Many businesses can find themselves overwhelmed with data. Asset managers can also feel inundated with data. Collating vast amounts of data into asset registers or folders is one way to store and protect the valuable information. But without linking into an asset management strategy that supports long-term business goals, your business will only leak value and increase risks. Too much data and incorrect use makes the business data rich, but intelligence poor.
But you want your business to be data rich and intelligence rich.
The capability of the IoT means that it’s now—more than ever—easier to leverage data to build value in your business. Maintenance of large engineering assets is more streamlined with machine learning techniques. Digital implementation can mean less time is wasted among asset operators and managers when it comes to record-keeping. The risk of asset downtime and failure is lowered when data is utilised and a planned, methodical approach is used to scheduling1.
If you want to know more about data in asset management, make sure you connect with our Data in Asset Management Special Interest Group for all the latest knowledge, advice and networking opportunities. Contact Julian.Watts@amcouncil.com.au for more information.
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1 Info in this article sourced: https://www.ey.com/en_gl/digital/why-digital-asset-management-is-a-fundamental-driver-of-utility
Many businesses can find themselves overwhelmed with data. Asset managers can also feel inundated with data. Collating vast amounts of data into asset registers or folders is one way to store and protect the valuable information. But without linking into an asset management strategy that supports long-term business goals, your business will only leak value and increase risks. Too much data and incorrect use makes the business data rich, but intelligence poor.
But you want your business to be data rich and intelligence rich.
The capability of the IoT means that it’s now—more than ever—easier to leverage data to build value in your business. Maintenance of large engineering assets is more streamlined with machine learning techniques. Digital implementation can mean less time is wasted among asset operators and managers when it comes to record-keeping. The risk of asset downtime and failure is lowered when data is utilised and a planned, methodical approach is used to scheduling