Data Valued

By Anmut

Water companies already apply asset management rigour to pipes, pumps, and treatment works. The same discipline, applied to data, would change how they plan investment, defend regulatory submissions, and absorb the demands of AMP8. Here is how to get started.

The decision-grade problem

Every capital and operational decision a water company makes rests on data. Which assets need renewal, and when? Where is leakage occurring, rather than where models estimate it to be? Can the performance figure submitted to Ofwat be traced back to a source system, and would it withstand challenge? These are not abstract information-management questions. They are the questions that determine ODI outcomes, capital allocation, and regulatory standing.

The problem is familiar to anyone working in the sector: data quality is inconsistent, data ownership is unclear, and the lineage from operational system to regulatory submission is frequently undocumented. Asset registers are incomplete. SCADA data sits in siloed platforms. Environmental monitoring records contain gaps. When Ofwat queries a reported metric, or when the Environment Agency scrutinises a discharge record, companies must assemble evidence trails that were not always built with that purpose in mind.

This is not primarily a technology problem. It is a governance and prioritisation problem - and it is one that asset management thinking is well positioned to solve.

Data and physical assets have more in common than you might expect

Consider a clean water network. Insufficient investment in physical assets - mains, valves, pumps - leads to degradation, causing leaks, bursts, and supply interruptions. The performance impact is visible and measurable. But the same causal chain operates through data. As asset condition records become outdated or inaccurate, renewal decisions are based on faulty information. Investment is misdirected: money spent on assets that did not need it, and assets that did go unaddressed. Different mode of failure, identical stakeholder impact.

The parallel extends further. Physical assets and data assets both require upfront investment to deliver value over time. Both move through comparable lifecycles - from planning and creation through operation to eventual disposal or retirement. Both need governance, management, and monitoring to remain fit for purpose.

The critical difference is how they are treated. For physical assets, these disciplines are mature and embedded. Asset management frameworks, condition assessments, whole-life cost modelling - the practices are well established and leadership understands their importance. For data assets, the equivalent disciplines are often informal, fragmented, or absent.

A recent Anmut project illustrates the point. Of a £149m AMP7 sewer level monitoring programme, £53m of value depended on data quality - not hardware. EDM sensors were generating spurious records, requiring significant manual edits per month for the purposes of submissions. The investment in sensors was real. The governance of what those sensors produced was not.

Why the absence of formal accounting treatment matters

Data does not appear on the balance sheet. This absence has practical consequences for how it is treated:

• It receives less visibility in board discussions and investment planning than assets that do appear on the balance sheet
• Accountability and ownership are frequently unclear - data sits between functions rather than being owned by any one of them
• There is no established mechanism to represent the value data creates, or the cost when it fails
• The data asset lifecycle is managed far less rigorously than physical assets, with no equivalent of condition assessment or whole-life cost modelling
• Auditors and regulators focus on reported outputs rather than the quality of the underlying data processes

Whether or not data eventually receives accounting treatment, the implication is the same: its value and cost must be accounted for in how decisions are made, resources allocated, and people held to account. The absence of a line on the balance sheet does not change the fundamental reality that data assets are critical to regulatory performance and capital efficiency.

In AMP8, this matters more than it ever has. The Cunliffe Review points toward mandatory external audit of regulatory submissions and automated real-time reporting - removing the correction window that currently absorbs much of the sector's compliance risk. The time available to fix data quality problems after the fact is narrowing.

Asset management principles apply directly to data

The reasons to apply asset management thinking to data are the same reasons it is applied to physical infrastructure.

It maximises value and minimises cost of ownership. Asset management is fundamentally about optimisation - getting the most value from assets while managing the full lifecycle cost. Data requires the same thinking. What is the total cost of ownership of a key data asset? It is not just the cost of the system that collects it. It includes ongoing quality assurance, storage, integration, correction, and the cost of decisions made on inaccurate information. On the value side: what regulatory outcomes does this data enable, what ODI exposure does it carry, what capital decisions does it inform?

It gives clarity on outcomes, not just outputs. Asset management distinguishes sharply between outputs and outcomes. "We cleansed the asset register" is an output. "We can now defend our leakage submission if challenged by Ofwat" is an outcome. Too many data initiatives are defined by what they produce rather than what they enable. The water sector's regulatory framework provides unusually clear outcome anchors - ODI metrics, June Return submissions, EA discharge records – giving strong reasons to prioritise investment in data asset management – upon which these all depend.

