Importance of Data Quality

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. As put by the Open Data Institute: “If you don’t think you have a quality problem with your data, you haven’t looked at it yet.” When talking about Linked Open Data quality the following aspects have been identified, by an extensive literature review done earlier by the authors, as being important. These quality aspects will come back in the different steps of the roadmap.

• Metadata: Name, description, publisher, locations, release, potential use, compliance, production date, provenance

• Data-set: Accessibility, format, kind of data, identifiers, use of vocabularies, semantics, data model, links, size, concise, complete, believability, reputation

• Data-records: Valid, complete, consistent, unique, timely, accurate, precise

• Process: Issues documented, process described, update frequency, support (official, community, tools)

• Availability: Rights, license/ fee, SLA, authenticity, security