Does investing more in digital technologies solve “Garbage in – Garbage out”?

Many companies rely on technology to manage their assets, from equipment and machinery to software and data systems. While there is no doubt technology is a valuable tool for asset management, it's critical to remember that it's only as effective as the underlying data. Layering more technology on top of incorrect data is unlikely to solve the foundational problems which result in poor performance and high costs and may in fact make us less productive.

One of the biggest problems with relying on technology to manage assets is the risk of "garbage in, garbage out" (GIGO). That is, if the data going into the system is incorrect or incomplete, the output will be flawed as well. For example, if a company doesn't have a complete and accurate inventory of all their assets, any software used to manage those assets will be working with incomplete data. This could lead to mismanagement, underperformance, and costly mistakes.

It gets even more complex when it comes to maintenance plan master data, where we have naturally organised our data to support effective execution. The problem is that a data structure that supports effective execution of maintenance creates complexity and disparity in the understanding of the task to failure mode relationship.

A simple example is a route-based maintenance plan where a collection of tasks on like assets are grouped to a route. This “route-based” plan is likely attached in the EAM system to a parent level in the hierarchy. There could be references to assets or perhaps even components on the plan – but the very nature of this type of plan means when we are analysing data associated with single failure modes – it is complex to put together the historical picture. If we are layering data analytics over this type of data – our confidence in any conclusions would be low.

Similarly, if a company doesn't have a thorough understanding and structured record of the causes of asset failure, or the current condition of those assets, any software or technology intended to monitor or predict failures will be less effective. To truly optimize asset performance, it's essential to have a solid foundation of accurate data and a deep understanding of the underlying factors driving asset performance.

Implementing more sophisticated technology may seem like a quick fix for asset management problems, but without the right data and insights, it's unlikely to improve performance. The current temptation and alure of generative and agentic A.I. is that we can ask questions or automatically generate recommendations – of course the responses or recommendations will be based on the data available to the system. If the data is incorrect or incomplete, the response can be misleading.

In fact, it could even make things worse by creating a false sense of security and leading to overconfidence in systems that may not be accurately reflecting the true state of assets.

So, what's the solution? To optimize asset management, it's crucial to start by getting the basics right. This means establishing a thorough and accurate inventory of all assets, understanding the root causes of asset failure and developing a maintenance strategy accordingly, and using data-driven insights to continuously optimize performance.

It is critical to recognise that data is a process not a project. As soon as a one-of data cleanse project is complete it starts to go out of date and become inaccurate. Any asset management process implemented must account for and ensure that changes in data are captured and updated in relevant systems.

In conclusion, while technology can certainly be a powerful tool for asset management, it's essential to remember that it's only as effective as the data it's built on. Layering more technology on top of incorrect data is unlikely to solve the underlying problems with asset management and may even make things worse. Instead, companies should focus on establishing accurate data, process that maintain it, and deep insights into their assets to truly optimize performance.

By Jason Apps

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Jason Apps is an Executive Level Asset Management Consultant, providing support to organisations in pursuit of high-reliability world-class asset management practices. He is the Author of ASMx: Asset Strategy Management - A Leaders Guide to Reliability Transformation in the Digital Age, a regular presenter, workshop facilitator and trainer.

Jason has delivered significant performance improvement, cost reduction, and risk management to global, blue-chip clients, for the last 20+ years. With a proven, unique, pragmatic approach to identifying improvement initiatives, implementing and structuring for enduring success.

Email Jason at Jason.apps@exar-am.com or visit exar-am.com