enRichMyData

High-quality, rich and meaningful data are crucial to successfully implementing Artificial Intelligence (AI) and Big Data Analytics (BDA) solutions. Delivering required data to feed into AI and BDA models is costly, difficult, and often limited in data and skill availability. It is well known that up to 80% of the effort spent in AI and BDA projects is dedicated to ensuring data is fit for purpose. Activities are required to discover, understand, select, clean, transform, and integrate data from a variety of sources in such a way that data can be fed into the modelling phase. Such activities result in enriched data, eventually improving the quality of downstream BDA and AI applications. The data enrichment process is implemented by specifying, deploying, and executing data enrichment pipelines over data that can be structured, semi-structured and unstructured, in large amounts, and from static or streaming sources. While techniques exist to cover different enrichment operations such as data cleaning, linking, feature extraction, classification and semantic annotation, etc., the lack of comprehensive approaches and established tools dedicated to data enrichment makes the definition, implementation, and operation of enrichment pipelines difficult for too many organizations willing to improve their BDA and AI applications.

The overall vision of the enRichMyData project is to create a novel paradigm for building rich, high-quality, valuable, and FAIR-compliant datasets to feed downstream BDA and AI applications in the context of data-sharing ecosystems, such as data spaces. The paradigm facilitates the specification and execution of data enrichment pipelines, focusing on supporting various data enrichment operations. enRichMyData makes this easily accessible to a wide set of large and small organizations that encounter difficulties in delivering suitable data to feed their BDA and AI solutions due to the lack of usable tools/expertise for the cost-effective management of data enrichment pipelines.