Many enterprises store the data from each site in a separate system, creating data silos that prevent unified analytics and management. As digital transformation progresses, it becomes necessary to break down these silos and create a unified platform for data storage and processing. However, simply aggregating all data is not enough; a modern data historian must retain the context of the data and implement data governance for centralized data to be useful to the organization.
How Data Centralization Benefits Industry
When all operational data is centralized in a single repository, access to the data is improved, and the manual workloads involved in preparing and transmitting it are eliminated. Teams can then work with real-time data for faster response times and more efficient problem-solving, and also collaborate more efficiently with analysts and data scientists inside and outside the organization.
Furthermore, data centralization enables advanced analytics that can produce new insights from industrial data, from predictive maintenance to process optimization. Decision-makers are empowered with comprehensive data sets and can make well-informed decisions based on accurate and relevant information.
What TDengine Offers
With TDengine, you can easily centralize industrial data from a wide variety of sources, including PI System, MQTT, and OPC. TDengine is a zero-code platform that requires only minimal configuration to implement the extract, transform, and load (ETL) process for industrial data sources, whose data is then centralized in our modern data historian.
There are benefits to deploying TDengine in all layers of the industrial data infrastructure. The preceding figure shows an architecture for a multi-site enterprise where TDengine is deployed on the edge, in a centralized location, and in a secondary cloud.
By making use of TDengine’s connectors, you can easily ingest data from a variety of industrial sources into a TDengine instance deployed on the edge. All data being generated at a site can be collected in a single system even when multiple protocols are in use — just connect your OPC servers, MQTT brokers, and other data sources to TDengine.
You can then configure TDengine instances on the edge — and even additional data sources — to send their data to a centralized TDengine deployment in a central data center or in the cloud. When you centralize your industrial data in the unified TDengine platform, you can discard “dirty” data to implement good data governance and avoid polluting your database. You can also associate labels with the data imported from your sites and add prefixes or postfixes to ensure that the context of your data is preserved after centralization.
After centralizing and cleaning your data in TDengine, you can replicate it to another TDengine instance in a different cloud or region. This is an effective way to implement high availability, as all of your data can be accessed from multiple sites, and also an option for disaster recovery. Data replication is an automated process in TDengine that makes use of the data subscription component.
With TDengine as your central repository for operational data, you can easily integrate visualization and business intelligence tools such as Seeq or Power BI to build company-wide dashboards and reports or connect with AI platforms for advanced analytics. Your applications and algorithms have access to all data in real time, enabling global insights and efficiency without custom code or manual operations.