have a specific objective in mind that you would like to achieve, such as optimizing energy consumption, reducing unplanned downtime, or cutting maintenance costs. This helps you to focus on one thing at a time instead of trying to tackle all the data at once.
IT and OT teams, data engineers, and domain experts will need to be brought together and become aligned in their efforts to bridge the gap between plant floor realities and IT infrastructure.
All data sources, such as PLCs, SCADA, and sensors, should be clearly documented, alongside any target systems and existing data flows.
2. Establish Edge Layer
Industrial connectors and protocols such as OPC UA, MQTT, and Modbus will be used to access data from machinery and legacy systems. Then, edge computing should be implemented to process data close to the source. This will bring the benefits of reduced latency, minimized bandwidth usage, and better security.
Afterwards, edge gateways will be set up to store and forward data, which prevents the risk of data loss if there is a disruption within the network.
3. Contextualize and Standardize Data
A Unified Namespace (UNS) must be created, which acts as a single structure in which all data is published and subscribed to. This will be a crucial part of providing a common semantic structure for data across the business, ensuring it can be used consistently in digital transformation efforts.
Then, data models will be applied, where standardized templates for assets are created, and local tags are mapped to consistent property names. Data should also be cleaned, with missing values handled and data transformed into more consistent formats, so it can then be accurately analyzed.
4. Implement DataOps Pipelines
Automated (ETL/ELT) pipelines are set up to automate the movement of data from the edge to the cloud or data lake. Data should be organized into clear layers for clarity, and this is usually carried out in the medallion architecture model, where bronze is raw data, silver is cleaned and contextualized, and gold is aggregated for KPIs.
Version control should also be used for data, code, and configuration files to maintain transparency and ensure quick rollbacks across industrial practices.
5. Establish Governance and Security Measures
Data governance is essential and involves defining policies that relate to data access, ownership, and compliance. This step is in place to ensure that your sensitive data is always protected.
In order to ensure that operations are secure, appropriate segmentation should be in place between OT and IT networks. While systems may become more integrated to support data sharing and digital transformation, controlled segmentation helps to manage risk and prevent unauthorized access between environments. Encryption and authentication should also be utilized for every data transfer.
6. Monitor, Iterate, and Scale
Dashboards are used to track the data pipeline performance in real time, which means that quality can be monitored instantly. Moreover, feedback loops from operators enable continuous improvement, where data models and analytics can be carefully refined.
Once these steps have been carried out, the proven, standardized templates can be replicated for additional lines or sites, scaling your solution to new, enterprise-wide assets.
Technologies Powering Industrial DataOps
- Unified Namespace (UNS) and MQTT: The UNS acts as the central hub for data, providing a structured data architecture in real-time. MQTT serves as the preferred lightweight protocol for moving data from the edge to the cloud.
- Edge Computing: Allows data processing to happen directly at the source, which reduces latency and minimizes data transfer. This is crucial for enabling real-time analytics.
- IoT Sensors: Collect huge amounts of data from machines, equipment, and legacy systems, providing the raw information needed to convert into useful insights.
- AI and Machine Learning: These capabilities are embedded within DataOps platforms to analyze, predict, and automate processes. They will often operate at the edge to optimize operations.
- Cloud-Native Architectures: Scalable cloud platforms are combined with container technologies to support resource utilization and the deployment of data-centric applications.
#industry
There are many different real-world applications of Industrial DataOps platforms, with some of the most significant being:
Pharmaceutical
Orise’s GxP Data Integrity reviewer is specifically designed to ensure that everything within a pharmaceutical system has been reviewed, evidence collected, and reports generated in accordance with GMP standards. Our GxP Logbook is also built with pharmaceutical manufacturing needs in mind. This system will collect and store data from the various activities being performed and enhance cost and time savings.
Chemical and Energy
For example, these platforms enable petrochemical producers to optimize their operations with advanced process control and data analytics. Operators can adjust to raw material variability and evolving regulatory requirements by unifying plant data and applying real-time insights.
Food and Drink
Throughout the supply chain stages, digital traceability technology will track products end-to-end. This allows manufacturers to immediately identify any sources of contamination and perform the required recalls quickly.
#contact
Contact Us
If you have queries about how Industrial DataOps platforms can merge Operational Technology and Information Technology within your organization, then we can provide you with personalized guidance to effectively optimize your daily operations. Reach out to us today!
