A Strategic Approach to Replacing Data Historians
Jason Myers /
Jun 14, 2023
Recently, I wrote an article discussing why industrial organizations should migrate from legacy data historians to modern, open source technologies. The reasons for such a migration remain valid; however, it dawned on me that such a heavy-handed approach is not always right for every organization.
While it’s easy for technology producers to simply recommend ripping out antiquated technology and replacing it with the latest and greatest, such an approach doesn’t reflect reality. Industrial operations tend to be very complex and involved, so that means that they can’t turn on a dime, especially when hampered by legacy technology. So, I want to spend a bit of time discussing more pragmatic approaches to implementing new technologies in the IoT/OT tech stack.
Automating manual processes
With the advances and proliferation of technology these days, it can still come as a surprise to find out that organizations rely on manual processes. At the same time, these processes present a unique opportunity for organizations to automate and/or optimize them.
This situation can provide low-risk opportunities for trying out new solutions. For example, let’s say we have a process whereby an individual walks the factory floor to check specific readings on certain machines. When we discover that a new technology, like Telegraf, can collect that data automatically, we’re able to create new workflows and automated processes to not only collect, but analyze and alert on that data. Now the person who had to walk the factory floor can keep an eye on dashboards and only needs to head down to the floor when alerted about an issue with one of the machines.
Overall, this is a situation that is great for starting small with a new technology so that you can get used to it, understand its capabilities, and then expand to other areas.
Breaking change in legacy technology
This is a situation that may be out of your hands. When legacy technology vendors introduce breaking changes or opt to end-of-life a product, this may create problems for otherwise optimized processes. You may be able to skirt breaking changes by simply not choosing to upgrade, but regardless an external interruption to your current tech stack can provoke a broader change.
This is an area where the interoperability, extensibility, and flexibility of open source tools really shines. Because not only can they replace legacy products, but they also add new functionality and capabilities that extend the value of industrial data, leading to greater optimization, efficiency, and effectiveness.
The next two situations may be related in some cases, but not necessarily so. Upgrading equipment may involve replacing an older model machine with a new one or updating the firmware on an existing machine. When this occurs, the likelihood is high that the new/updated equipment has different sensors or capabilities. A legacy data historian may be able to take advantage of these new developments, but the reality is that to do so will involve a lot more time and money than it’s worth.
Introducing a system with broad interoperability and capabilities, like InfluxDB, allows you to take advantage of all your equipment’s features with a very light lift.
Hopefully, business is good and that means growth. Having the right technology in place can help mitigate growing pains. Whether you’re looking to add a new line to an existing factory, add an entirely new facility for a larger, distributed organization, or any other type of growth, you have an opportunity to implement new technologies.
The beauty about this situation is that you can test and iterate on each aspect of the process as you build it. Let’s say you chose to use InfluxDB for all your machine data in a new facility. As you’re installing and testing the new equipment, you can compare the results you get from InfluxDB with similar processes in other lines or facilities and start to optimize right away.
There’s a certain irony intertwined with innovation. There’s a lot of talk about thinking outside the box, but doing so often involves risk. Yet, many organizations are inherently conservative when it comes to putting the rubber to the road. That’s why it seems important to highlight how iterative the process of replacing a legacy data historian can be. Clearly, there are infinite variations on the situations above, and other contexts to consider as well. The important thing is to remember that the versatility of a solution like InfluxDB enables you to start small, implement quickly, and scale deployment and scope at the pace that makes the most sense for your organization. This reduces the risk of upgrading your IIoT/OT stack, and ultimately leads to significant improvements in a wide range of industrial processes.
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