Data Integration has been an active area of computer science research for over two decades. A modern manifestations of data integration can be seen as Knowledge Graphs which integrates not just data but also knowledge at scale. Tasks such as conceptual modeling, schema/ontology matching, entity matching, data quality among others are fundamental in the data integration process. Research focus has been to study the data integration phenomena from a technical point of view (algorithms and systems) with the ultimate goal of automating these task.
In the process of applying scientific results to real world enterprise data integration scenarios to design and build Knowledge Graphs, we have experienced numerous obstacles. In this talk, I will share insights about these obstacles. I will argue that we need to think outside of a technical box and further study the phenomena of data integration with a human-centric lens: from a socio-technical point of view.