Use cases
Data quality and compliance for drinking water networks
Dotic's solutions combine two complementary areas of expertise: a deep understanding of water utility operations and advanced capabilities in asset data management, GIS and data quality assurance.
Water utilities and local authorities rely on this data every day to locate assets, carry out network operations, manage incidents and plan maintenance activities.
In practice, however, this information is often heterogeneous. It may originate from multiple sources — including construction projects, field surveys and historical records — and can contain inconsistencies, location errors or incomplete asset attributes.
In addition, organisations often use different data models, standards and data management practices, which can lead to variations in data quality and consistency.
In this context, data quality assurance becomes essential to maintaining a reliable information system and ensuring the safe and efficient operation of the network.
Critical data for effective drinking water network management
Unlike many other utility networks, asset data in drinking water systems is directly linked to the delivery of an essential public service.
This information enables organisations to:
- locate pipelines and service connections
- identify valves and isolation assets
- respond rapidly to leaks and network incidents
- manage network pressure effectively
- plan maintenance and renewal activities
Inaccurate or incomplete data can lead to operational errors, longer response times and, in some cases, service disruptions.
For water utilities, reliable asset information is not simply a convenience—it is a fundamental requirement for safe, efficient and resilient network operations.
Data that is often fragmented and inconsistent
In many drinking water infrastructure projects, asset data originates from a wide range of sources, including:
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legacy asset records
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as-built drawings and project documentation
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field surveys and inspections
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data delivered by contractors and engineering firms
These datasets are often produced using different methods, formats and data management practices.
As a result:
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data may be inconsistent across systems
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information can be incomplete or outdated
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asset records may not follow the same structure or level of detail
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overall data quality can vary significantly between sources
The outcome is often a fragmented asset database that is difficult to maintain, analyse and use effectively for operational and asset management purposes.
Improving data consistency and standardisation is therefore a key step in building a reliable foundation for network operations and long-term asset management.
Identifying data quality issues in drinking water networks
Data quality control helps identify issues that can affect the operation and management of drinking water networks.
Common data quality issues include:
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inaccurate pipeline locations
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inconsistencies in service connection records
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missing or incorrect asset attributes
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incorrectly recorded network assets such as valves, hydrants and other critical equipment
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inconsistencies, duplication or incomplete asset information
These issues are not simply technical concerns. They can have a direct impact on operational efficiency, maintenance planning and the ability of field teams to respond quickly and effectively when incidents occur.
Identifying and resolving data quality issues is therefore essential to maintaining a reliable and operationally effective asset information system.
Verifying compliance with asset data standards
Asset data standards play a key role in ensuring that drinking water network information can be shared, integrated and used effectively across operational and asset management systems.
In practice, however:
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data is not always produced according to consistent standards
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asset structures may be incomplete
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critical information can be missing or inconsistently recorded
As a result, asset data often needs to be validated and standardised before it can be integrated into GIS and asset management platforms.
With Dotic’s solutions:
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asset data is analysed using ConnectControl based on configurable business rules and data quality requirements
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compliance issues and inconsistencies are identified automatically
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data quality issues can be classified and prioritised according to their operational impact
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teams gain clear visibility of overall data quality and compliance levels
This approach helps organisations build a consistent, reliable and interoperable asset information system, even when working with data from multiple sources and stakeholders.
Automating asset data quality control
Manual validation of drinking water network data is often time-consuming, complex and difficult to maintain consistently over time.
With Dotic’s solutions:
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asset data can be validated automatically at scale using ConnectControl
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inconsistencies and data quality issues are detected systematically
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validation rules are applied consistently and can be reused across projects
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data quality reports are generated automatically
The results can be visualised and analysed within ConnectServices, enabling teams to pinpoint data quality issues directly on the network map.
This automated approach reduces the time required for data validation while improving the consistency, reliability and overall quality of asset information.
Connecting data quality control with field reality
Effective data quality control cannot be disconnected from field reality.
With Dotic’s solutions:
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data collected through ConnectField is integrated directly into the system
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discrepancies between asset data and actual field conditions are identified quickly
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corrective actions can be initiated without delay
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field and office teams work from a shared data foundation
This continuous connection helps improve the overall consistency and reliability of asset data.
An integrated step in the drinking water network management lifecycle
Data quality control is a fundamental part of effective drinking water network management, helping organisations maintain reliable asset information and support efficient operations.
In an environment where asset data is collected from multiple sources and managed through different processes, establishing a structured and automated approach to data validation is essential for maintaining consistency, accuracy and long-term data quality.
By combining automated validation, GIS integration and field-connected workflows, water utilities can improve the reliability of their asset information, support more effective operational decision-making and strengthen control over their network assets throughout their lifecycle.
Managing defects and corrective actions
- Identify defects and track corrective actions through to resolution
Planning and tracking field interventions
- Identify defects and track corrective actions through to resolution
End-of-work acceptance and handover validation
- Validate work completion and identify issues directly in the field
