Data quality, validation and standards
Build confidence in the spatial data behind your decisions.
InfoSpatial assesses datasets, asset registers and working practices to identify weaknesses, define priorities and establish a practical route to more reliable delivery.
A clear baseline
Understand the data before deciding how to improve it.
Data quality problems are rarely limited to missing values or geometry errors. They can involve unclear lineage, inconsistent structures, duplicate records, weak relationships, unsuitable validation rules and uncertainty over ownership.
We examine the data in context, including how it is created, maintained and used. The outcome is a prioritised view of the issues and a proportionate improvement approach.
Potential outputs
- Data quality and completeness assessments
- Validation frameworks and quality assurance rules
- Asset register reviews
- Data standards and specifications
- Governance and ownership recommendations
- Prioritised improvement plans
- Target data models and implementation roadmaps
How the work develops
From evidence to practical improvement.
The scope should reflect the importance of the data, the decisions it supports and the organisation’s ability to implement change.
Assess
Review datasets, documentation, rules, workflows, ownership and known problems.
Prioritise
Separate critical risks from lower-value issues and define proportionate acceptance criteria.
Improve
Create the rules, standards, plans or technical processes needed to increase confidence.
Establish what you have and what needs to change.
You do not need to know the exact assessment or deliverable required before getting in touch.
Start with a data assessment