Manufacturing data: too much of a good thing?

Terri Ghio explores the importance of centralised data and interconnectivity, data architecture and eliminating siloed data

Whoever said, “there’s no such thing as too much of a good thing” clearly never looked at manufacturing data historians full of unused data. At the turn of the 21st century, there was a push towards collecting greater and greater amounts of data in manufacturing. Data collection was—and remains—a good impulse, but data collection rapidly outpaced data integration, architecture, and analytics. Today, vast amounts of data are going unused.

The road to data inefficiency

Modern manufacturing abounds with analogue and digital systems. At any given manufacturer, one might find various combinations of ERP, MES, CRM, PLC, and SCADA systems, not to mention home-grown excel workarounds. Each of these systems collects vast amounts of data. However, not all data is equally valuable. Data is only helpful insofar as it can be leveraged for actionable insights which lead to more efficient processes and ultimately positive ROI.

Data collection rapidly outpaced data integration, architecture, and analytics. Today, vast amounts of data are going unused

Manufacturers have spent significant investment on various legacy systems, which on their own deliver results. Ye, they’re routinely finding that the ROI falls short of projections. The problem is not the systems; it’s a lack of interconnectivity.

When manufacturing systems are functioning independently, their utility is greatly reduced. Just as a business will struggle without collaboration between employees, manufacturing systems need interconnectivity to reach their full potential. With the right solution, manufacturers can get the most out of their existing systems, turning silos of unused data into pipelines of insight.

Data integration

The first step to efficient and effective data is rationalising and integrating data in a data rich environment. To do this, manufacturers leverage Industrial Internet of Things (IioT) devices to gather data from the shop floor. The key, however, is not merely creating a data rich environment, but ensuring that the data being collected is effectively integrated between systems and machines.

Manufacturers need plug and play connectivity between existing machines, workstations, and software systems, whether old or new, manual or automatic. Moreover, mid-size manufacturers face additional pressure to remain ROI positive throughout the journey to Industry 4.0 and data efficiency. To do this, they must optimise what they already have, rather than making costly wholesale replacements which will take years to become ROI positive. By integrating existing systems with Industry 4.0 technology, manufacturers can set a trajectory of continuous improvement as they implement Industry 4.0 in stages as well as immediately proving ROI through key performance indicators (KPIs) such as decreased waste, energy efficiency, increased productivity, and decreased downtime from predictive maintenance.

Data architecture

Data silos, which plague countless manufacturers, are at the core of the data architecture problem. When data is left in a data historian, individual machines, or separate software systems, it becomes siloed. The solution is data centralisation through Industry 4.0. Centralising data into data lakes increases the value of data by creating an ecosystem for cross-communication which leads to actionable insights. Industry 4.0 allows for organisations to preemptively strategise and address issues both before and as they occur, instead of looking in the rearview mirror and reacting to issue when it is already too late.


To illustrate, imagine a case of broken machinery. When a machine breaks down, it pays to have the tools close at hand. If a company has the tools, but they’re in a different warehouse across the country, they won’t be of much use. The same is true with data. Collecting data that is staying stagnant in one isolated system won’t help solve the complex, interconnected problems that manufacturers are facing. However, if that data is centralised in a user-friendly dashboard, manufacturers can leverage actionable information to make better business decisions.

Data analytics

Proper data architecture is the prerequisite to optimal data analytics. Centralised data ensures that all departments have access to the data they need. Far too often various business departments only have access to data from within their department, hindering a chance of the holistic view. With limited visibility, their data analytics have limited utility.

For example, in a typical siloed system, a sales/quality associate might have access to data analytics on warranty claims and product recalls such as how many claims were made, clusters of claims, percentage of products recalled, and so on. With centralised data, this warranty data can be integrated with manufacturing floor data to analyse what was happening on the factory floor when a warrantied product was produced.

Data is only helpful insofar as it can be leveraged for actionable insights which lead to more efficient processes and ultimately positive ROI

Furthermore, the warranty data and production data can be correlated to raw material data. Rather than having three separate teams see one-third of the picture, teams can now see the whole picture and make correlations between raw materials, production, and finished products. For the C-Suite, this means no more tracking down fragmented information from disparate sources. Instead, data analytics is as easy as pulling up a dashboard on a phone, tablet, or laptop from anywhere in the world.

What’s next?

In manufacturing, data is cheap, and execution is everything. If today’s mid-size manufacturers hope to compete in a crowded marketplace, they can’t afford to have large amounts of siloed data. Manufacturing leaders must shift their target from mere data collection to data-informed execution. To do so, this begins with creating data integration at every level of operations, whether between shop floor machines or top floor enterprise systems. Then this integration must be done with proper data architecture, centralising data into data lakes which ultimately allow for high quality analytics across multiple departments.

Industry 4.0 was once thought of as something nice to have, if you had the resources. Today, manufacturers of all sizes are rapidly realising that Industry 4.0 is necessary for staying competitive in the short term and future proofing for the long term. Fortunately, Industry 4.0 solutions have become more affordable over the years, and now is the time for mid-sized manufacturers to embrace modern technology and reach smart factory status.

About the author: Terri Ghio is President of FactoryEye

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