Unicorn is leading the IT revolution in which data is transformed into information.

Provided with semantics (meaning and context), data is elevated to information, thus becoming a more powerful and manageable resource for the enterprise as a whole, and for IT in particular.

Enterprise data is stored in thousands of incompatible databases, data formats, and legacy technologies that have accumulated over the years. As a result, corporations must depend on a fragile and complex data environment. This data landscape creates inefficiencies for data and integration engineers trying to understand and join together obscure data formats. Currently, engineers manually code and maintain point-to-point translation scripts (using unstructured languages such as SQL and XSLT).

Introducing semantics to corporate data provides a coherent platform for managing and integrating data. Having a clear understanding of the information embedded in the data eliminates the inefficiency and inflexibility of directly dealing with physical data. Interestingly, while semantics are being introduced into corporate data, the Web is also being reengineered as the Semantic Web led by the W3C.

Whereas current tools are limited to creating a data model for one specific database, semantics captures data’s meaning by referring to a rich Information Model reflecting business reality. Known as an ontology, this model describes the business entities, relationships, and rules of a knowledge domain by using an agreed-upon vocabulary that bridges between IT and the business. Multiple data sources and message formats are understood and accessed by mapping to a single ontology model. Mixed technologies and schemas are therefore easily integrated and compared.

There are three key steps to implementing semantics:

• knowing your data (by collecting metadata)

• knowing your business (by capturing information in an ontology model), and

• obtaining a strategic understanding of data (by applying semantics).

Not only do semantics provide a strategic view and understanding of one’s data, they can also be applied for active and immediate use in automatic, critical, and painstaking tasks of data management and integration. With the right platform, semantics can be used to support the entire data life cycle.