For industrial operators to capture the benefits of increased automation, they cannot rely on cloud-technology alone to bring the resiliency and speed demanded by AI, HD cameras and other Industry 4.0 technologies. Local edge data centres are IT infrastructure enclosures/spaces/facilities distributed geographically to enable endpoints on the network.
When in industrial environments such as a manufacturing plant or distribution centre, this application is referred to as “industrial edge.” Analysts have identified the edge as becoming increasingly important.
The industrial edge is one of the fastest-growing segments of industrial automation and a key driver that is influencing digital transformation. To achieve the shortest possible ROI, manufacturers must properly measure asset performance, rapidly identify any problem areas, and make any crucial changes in real-time that will drastically improve their operations.
The industrial edge is where this important on-site data capture occurs, real-time analysis of this data is performed and converted into intelligent information, and then shared with the cloud and throughout the entire enterprise while addressing manufacturers’ concerns, such as latency and security for production environments.
Industrial Internet of Things (IIoT) and Industrial Edge holds a promise of a future that offers seamlessly connected devices and integrated systems that would make industries efficient, optimise overall operations, and support sustainable growth. As companies in a variety of industries continue to take advantage of IIoT applications, they’re facing questions around how and where to best process and store vast amounts of IIoT data.
It’s an important question because of the sheer size and scope of investments being made in IIoT applications. McKinsey & Company estimates companies will spend between $175 billion and $215 billion on IIoT hardware by 2025, including computing hardware, sensors, firmware, and storage.
Gartner predicts that 75% of enterprise generated data will be stored, processed, analysed, and acted upon at the edge by 2025. That’s a big number, but it still leaves 25% of data to be processed elsewhere.
Which gets back to the question of how to decide where best to store, process, and analyse all that IIoT data. To answer the question, you need to explore four factors about the data in question – the big Vs: volume, variety, value, and veracity.
- Volume: The amount of data being generated is the first factor to consider. IIoT environments generate large amounts of different data streams with wildly different volumes. Simple things like temperature, pressure, time, and volume levels produce relatively small amounts of data; that’s especially true if they’re measured only occasionally, as opposed to constantly.
- Variety: As the volume discussion makes clear, IIoT applications involve many different varieties of data. Not all sensor data, for example, is of the low-volume type associated with, say, temperature. Even a sensor that measures the levels of a liquid may differ dramatically from one application to the next.
- Value: Value is a determination of what data you need to keep and for how long. If you measure temperature as part of a manufacturing process, does that data have any value after the process is complete? Is there a reason you need to keep it beyond today, tomorrow, or next year?
- Veracity: The final quality to consider is the veracity of the data, or whether it’s accurate. In most enormous data streams, it’s likely that some amount of the data represents outliers or inaccuracies.
If you apply the big Vs to a given IIoT application, it should paint a picture of where the resulting data needs to be processed, stored, protected, and how best to transport it.
In addition to the latency requirements, bandwidth cost can also be a factor. The more data needs to be sent, the greater the bandwidth provision needs to be. And the bigger the data pipe, the greater the investment in network capacity and infrastructure.
Sometimes, the sheer volume of data will dictate that it is best to initially deal with it locally, and then plan to send a subset of that data to a regional or cloud facility for additional processing. An example might be a process automation application where the data required to actually run the process is processed locally, while data about the process — time required, quality assurance results, health of the machines — is sent to a cloud-based analytics application to optimise the process and track the health of the machines involved. Depending on the actual use case and the aforementioned considerations, deploying a hybrid data architecture seems to make the most sense.
Finding the right balance between industrial edge computing, larger regional enterprise or colocation data centres, or cloud data centres can be tricky. If you’d like some help determining the best IT solution for your IIoT applications, join the Schneider Electric Exchange.
On Exchange, you can connect with experts for advice and find qualified service providers who can help get your IIoT application from the proof of concept stage to production, where it can generate real value.
