GIS Data Quality
Producing Better Data Quality through Robust Business Processes
Due to the growth of the internet, the explosion and proliferation of geographic data is providing users with wider access to this information, though it is not necessarily of the best quality.
As demand for real-time information increases, the quality of data is increasingly of central importance and concern within the GIS user community, as the data that is used and produced impacts directly on the quality of decisions made.
Data generated from different sources might utilise different techniques and may sometimes be presented differently to its original source or purpose thus leading to discrepancies, especially upon integration. In other words, data that is appropriate for use with one application may not always be fit for purpose with another. And as data sets from different sources are integrated, difference in scale, accuracy, purpose and extent, as well as inherent errors can yield inconsistencies and further lack of accuracy, integrity, validity and auditability.
Thus to ensure business decisions are effective and valid, underlying GIS data needs to be accurate, current, consistent, complete and well maintained. GIS Data Quality – Producing Better Data Quality through Robust Business Processes is designed to assess data quality issues in GIS through discussion and the development of robust processes that support accurate collection, allocation, standardisation and management of geospatial data to support business decisions. This hands-on and interactive course will include NZ and international case studies.
SEVEN GREAT REASONS TO ATTEND THE COURSE
1. Gain more in-depth understanding of the quality requirements and components of data quality
2. Improve data quality through the refinement of processes and outputs of data
3. Learn how to assess quality and how to enhance it through standards
4. Understand the importance of metadata in the context of GIS
5. Implement successful GIS projects through the development of a quality assurance programme and plan
6. Develop and drive business processes to support quality data
7. Be more sensitive to potential limitations of GIS to achieve immaculacy
As demand for real-time information increases, the quality of data is increasingly of central importance and concern within the GIS user community, as the data that is used and produced impacts directly on the quality of decisions made.
Data generated from different sources might utilise different techniques and may sometimes be presented differently to its original source or purpose thus leading to discrepancies, especially upon integration. In other words, data that is appropriate for use with one application may not always be fit for purpose with another. And as data sets from different sources are integrated, difference in scale, accuracy, purpose and extent, as well as inherent errors can yield inconsistencies and further lack of accuracy, integrity, validity and auditability.
Thus to ensure business decisions are effective and valid, underlying GIS data needs to be accurate, current, consistent, complete and well maintained. GIS Data Quality – Producing Better Data Quality through Robust Business Processes is designed to assess data quality issues in GIS through discussion and the development of robust processes that support accurate collection, allocation, standardisation and management of geospatial data to support business decisions. This hands-on and interactive course will include NZ and international case studies.
SEVEN GREAT REASONS TO ATTEND THE COURSE
1. Gain more in-depth understanding of the quality requirements and components of data quality
2. Improve data quality through the refinement of processes and outputs of data
3. Learn how to assess quality and how to enhance it through standards
4. Understand the importance of metadata in the context of GIS
5. Implement successful GIS projects through the development of a quality assurance programme and plan
6. Develop and drive business processes to support quality data
7. Be more sensitive to potential limitations of GIS to achieve immaculacy
