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How Data Quality Drives Efficiency in Industrial IoT

by Michael Parisi
11 min read

To say manufacturing is a highly competitive industry would be an understatement. Numerous factors contribute to the aggressive strategies put forth by manufacturing companies ranging from industry segment, to location, to the actual goods being produced. Needless to say it can be cut-throat out there, so every little advantage available should be seized as it could end up having a significant effect on the bottom line.

Two of the biggest factors that drive competitiveness in manufacturing are cost efficiency and advancements in technology. With cost efficiency it’s pretty simple, it behooves manufacturing companies to be as cost efficient as possible because simply put, every inefficiency increases the cost of goods manufactured and the total manufacturing cost. The last thing any manufacturing facility wants to see is unplanned downtime where every minute systems are offline could cost thousands or even millions of dollars.  

On the opposite side, with technology advancements, seemingly very small improvements in processes and equipment can have a huge impact when multiplied by millions of products manufactured over the course of a year. Therefore it should come as no surprise that many manufacturing companies are compelled by the Industrial Internet of Things (IIoT) and a data- driven approach to increase efficiency and address their pain points.

The Significance of IIoT and MQTT

IIoT is a technology ecosystem that involves the connecting and interconnecting of industrial equipment, machinery and devices to the internet leveraging sensors and data analytics to enable the collection and exchange of real time data. IIoT utilizes the power of smart machines and real-time data analytics to make more efficient use of industrial machine data, which ultimately helps to improve operational efficiency, enable predictive maintenance, enhance productivity, and reduce system downtime.  

MQTT serves as the de facto protocol to help manufacturing organizations realize all of these benefits (seamless exchange of data between industrial devices and sensors, real time monitoring,etc.) with the one caveat being MQTT is data agnostic. This means that an MQTT broker ensures that an organization’s data gets from the data producers to the data consumers securely and reliably at scale, but there is no quality check on that data.

Enhance Data Quality with HiveMQ Data Hub 

It goes without saying that data is the focal point of most modern business initiatives. In this instance when we talk about data quality we’re talking about the accuracy and consistency of the data collected from industrial IoT devices and sensors. The quality of this data is crucial for manufacturing organizations to make informed decisions, optimize operations, and achieve the business outcomes they desire. 

The HiveMQ Data Hub introduces an integrated policy engine in the HiveMQ broker that can validate, enforce, and manipulate data in motion. The policy engine helps organizations that have a large amount of devices transmitting MQTT data to ensure data quality and integrity. The engine is designed to get tough on bad data by validating data before it reaches consumers. It does this by allowing users to create:

  • Schemas that determine the blueprint for how data should be formatted

  • Both declarative and behavioral policies that enforce the rules and guidelines dictating how data and messages that move through the MQTT broker should be expected by users.

These features work together to increase organizations' overall data quality and operational efficiency, while simultaneously reducing system and production costs. 

Manufacturing Use Case

Let’s look at the issues described above in the context of an automotive manufacturing company. The company owns multiple factories and each contains multiple production lines made of everything from molding machines, to quality control systems, to remote monitoring and control systems (SCADA), plus more. There could be thousands of data consumers involved as well as dozens or more third parties and third-party machines all communicating and contributing to the data pipeline. These systems are all connected using MQTT to gather and send data in order to monitor processes, conditions, production orders, and inventory management. 

HiveMQ Data Hub now empowers these plants to validate and manipulate data to ensure data quality and integrity throughout the whole organization's data stream. This is especially important when there are scenarios with thousands or even millions of producers and consumers. 

Without the level of data quality control that Data Hub provides, there are a wide range of scenarios that can occur. For example, large manufacturing companies incorporate OEE (overall equipment effectiveness) computations to glean insights and improve efficiency in the factory. Bad data entering the data stream can lead to misreporting on processes, which hinders the ability to improve the desired efficiency (identify bottlenecks, inefficient logistics) and creates delays in identifying production issues due to bad data being masked — not to mention the decline of trust caused by faulty data breaking trust in the overall data collection and analysis process. 

HiveMQ Data Hub Improves Manufacturing Data Quality with IIoT and MQTTPut simply, when you have inaccurate data, it can cause problems with predictive maintenance. This, in turn, leads to wasted time and resources spent on bug identification and resolution, system downtime, production halts, and even lower product quality due to incorrect reporting on factors like temperature, which can result in subpar products and harm the brand's reputation. Moreover, bad data can enable malicious users to inundate systems with inefficient client activities, causing additional strain and resource consumption on the system.

On the plus side, with the data and behavioral policy capabilities of HiveMQ Data Hub, manufacturing organizations have access to better business insights by acting on good data and not rogue data. As previously mentioned, Data Hub contributes to increased operational efficiency as organizations are able to stop bad actors from misusing MQTT connections and hogging resources. And last but not least, the data validation capabilities in Data Hub enable organizations to quickly and easily integrate third-party data sources in the data stream by providing clear data requirements and real-time feedback. 

Try HiveMQ Data Hub

HiveMQ is committed to ensuring the secure movement of data between devices and the cloud at the enterprise scale. Now the HiveMQ Data Hub empowers manufacturing organizations to maximize the business value of the data being transported by the HiveMQ platform so you can ensure that data is standardized, accurate, and fit for purpose.

Take a look at our policy cookbook for how to get started with Data Hub quickly (creating your first policy takes just a few minutes) and take a look at the docs for a more in-depth explanation of all the features available.

Michael Parisi

Mike Parisi was a Product Marketing Manager at HiveMQ who owned positioning for the core HiveMQ platform and HiveMQ Data Hub. Mike specializes in hosting training sessions and go-to-market plans for new products and features. He has extensive experience helping SaaS companies launch new high impact products and he revels in bringing together people and technology.

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