The manufacturing data maturity model and the first frontier

Posted on 13 Sep 2022 by The Manufacturer
Partner Content

Data and analytics capabilities have leapt forward in recent years. The volume of available data is growing exponentially, more sophisticated algorithms have been developed, and computational power and storage have steadily improved. Therefore, instead of intuition, the new normal is to rely on data to drive digital innovations and business decisions. Indeed, data is the “most valuable resource” for organisations including in manufacturing.

Industry 4.0 brings together advanced manufacturing technologies like Artificial Intelligence (AI), Machine Learning (ML), Digital Twins, Augmented Reality (AR) and Virtual Reality (VR) to enable integrated, autonomous, and self-organising manufacturing systems that operate independent of human intervention. Manufacturing machine/process data can be analysed by algorithms and used for critical real-time business and operational decisions that directly impact production outputs. 

As shown in Figure 1, the journey from data collection to digital maturity is one in which analysis, context and insights are added to transform raw data captured from a device or system into information, knowledge, and finally actionable wisdom for decision makers.

Figure 1: The stages of data maturity model on the path to realising Industry 4.0.

First, data is collected from manufacturing machines/processes and normalised, digitised, and organised as Big Data. Next, meaning is added and data is synthesised into knowledge via AI. Finally, the data is transformed into actionable wisdom attained through the combined insights of digital maturity.

Data Collection – The First Frontier

The first and most significant frontier to achieve digital maturity for Industry 4.0 is data collection. Data from manufacturing machines/processes is captured via sensors and stored via several key technologies. On the Operational Technology (OT) side data is stored with controllers, PLCs, gateways, and edge devices, and on the IT side with a data centre or enterprise cloud. Data storage technology enables the long-term storage of digitised data captured from advanced sensors. This data-rich environment enables initiatives from Industrial Internet of Things (IIoT), to Big Data and simulations, AI, adaptive control, and digital twins. 

There are some challenges to data collection in manufacturing. Machines and processes in the manufacturing plant are heterogeneous and use various protocols to communicate. Data connectivity is also a major issue due to the archaic, legacy nature of factory systems. As a result, typically IT and OT systems don’t have an easy way to communicate to enable Industry 4.0 initiatives.

A key technology enabler for overcoming these challenges and bridging OT data to IT systems is a data broker. Using an underlying standard such as MQTT, a data broker supports the ability to have multiple clients connected that are publishing data and multiple clients that are subscribed to receive the data such as enterprise applications. The clients communicating with the broker can abstract the underlying protocol that the machines/processes use to communicate. The broker works well in low bandwidth environments with unreliable communication mechanisms due to the underlying publish/subscribe method where machines/processes don’t need to keep polling to get the data.

The broker is able to securely communicate the data between publishing clients typically on the OT side to subscribing clients on the IT side. For example, a streaming analytics application might want data from the SCADA system to run its analytics and publish real-time results. The application would run an MQTT client that is subscribed to the broker. The SCADA client would publish data to the broker when available. As a result, the streaming analytics application subscribed to the broker would automatically get the updates without needing to poll for the data.

Figure 2 provides a sample data architecture of how the data broker connects multiple machines/processes and applications to enable seamless bidirectional data movement.

Figure 2: Data must be architected to support multiple data producers and data consumers to bridge OT to IT.


As discussed in this article, when harnessed correctly, data is the most valuable asset for many organisations, particularly in manufacturing. In order to achieve the most benefit from Industry 4.0 technology, data needs to be transformed to wisdom. A key initial step towards this journey is data collection.

There are several challenges to data collection especially when it comes to bridging data efficiently from OT systems to IT systems. Data brokers play a very important role to ensure that the data is available for advanced use cases that allows organisations to fully benefit from Industry 4.0 technology.

About the author

Ravi Subramanyan

Ravi Subramanyan, Director of Industry Solutions Manufacturing at HiveMQ

Ravi Subramanyan is a Product Marketing and Management leader with extensive experience delivering high-quality products and services that have generated revenues and cost savings of over $10bn for companies such as Motorola, GE, Bosch, and Weir. Mr. Subramanyan has successfully launched products, established branding, and created product advertisements and marketing campaigns for global and regional business teams.

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Email ID: Ravi Subramanyan <[email protected]>