Upgrade Your IoT/OT Tech Stack: Replace Legacy Data Historians with InfluxDB

By Jason Myers / Use Cases
Mar 28, 2023

Intro to Industry 4.0

Manufacturing and industrial organizations are firmly in the era of Industry 4.0. The third wave of industrial revolution, which saw the introduction of computers, robots, and automation in industrial processes, has given way to instrumentation, and the use of advanced technologies, like machine learning (ML) and artificial intelligence (AI), using both raw and trained data, to enhance industrial processes.

The introduction of computers in the third wave brought with it data historians. Now a legacy technology, data historians worked well in the Industry 3.0 context because they integrated with other operations technology (OT) systems. As technology continues to advance, these legacy data historians can’t keep up with the demands of modern industrial and manufacturing processes.

The issue with industrial data historians

While data historians served their purpose, the modern factory requires something different. There are several drawbacks to these legacy data historians in the Industry 4.0 context.

Lack of interoperability

Industrial controls systems typically consist of a combination of hardware and software. These Supervisory Control and Data Acquisition (SCADA) systems collect and store industrial data for shop floor operators locally in an on-site data historian. However, these historians are closed solutions that lack open APIs, microservice architecture, or other networking technologies that allow users to separate data and transmit specific data to other tools and services to draw out additional value from that data.

Vendor lock-in

Along similar lines, because of the specialized nature of these legacy data historians, operators must purchase all components and software updates from a single vendor. Primarily Windows-based systems, they don’t provide APIs to interface with software outside its own ecosystem. This limits manufacturers’ ability to add new machinery and technologies to their technology stacks, inhibiting production and growth.

Poor developer experience

The closed design of legacy data historians and limited API support reduces the ability of developers to extend industrial systems. When it comes to tools, developers are left with whatever is in the limited ecosystem of the data historian, and the available options may not serve the needs or goals of the organization. Best-in-breed solutions are almost completely out of reach, so even if users want to add functionality to their OT stack, they can’t. Closed source software lacks the community support that open source solutions have, further limiting innovation and, by extension, developer interest.

Scalability

The limitations of legacy data historian ecosystems also limit the scale at which an industrial organization can operate. Without the ability to add functionality or derive new insights from the available data, companies take on significant risk when expanding their commercial operations. Related to this challenge, data historians are designed to handle a limited dataset. They simply cannot do the things that modern industrial organizations want to do with their data. The need to introduce advanced capabilities like AI or ML require vast amounts of data to train models. Data historians aren’t built to handle the large datasets generated by modern, instrumented machinery, nor the processes that require that data at scale.

Cost

It probably comes as little surprise that, given everything else, the total cost of ownership (TCO) for data historians is very high. Data historians are expensive to set up, typically have annual licensing costs, and charge support fees. As proprietary software, they’re not one-size-fits-all, so they require custom development work to install and maintain. Not only does this translate into more work for in-house developers, but often involves external consultants, too. In either case, the cost of this type of work is significant.

Predictive maintenance

At a time when companies want to get more value out of their data, legacy data historians limit the extensibility of that data, reducing the overall efficacy of OT systems. Data historians can record the state of things but lack the capabilities and interoperability necessary to meet the needs of Industry 4.0.

It’s no longer enough to simply know how many hours a machine on the shop floor operated. Organizations also need the ability to ensure that machine is running at optimal levels, that its output is consistent, and to connect machines to external technologies to enable predictive maintenance.

For example, you can use data to determine the lifespan of a valve. You can then use predictive maintenance models to track the usage of valves across your entire operation and set alerts for when parts are near their end of life. This approach enables organizations to be proactive with maintenance and repairs, and to schedule downtime so that it doesn’t affect production or the company’s bottom line.

Replacing data historians

To get the benefits of predictive maintenance, and other advanced technologies, organizations need to move beyond legacy data historians and their inherent limitations. A time series database, especially an open source one, like InfluxDB, solves all the limitations of a data historian right out of the box. Telegraf, an open source data collection agent that’s part of the InfluxDB platform, uses plugins to connect to virtually any data source, including MQTT, Kafka, ModBus, OPC-UA, and more. InfluxDB also integrates with established IIoT/OT ecosystems, like PTC Kepware, PTC Thingworx, Bosch ctrlX, and Siemens WinCC OA.

With InfluxDB managing your time series data, industrial organizations can run advanced, real-time analytics, leverage large datasets for AI/ML processes, deploy effective predictive maintenance procedures, generate historical and real-time digital twins, and much more. It can do all this at scale, both in the cloud and at the edge. Leverage edge data replication to create durable data transfers between the edge and the cloud so you can use both in tandem, ensuring the right stakeholders always have the data they need.

So, whether you’re looking to improve industrial practices on the factory floor, track distributed assets at the edge, enhance renewable energy production, or push the boundaries of science, both on earth and beyond, InfluxDB has the capabilities and features that enable Industry 4.0 to advance and thrive.

Sign up for a free trial of InfluxDB to start getting more value from your industrial data.