Asset Management
Make better asset decisions from better evidence
Bring asset, maintenance, performance and risk information together so teams can prioritise intervention, understand where risk is building and make investment decisions with greater confidence.
RGA improves the data foundations, analytics and decision-support tools behind asset management, from asset information and risk models through to investment planning and reporting.
Asset decisions become harder when the evidence is fragmented
Water companies hold large amounts of information about their assets, but the evidence needed to make a good intervention or investment decision is often spread across different systems and teams.
Asset registers, maintenance history, telemetry, GIS, failure records, inspection data and operational performance can each provide part of the picture. The difficulty is bringing those sources together consistently enough to understand condition, consequence and risk.
Where asset information is incomplete or structured differently across systems, teams can spend significant time reconciling data before it can be used. Important decisions can then depend heavily on local knowledge, isolated reports or whichever issue is most visible at the time.
That makes it harder to compare assets consistently, understand where risk is increasing and explain why one intervention should take priority over another.
The problem becomes more significant when investment decisions need to work across large portfolios. Better asset management depends on having information that is reliable enough to support repeatable decisions at scale.
What better looks like
Asset teams should be able to understand the evidence behind an intervention without first having to assemble it from multiple systems.
Core asset information should be connected with maintenance, failure, operational and spatial data so condition and performance can be viewed in context.
Risk should be assessed consistently, using the best available evidence about likelihood of failure, consequence, criticality and historical performance rather than relying on a single measure or the most recent problem.
Investment decisions should have a clear line back to the data and assumptions that support them, making priorities easier to explain, challenge and update when new information becomes available.
The underlying information should also improve over time. Data-quality gaps, inconsistent asset structures and missing evidence should be visible so they can be addressed rather than hidden inside individual analyses.
The result is a more consistent view of asset risk and performance, with investment and maintenance decisions based on evidence that can be traced and tested.
How RGA helps
We improve the information and analytical foundations behind asset decisions, helping teams move from fragmented asset data towards consistent risk, prioritisation and investment evidence.
Asset Data Foundation
Bring asset registers, maintenance history, telemetry, GIS and other relevant information into a governed, quality-assured view.
We can connect information from existing asset and operational systems, establish consistent structures and ownership, and identify gaps or quality issues that make analysis difficult. This creates a more reliable foundation for understanding asset condition, performance and history across the portfolio.
The objective is not to create another asset register. It is to make the information already held across the organisation easier to combine, trust and use.
Risk & Prioritisation
Target maintenance and renewal activity towards the assets carrying the greatest risk.
We can combine failure history, asset condition, criticality, consequence and other relevant evidence to support more consistent risk assessment and prioritisation. Analytics can help identify patterns, compare assets or groups of assets and highlight where risk is changing.
The approach should make the reasoning behind prioritisation visible, so teams can understand why an asset has been identified for intervention rather than relying on a score that cannot be explained.
Investment Assurance
Build a clearer evidence base behind investment planning and intervention decisions.
We can connect proposed investment to the underlying asset information, risk assessment and performance evidence, helping teams understand the assumptions and reasoning behind priorities. Reporting and decision-support tools can then make it easier to review options, challenge assumptions and maintain a traceable link between asset need, proposed intervention and investment decision.
The aim is to make investment decisions easier to evidence internally and under regulatory scrutiny without creating a parallel assurance process around the planning activity.
What this looks like in practice
The starting point is the asset decision that needs to be improved.
That may be deciding which assets need intervention, understanding why failures are occurring, comparing risk across a portfolio or strengthening the evidence behind an investment plan.
We work back from that decision into the information required to support it, identifying where the relevant asset, maintenance, operational, spatial and performance data is held and how reliable it is.
That can mean connecting existing systems into a governed data environment, aligning asset identifiers and hierarchies, improving data quality and creating consistent structures that allow information from different sources to be analysed together.
From there, analysis can be applied to understand failure patterns, condition, criticality and risk. Where the data supports it, forecasting or predictive models can also be used to identify where deterioration or failure is more likely to occur.
The outputs then need to fit the way asset decisions are actually made. That can mean portfolio reporting, prioritisation tools, investment dashboards or applications that allow teams to explore the evidence behind individual assets or intervention options.
The aim is not simply to create a better asset dataset. It is to create a stronger connection between the information the organisation holds and the decisions it makes about maintenance, renewal and investment.
Turning operational history into an evidence base for planning
Across a sewer network of around 40,000km, RGA brought eight years of operational history and around 65,000 maintenance calls a year into a connected view.
The work was developed to support predictive demand modelling, but the underlying data foundation also demonstrates an important asset-management principle: maintenance and operational history becomes much more useful when it can be considered together rather than held across separate records and systems.
Bringing that information together created a stronger evidence base for understanding patterns in demand and maintenance activity, and for supporting more informed planning.
Read the case study: Predicting weather-driven demand across a 40,000km network →
Where the same approach applies
The exact decision varies, but the same data and analytical foundations can support a range of asset-management activities.
Maintenance prioritisation
Use maintenance history, condition, criticality and performance information to focus attention on assets where intervention is likely to matter most.
Renewal planning
Bring asset need, failure history, risk and consequence together to support more consistent renewal priorities.
Asset performance
Connect operational and asset information to understand how assets are performing, where deterioration may be emerging and what factors are contributing to failure.
Investment planning
Create a clearer evidence trail between asset need, risk, proposed intervention and investment decision.
The common requirement is a reliable connection between the asset information held across the organisation and the decision that needs to be made.
Related services
Asset-management decisions depend on information and processes that often extend into operations, transformation and compliance.
Put better evidence behind asset decisions
If asset information is fragmented, prioritisation is difficult to explain or investment decisions depend on manually assembled evidence, we can help strengthen the data and analytics behind them.