HighByte Blog
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Read company updates and our technology viewpoints here.
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Time to read: 8 minutes 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. → 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. The Functions An industrial DataOps solution must include five essential capabilities to achieve value:
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. Comments are closed.
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