ADEQUATe Open Data: Analytics & Data Enrichment to improve the QUAliTy of Open Data builds on two observations: An increasing amount of Open Data becomes available as an important resource for emerging businesses and furtheron the integration of such open, freely re-usable data sources into organisations’ data warehouse and data management systems is seen as a key success factor for competitive advantages in a data-driven economy.

The project now identifies crucial issues which have to be tackled to fully exploit the value of open data and the efficient integration with other data sources:

  • the overall quality issues with meta data and the data itself
  • the lack of interoperability between data sources

The projects approch is now to address this point already in an early stage – when the open data is freshly provided by either governmental organisations or others.

The ADEQUATe project works with a combination of data and community driven approaches to address the above mentioned challenges. This include 1) the continuously assessment of Data Quality of Open Data Portals based on a comprehensive list of quality metrics, 2) the application of a set of (semi)-automatic algorithms in combination with crowdsourcing approaches to improve identified quality issues and 3) the use of Semantic Web Technologies to transform legacy Open Data sources (mainly common text formats) into Linked Data.

So the project intends to research and develop novel automated and community-drivendata quality improvement techniques and then integrate pilot implentations into existing Open Data portals ( and  Furtheron a quality assessment & monitoring framework will evaluate and demonstrate the impact of the ADEQUATe solutions for the above mentioned business case.