  Data-driven analysis is important in virtually every modern organization.
Yet, most data is underutilized because it remains locked in silos inside of
organizations; large organizations have thousands of databases, and billions of
files that are not integrated together in a single, queryable repository.
Despite 40+ years of continuous effort by the database community, data
integration still remains an open challenge. In this paper, we advocate a
different approach: rather than trying to infer a common schema, we aim to find
another common representation for diverse, heterogeneous data. Specifically, we
argue for an embedding (i.e., a vector space) in which all entities, rows,
columns, and paragraphs are represented as points. In the embedding, the
distance between points indicates their degree of relatedness. We present
Termite, a prototype we have built to learn the best embedding from the data.
Because the best representation is learned, this allows Termite to avoid much
of the human effort associated with traditional data integration tasks. On top
of Termite, we have implemented a Termite-Join operator, which allows people to
identify related concepts, even when these are stored in databases with
different schemas and in unstructured data such as text files, webpages, etc.
Finally, we show preliminary evaluation results of our prototype via a user
study, and describe a list of future directions we have identified.
