How Industrial DataOps is shaping Industry 4.0
John Harrington
is the Chief Product Officer of HighByte, focused on defining the company’s business and product strategy. His areas of responsibility include product management, customer success, partner success, and go-to-market strategy. John is passionate about delivering technology that improves productivity and safety in manufacturing and industrial environments. John received a Bachelor of Science in Mechanical Engineering from Worcester Polytechnic Institute and a Master of Business Administration from Babson College.
The manufacturing industry is experiencing a change so significant it has earned the title of Fourth Industrial Revolution. This transformation was kick-started by the need to become more data driven and then fueled by a number of recent technological advances. Early adopters in factories around the world recognize that industrial data—operations data coming from machines, processes, products, and systems on the plant floor—is gold. More users and systems want access to this data in real time to convert it into valuable information they can act on to predict machine failure, prevent downtime, and improve product quality. In fact, IDC recently projected that there will be 41.6 billion IoT devices in the field generating 79.4 zettabytes of data by 2025. These devices include machines, sensors, and cameras as well as industrial tools. It’s an immense, even overwhelming, volume of data. How can companies leverage it effectively?
The Problem: Unusable Data
Industry 4.0, Industrial Transformation, and Smart Manufacturing combine disparate information to drive automated decisions from machinery to the Cloud. Multiple technologies play a role in this transformation, including Cloud computing, IIoT platforms, advanced analytics, augmented and virtual visualization, mobile platforms, miniature and inexpensive sensors, and networking. The objective is to put more information in the hands of stakeholders when and where they need it.
Early adopters of Industry 4.0 technologies imagined that simply connecting their industrial data to analytics and visualization applications via APIs would deliver results. Instead, they discovered the data lacked accessibility and context. It was inconsistent across machinery and calibrated to the controls equipment—not to how business users think.
The Solution: Industrial DataOps
Today, a new software category is emerging that will be key to helping companies adopt Industry 4.0. Known as DataOps (or industrial DataOps when specifically designed for industrial data), this approach solves data architecture and integration challenges and provides data standardization and contextualization for enterprise-wide use.
→ Data Architecture & Integration
Prior to Industry 4.0, industrial data architecture had evolved into a layered approach defined in the Purdue Model or ISA-95. Data flowed from sensors to automation controllers to SCADA/HMI to MES and finally to ERP. But both volume and resolution reduced significantly as data moved up the stack. Communication protocols at each connection point between layers tended to be proprietary and unique rather than reusable.
Processing data through layers of systems worked for many years primarily because the amount of data was relatively limited. This is no longer the case. Pushing excess, unused data through systems that don’t need it (to arrive at those that do) complicates and slows processing, reduces security, and increases vulnerability.
Processing data through layers of systems worked for many years primarily because the amount of data was relatively limited. This is no longer the case. Pushing excess, unused data through systems that don’t need it (to arrive at those that do) complicates and slows processing, reduces security, and increases vulnerability.
→ Data Standardization & Contextualization
Industrial Transformation aims to leverage real-time data to drive the business. In a manufacturing company, this means extending factory floor data beyond the traditional operations environments to business users throughout the company. These users do not have the same understanding of the manufacturing controls system but require rich data to optimize business performance.
Because data in industrial environments is very inconsistent across machinery, lacks context, and is correlated to the controls equipment, an industrial DataOps solution has very different requirements than the DataOps technology used today for business transaction systems.
Because data in industrial environments is very inconsistent across machinery, lacks context, and is correlated to the controls equipment, an industrial DataOps solution has very different requirements than the DataOps technology used today for business transaction systems.
The Functions
An industrial DataOps solution must include five essential capabilities to achieve value:
- Standardize, normalize, and contextualize data.
Industrial data is generated by motors, valves, conveyors, machinery, and other such equipment. This data typically comes from PLCs, machine controllers, RTUs, or smart sensors. A factory might have hundreds of PLCs and machine controllers, often purchased at different times from different vendors. The data points available on the controllers vary; they were also designed for use by industrial software solutions.
To derive valuable insights from this data, it must be analyzed across machinery, processes, and products. A set of standard models is required within the DataOps solution to handle the scale of hundreds of machines and controllers, correlate the data, and present it to the consuming applications. - Connect to both industrial and IT systems.
Industrial devices/systems and IT systems natively communicate in different ways. The former use many proprietary protocols, though support for OPC UA and other open protocols is trending upward. IT systems rely on their own protocols to communicate with extensive usage of APIs and bespoke integrations.
IT systems communicating with Edge devices have begun to leverage MQTT, which provides a highly flexible pub/sub methodology, with low overhead, to minimize cybersecurity exposure and secure encrypted communications. A DataOps solution must be able to integrate seamlessly with devices and data sources at the operations layer by leveraging industry standards, while providing value to business applications that conform to current IT best practices. - Manage the flow of information.
It is critical that information flows are contained and managed within a system where they can be identified, enabled, disabled, and modified. In order to ensure that “good” data is being stored, machinery changes must be accurately reflected in the data collected and transformed.
From a security perspective, it is critical to know what data is moving among systems -- and be able to turn it off. Because many vendors want machine data in order to provide enhanced service, the operations team needs to be able to control that data flow and set conditions and frequency. - Provide industrial-level scale and security.
The vast breadth and scale of industrial data distinguish it from the typical transactional data stored in most IT systems. Industrial data is typically used within milliseconds to seconds after it is created and must be contextualized and delivered at a resolution unique to the specific use case.
As such, batch processing ETL solutions built for transactional data do not work well for industrial data. Furthermore, industrial data contains the intellectual property of a manufacturing plant, which must be secured and discretely communicated. - Live at the Edge.
Analytic and visualization applications that consume industrial data may be processed close to the machinery, in an on-premises data center, or in the Cloud. But the industrial DataOps solution itself must run close to the device, feeding applications at the frequency or condition specified. It must also be able to share models across the factory and the company to allow for data standardization and normalization.
The Takeaways
An effective industrial DataOps solution:
Want to read more on this topic? Download our white paper DataOps: The Missing Link in Your Industrial Data Architecture.
- Addresses data inconsistencies across machinery
- Normalizes and contextualizes data
- Aligns data for business users
- Provides secure management of data flow
- Offers accessibility across network infrastructure, throughout the company, and to external vendors
Want to read more on this topic? Download our white paper DataOps: The Missing Link in Your Industrial Data Architecture.
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