Recommendation 13

Recommendation 13: Manage location data quality by linking it to policy and organisational objectives, assigning accountability to business and operational users and defining what “fit for purpose” means and implies


  • Recent research indicates that poor data quality is costing organisations an average of €8.4 million per annum and this is likely to worsen as information environments become increasingly complex
  • Improved data quality is a primary source of value for many IT-enabled business initiatives. On the other hand, research shows that 40% of the anticipated value of all business initiatives is never achieved. Poor data quality in both the planning and execution phases of these initiatives is a primary cause. Poor data quality also affects operational efficiency, risk mitigation and agility by compromising the decisions made in each of these areas
  • INSPIRE is creating a data infrastructure where we can anticipate reuse of the data. Public administrations are publishing open data. Same data is reused in many circumstances, and there is a need for a balanced approach to managing data quality and metadata across different EU Member States to support effective reuse
  • Managing data quality with a common approach/framework will enable a seamless exchange of data between different public service providers reusing this data. This can be done when administrations share their data through a common service for example
  • Managing data quality with a common approach will also enable the exchange of data between data providers. These can define “fitness for purpose” quality levels which include frequency of updates, produce data of a specific level of quality/detail with the adequate level of resources and define appropriate licensing. Data providers can also contribute to and enhance each other’s data, thus sharing resources
  • Data quality has the potential to improve labour productivity by as much as a 20%
  • As more business processes become digitalised, data quality becomes the limiting factor for overall process quality


  • Determine what is meant by and what is needed in terms of data quality. The dimensions of data quality include timeliness, accuracy, completeness, integrity, consistency, compliance to specifications / standards / legislation, well-described etc
  • Achieving perfect data quality on all data quality dimensions (typically ranging from three to six but sometimes up to several hundred) is impossible to achieve at reasonable cost for most organisations. Therefore it becomes essential to instead clearly define what is meant by "fit for purpose" data quality. By initiating an ex-post evaluation of existing data quality issues against data quality best-practice guidance, an organisation can define what “good enough” data quality means and develop and apply a framework for analysis. This framework will enable common data quality language, better communication of issues, and less confusion and better positioning of governance
  • Establish a clear line of sight between the impact of data and data quality improvement. This can be best achieved by:
    • Identifying the application systems and external services that produce data to support business activities and policy making
    • Measuring conformance of data to quality parameters set out in the data policy on an agreed frequency
    • Assessing the current business value in terms of the existing data quality level, and engaging with relevant stakeholders to assess the value of improving specific data quality items
  • Use data profiling techniques early and often to assess data quality and present profiling results in a way that appropriate issues can be acted upon, identifying outliers, anomalies, cross-referencing errors, gaps etc. A useful approach is to design and implement data quality dashboards for critical information such as authentic data and to embed this as a business-as-usual IT process
  • Establish a data quality standard which also addresses multilingualism to ensure consistency and appropriateness in the way key enterprise data is applied and reported across the National and European Data Infrastructures
  • Data quality standards are linked to data standards; ensure completeness and adequacy of the metadata, this will support reusability
  • When using common metadata standards, agree among the different stakeholders on the meaning of each metadata field, this ensures semantic interoperability of data
  • Identify authoritative data and on-authoritative data using the quality framework, standardise the referencing of this authoritative/non-authoritative data for example with a specific metadata field in a common standard
  • Combine authoritative and on-authoritative data for enhancing public services but define a framework or use cases where this is allowed, so as not to create legal uncertainty or infringement in public service delivery
  • Allow the combined publication of authoritative data and non-authoritative data on common platforms so as to favour market places driving innovation in public services
  • Make data quality a recurring agenda item at the information governance steering group meetings to ensure the data quality improvement roadmap is aligned with the information governance vision and strategy
  • Establish data quality responsibilities as part of the information steward role
  • Establish a cross-unit or cross-organisation special interest group for data quality, led by the Information Management team or equivalent body
  • Establish a data quality review as a release management "stage gate" review process
  • Communicate the benefits of better data quality regularly to departments by benchmarking improvements with other similar organisations or creating a regular data quality bulletin and highlighting what could be achieved with better data quality management
  • Leverage external/industry peer groups by inviting them to present at special interest group meetings
  • Encourage feedback from users to report problems and help improve data quality. This process can be incorporated in licensing agreements
  • Use artificial intelligence (AI) techniques to make suggestions for improving data quality


  • Chief data officers (CDOs) and information management leaders continue to struggle with getting data quality onto their digital business agendas. This is often due to an overemphasis on enabling technology rather than a focus on organisational culture, people and processes
  • Few organisations attempt to use a consistent, common language for understanding business data quality. Instead, they maintain divergent and often conflicting definitions of the same logical data
  • Information leaders struggle to make data quality improvements beyond the level of a project and do not embed them at the programme level as part of their digital business information culture
  • Required data quality may come at a price that is not affordable
  • Drawing together data from multiple sources for analysis increases the possibility that effort will be needed to transform data to a form where it can be used

Best Practices:

Further reading:

Nature of documentation: Technical report


Type of document
European Union Public License, Version 1.1 or later (EUPL) 
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