Over the past couple of years COVID has created pressure on costs for many industries. Organisations have been forced to pivot, repurpose their assets and re-evaluate investments. For asset managers, driving efficiencies and trying to maximise and optimise asset utilisation have become top priorities.
Many are turning to digital platforms that use AI to generate smart insights and shape asset management strategies. Sophisticated algorithms can analyse an asset’s history, its current condition and utilisation rate, and accurately predict future maintenance requirements. This has a huge cost savings potential, whether from avoiding unnecessary maintenance or preventing expensive outages by fixing issues before critical failure occurs.
But to do this, good data is needed. This means data that’s reliable, accurate, relevant and up-to-date. Unfortunately for many asset managers, existing data may not be available in one place. It may be siloed in different repositories or stored in different spreadsheets, or even in physical registers. It may be patchy or incomplete. The problem is that you can’t make good decisions based on bad data. If you put garbage in, you get garbage out.
The problem of "garbage" data
Without reliable data you can’t make the most strategic decisions or plan budget accurately. If you don’t know a building’s utilisation rates and current state of repair, it’s very difficult to predict its maintenance costs and plan appropriate maintenance. This has knock-on effects, because if an asset fails completely through poor maintenance, the disruption and replacement can be very expensive.
Data may also be incomplete due to a database that isn’t linked, or not having captured the data you need to make the best decisions. There may be new sites and completely new buildings where accurate forecasts are needed, but they’re not yet recorded. You may have replaced an asset or it’s damaged, but this isn’t updated in your asset register.
Even good data can "go bad" if it’s not maintained. Older data quickly uses relevancy and can’t be used to make reliable forecasts. The pandemic lockdowns have resulted in permanent changes to utilisation patterns, making historic usage trends redundant. We don’t yet know how buildings will be used in future compared to how they were used pre-COVID. Everything has changed. A robust data policy should include retiring/deleting data that’s no longer useful. Otherwise, poor data can ripple across an organisation, causing:
· Large unforeseen capital expenditure occurrences to replace failed assets
· Increased Work, Health and Safety (WH&S) risks
· Design of assets which aren’t "fit for purpose"
Getting good data with AssetFuture
At AssetFuture we take a multi-pronged approach to getting good data.
AssetCapture is a mobile app that enables rapid and quality-controlled asset condition assessments. It’s used by our internal assessment teams and our customers’ teams for condition assessments of their asset portfolios.
File Templates enable the easy import of data from other CMMS/WMS/AMS systems. This means all your data ends up in the same place so you have single source of truth. It also allows unstructured data to become structured into an Asset Register that can then be used to create better forecasts and Asset Management plans.
Condition Assessment teams can help with acquiring the data from sites and turning the raw data into a validated asset register.
Additionally, our API Library will allow customers to import/export data from/to other systems. So if owned systems are updated, data will also be updated in AssetFuture.
Data is the lifeblood of effective asset management. Better data = better decisions = better cost efficiencies and lifecycle optimisation. Without clear insight and visibility of data generated from each stage of an asset’s life, future decisions and ongoing strategy may be made poorly.
At AssetFuture, we’re experts in helping organisations compile accurate, up-to-date asset registers with the high-quality data they need for better planning and decision making. If you’d learn more about how data is used in asset management, click here.