Posted - Matt Beare: Last week Bonn hosted the largest attended
OGC technical committee meeting, with 230 delegates enjoying the hospitality of United Nations Environment Programme and the UN-SPIDER Bonn Office.
A packed agenda spanning the entire week afforded the community a platform to discuss and drive forward matters on the development of geoprocessing interoperability computing standards.
On a lighter side we experienced the customs of Women's Carnival - held on the last Thursday before Fastnacht, the locals amongst us (and others) were in festive mood and attire, with several men falling prey to women with scissors, and losing the ends of their ties. For my part, on Wednesday I chaired the
Data Quality Domain Working Group (DQ DWG).
The mission of the DQ DWG is to provide a forum for describing an interoperable framework or model for OGC Quality Assurance measures and Web Services to enable access and sharing of high quality geospatial information, improve data analysis and ultimately influence policy decisions.
An imperative consideration for many disciplines (as typified by
last week's blog) we gained three perspectives on the role and need for data quality knowledge in the earth observation community. These were followed by an academic perspective on how standards and data quality underpins a GeoInformatics curriculum. We concluded the meeting with an update on the status of the ISO 19157 draft standard for data quality, with a focus on how OGC comments had been received and acted upon by the ISO editorial committee.
May I take this opportunity to thank our five presenters (Joan Masó, Hervé Caumont, Dan Cornford, Ivana Ivánová and Marie-Lise Vautier) for excellent content in their presentations which together contributed to positive dialogue and proposals for future tasks for the DQ group:
- Engage with the GeoViQua project to assist in the development of the proposed GeoLabel concept to support the creation, search and visualization of quality information on EO data.
- Based on discussions around accuracy and uncertainty, document best practice examples in the application of the guidance offered in the forthcoming ISO 19157 standard.
- Develop understanding and guidance on the role of data quality control in data processing tasks such as schema transformation and generalisation, seeking to ensure the preservation of data integrity as it moves through the data supply chain.
One discussion that I found particularly relevant is whether data has to be of high quality or known quality? As part of any data improvement programme, striving to achieve the highest possible quality data for a particular purpose with the resources available will always be a key objective for data providers. However, fit for one purpose may not be of use for another purpose, and vice-verse. Indeed, in the age of the SDI and concepts like
Linked Data the aim is to publish data to be used for unknown purposes (as highlighted by this video that one OGC participant pointed me at a
YouTube video. So for the user community, having knowledge of the quality of the data and the types of purposes it can be used for is important. So there appears to be a growing consensus that known quality is more important than high quality. This is seen in initiatives like
GEOSS and
INSPIRE, where data quality is presented as being important, but equally critical is that no data should be deemed poor quality, for fear that this may act as an obstacle to an organisation’s willingness to publish and share data. Instead users desire access to as much data as possible, with documented guidance on its limitations of use and whether an element of caution should be applied to applications or decisions that are based on the data, and in turn this will enable informed decision making and business planning.
