Which is better? On-Premise or Cloud Based Industrial Internet of Things Data Flow?
Holger Amort
Which is better? On-Premise or Cloud Based Industrial Internet of Things data flow.

Applications around the Industrial Internet of Things (IIOT) have mushroomed and each one comes with a different set of capabilities and features. So how do you compare different applications or services? And how does the new solution fit into your existing data architecture?

In general, industrial internet of things architectures fall into three categories: (1) on-premise, (2) cloud based or (3) a hybrid of the two. In the on-premises solution, data are never leaving the manufacturing network, whereas in the cloud solution all data are directly send to the cloud. In the hybrid solution, a subset of the data is replicated to the cloud and used for analysis.

Industrial Internet of Things data flow.

Today, many industrial internet of things applications fall into the hybrid category and lead to a scenario where some applications will execute on-premise and others in the cloud. To choose the right blend of on-premise and cloud functionality, let’s consider the following key metrics:

  • Data Integrity: Assures that a time series is complete, and no data points are lost. This often requires node buffering to avoid network outages or disconnects. The buffer fills in any gap on reconnect.
  • Data Availability: A highly reliable infrastructure that provides up-to-date data at any point in time. A high data availability can be achieved by deploying redundant architectures.
  • Data Latency: Latency is the time it takes for a single data point to move from the source system to the destination system. Latency on premise varies between 10s of milliseconds to seconds. Cloud-based connections are on average slower. The main drivers for data latency are network speed, queueing at the source and target system.
  • Data Compression: Industrial scale databases typically compress sensor data, which means that a timeseries is down sampled to reduce storage size and increase database performance. The most balanced approach is the swinging door compression that has shown to be superior to regular interval based down sampling (every n seconds or minutes).

For regulated industries, there is often a requirement that the compressed timeseries is identical between two components.

  • Redundancy\System reliability: Any component that is added sequentially reduces the overall system reliability, whereas parallel or redundant components increase the system reliability.

For a sequential system, the calculation is as follows:

R=R1×R2×R3× ... ×Rn=ΠRj

As an example, if a system has four components with a reliability of 95% each, the overall reliability drops to 81.4%.
Making the same system redundant increases the overall system reliability to:

R=1-(1-R1)×(1-R2)×(1-R3)× ... ×(1-Rn)=1-Π(1-Ri) or 96.6% using Ri=81.4%

  • Meta Data: Data cleansing and contextualization are key to deploying analytics at scale. Data are contextualized on premise (example OSIsoft AF) or in the cloud application. The ability to replicate existing meta data depends on the connector’s data protocols, common ones are:

    MQTT Sparkplug A or B

Highbyte is providing in flight data contextualization on the edge. This opens the door for very flexible and dynamic solutions.

Most of the protocols are equipment centric, missing relational information (one-to many and many-to-one) and time segmentation. Microsoft’s Digital Twins Definition Language (DTDL) is a relative new approach that has the potential to bridge the gap.


Industrial internet of things apps range from pure on-premises to all cloud-based solutions. On-premises architectures typically provides a higher system reliability and lower latency, while cloud-based solutions offer scalability, flexibility, and wide range of readily accessible data analytics. As a result, manufacturing IT will most likely have a blend of both, where process level analysis will run on premise and enterprise level analytic in the cloud.

Current connectors do not provide a complete manufacturing process model, industrial strength data compression, and redundancy necessary to seamlessly integrate into existing on-premises data architectures. But this is changing quickly and new approaches of in-flight contextualizer are closing the gap quickly. The goal being to better understand and utilize industrial internet of things data.

Please contact us for more information.

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