Data Quality Explored
This section includes comprehensive information from expert users, analysts, implementers and the media about all aspects of Data Quality. This section contains rich presentations, research, analysis and guidance on Data Quality and is the most concentrated section of the Data Quality Resource Guide.
| Organizing for Data Quality |
| A rich, valuable look into data quality from an IT and organizational perspective. Includes specific guidance on data quality business drivers, impact, business case, use cases and methodologies. Highly recommended. From the Resource: "Make data quality a business problem, not an IT problem." |
| Andreas Bitterer, Gartner |
| Assessing Data Quality |
| As part of adopting a data quality framework, the Bank of England shares the following definitions of data quality dimensions: 1) Relevance: Relevance is the degree to which statistics meet current and potential users' needs. 2) Accuracy: Accuracy in the general statistical sense denotes the closeness of computations or estimates to the exact or true values. 3) Timeliness and Punctuality: Timeliness reflects the length of time between availability and the event or phenomenon described. Punctuality refers to the time lag between the release date of data and the target date when it should have been delivered. 4) Accessibility and Clarity: Accessibility refers to the physical conditions in which users can obtain data. Clarity refers to the data's information environment including appropriate metadata. 5) Comparability: Comparability aims at measuring the impact of differences in applied statistical concepts and measurement tools/procedures when statistics are compared between geographical areas, nongeographical domains, or over time. 6) Coherence: Coherence of statistics is their adequacy to be reliably combined in different ways and for various uses. |
| Bank of England/Eurostat |
| How to Build A Compelling Business Case For Data Quality |
| A data- and research-intensive presentation on why data quality matters, strategies for data quality management and how to make the business case for a data quality initiative. |
| Prof. Dr. Peter Chamoni, University of Duisburg-Essen |