It takes a whole-life perspective. A piece of infrastructure is a decades-long commitment of maintenance, inspection, repair, and eventual renewal. Decisions made at design stage affect cost and performance for the full asset life. Data demands the same perspective. The standards set for how data is captured today, what governance is applied, and how it is integrated determine whether it remains usable and auditable across multiple AMP cycles. Short-term decisions about data architecture have long-term consequences.

It breaks down silos. Physical asset management links strategy, planning, operations, and performance management into an integrated approach. Data in water utilities is typically anything but integrated: engineering, operations, finance, and customer teams manage separate data estates with limited cross-functional governance, creating inconsistency and gaps at the boundaries between functions. Applying asset management discipline to data requires the same integration across functions.

It relies on culture and leadership. The best asset management framework fails without leadership commitment and a culture that values long-term thinking. Data is no different. It requires leadership to elevate data from a technical concern to a business priority, and to embed accountability for data quality into everyone's role - not just the data team's.

The governance gap

If the principles are clear andthe parallels evident, what is getting in the way?

The core problem is a governance gap. Business leaders do not treat data the way they treat physical assets.

Consider the analogy: you would not delegate infrastructure maintenance solely to civil engineers. You maintain strategic oversight, set standards, make investment decisions, and hold people accountable for asset performance. Engineers provide essential technical expertise, but you own the asset management decisions because you are accountable for the outcomes. So why is data leadership routinely delegated to IT?

IT provides essential technical capability, but it should not be solely responsible for determining which data assets are strategically important, how they should be governed, which standards apply, or how to prioritise investment. These are business decisions with business consequences - in water utilities, directly financial consequences through ODI exposure and regulatory enforcement.

Without proper stewardship, companies cannot build compelling investment cases for data improvement, cannot demonstrate due diligence to regulators, and cannot optimise performance against commitments. The quality of strategic decisions is constrained by the quality of the data assets underpinning them, yet those data assets receive a fraction of the management attention.

At an Australian water utility, meter errors were indistinguishable from real leaks - and the organisation was considering premature desalination investment worth billions of dollars. A data valuation exercise showed that AUD $1.8m in data investment could unlock over AUD $20m in value across five years: capex avoidance, water loss savings, and productivity gains. The infrastructure decision was being driven by a data problem, not a physical one.

Where to start

The asset management mindset is already present in water. The task is to extend it to data.

Start by identifying the data assets most critical to regulatory outcomes. Which data feeds the ODI metrics carrying the greatest financial exposure? Which submissions face the highest challenge risk? Which capital decisions are currently being made on incomplete or estimated information? Map those connections and understand the value atstake.

Then assess current condition -the data equivalent of a condition inspection. Where are the gaps in coverage, the undocumented lineages, the manual workarounds? Where is data ownership unclear? Where do transformation programmes risk introducing new quality problems?

From that baseline, apply the same governance, investment, and accountability frameworks used for physical assets. Set standards. Assign clear ownership. Establish audit trails. Build whole-life cost and value thinking into data investment decisions rather than treating them as one-off projects.

The diagnostic questions to ask are straightforward:

• How are ODI metrics currently compiled - and can the team trace each number back to its source system?
• What happened the last time Ofwat challenged a reported performance figure? How was the evidence assembled?
• Where in the journey from operational system or technology to regulatory submission are the biggest gaps or manual workarounds?
• Who owns the data that feeds the ODI reports, and how is governance structured across the organisation?

The question is not whether, but when

AMP8 is the largest investment cycle the UK water sector has seen in a generation. It arrives alongside a regulatory environment that is becoming more demanding, not less. The Cunliffe Review, tightening EA enforcement, and Ofwat's increased scrutiny of data quality in business plan assessments all point in the same direction: the cost of poor data stewardship is rising, and the window for absorbing it informally is closing.

The principles of data asset management are proven. The business case is quantifiable. The sector already has the mindset - applied with rigour to physical infrastructure every day. What is needed is the decision to apply the same rigour to data.

About Anmut

Anmut helps organisations treat data as an economic asset -making its value explicit, measurable, and actionable. Anmut conducted the UK water sector's first quantified, asset-level data valuation and work across critical national infrastructure. For further information, visit anmut.co.uk.

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