Proper data management — with clean, consistent data that can be accessed and shared across business systems — is key to a successful Internet of Things framework.
For many companies, especially those with large databases or data warehouses, data management (including definitions, classifications, and integrity) is often reduced over time after an initial data project is under way. This can create significantly more complex challenges and requires more powerful data cleansing, filtering, and analysis tools to convert data into useful information.
Industrial IoT could generate more than $3 trillion for the global economy, thanks to about 50 billion devices that will be deployed by 2020, estimates McKinsey & Co.
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- IoT initiatives should include a coordinated data management, integration, and synchronization strategy.
- Data management directly affects systems monitoring, which is critical to maximizing the uptime of production lines and robotics equipment.
- Interconnected planning, scheduling, and execution systems require accurate and current data, as well as real-time activity to meet users’ expectations.
Data management for manufacturing
For many manufacturing organizations, data management is top of mind, especially as manufacturers employ complex and interconnected planning, scheduling, and execution systems. These systems require accurate, de-duped, and current data as well as real-time (or near-real-time) activity to meet users’ expectations.
Not only should users be aware of the methods of data collection — such as mobile sensors in drones or robots — but the types of data and where they go are also relevant.
Manufacturers aren’t the only organizations for which data management plays a fundamental role. Companies of all types and sizes need to manage and transform their data into actionable information.
These capabilities are particularly important to organizations deploying IoT solutions that span IT functions, including enterprise resource planning (ERP), enterprise asset management (EAM), manufacturing execution systems (MES), and a wide variety of back-office systems. Because of the integration and interoperability that such systems need, businesses should have a coordinated data management, integration, and synchronization strategy.
Also, many companies today need visibility beyond automation controls. Industrial IoT encompasses analytics such as transaction monitoring and component tracking for predictive maintenance. These capabilities enable organizations to better meet uptime requirements as demanded by 24/7 operations. By watching equipment for signs of failure, a factory could schedule maintenance tasks for non-peak times.
Asset monitoring, systems integration, and IoT data
Other areas in which data management is needed for successful IIoT implementation is in monitoring systems and networks availability and in integration among systems.
High-quality asset management data is needed for enforcing service-level agreements. Industrial end users need to be able to rely on such data to determine whether their expectations are being met or exceeded.
As companies move beyond point-to-point integration and move toward integration hubs and native interoperability across components and systems, they need an enterprise integration strategy.
This may be challenging because much of this data is “big data” and is largely unstructured. Since such data can originate from a wide variety of sources, a well-planned and executed integration layer or hub is essential to enable interoperability between systems, as well as to provide a conduit to analysis tools.
In some cases, organizations are also developing broad strategies to ensure that the underlying systems architecture and technology stacks include native interoperability to between sensors and databases to provide real-time visibility.
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More on Industrial IoT:
- Don’t Worry — Robots Aren’t Going to Take Your Job
- Tend in.view Software Remotely Monitors Cobots
- Why Can’t Smart Manufacturing Be Simple?
- CROs Need Robotics, AI Roadmap to Prepare Others for the Future
- NIST Scientist Talks AI, IoT, and the Human Draw of Speaking at RoboBusiness
- Robotics Advances Affect Geopolitics, Cyber Security, and Retail
- IoT World Looks at Buzz, Barriers Around Industry 4.0
- Top 6 Automation Trends Discussed at LiveWorx 2017
- Why Will 5G Disrupt the Robotics and Manufacturing Industries?
Know your IIoT partners
Still another consideration for companies considering various IoT initiatives is the selection of qualified and like-minded technology partners. The use of code sharing for application programming interfaces (APIs), open APIs, and integration hubs can help IoT partnerships succeed.
As businesses strive to better understand their customers, data management in providing the foundation for effective IoT implementations. Today’s manufacturers need to review, analyze, and navigate through unprecedented amounts of structured and unstructured data to maximize their use of big data from robotics and AI systems.
Consequently, a well-planned and executed data management strategy is essential in creating an effective IoT framework that can be used throughout today’s increasingly sophisticated manufacturing organizations.