Today most enterprises in their endeavor to be data-centric, face challenges like disconnected and heterogeneous data; legacy infrastructure that cannot scale; and complex data that analysts are unable to utilize. Ontology can help an enterprise overcome these challenges.
Technically, an ontology is a set of statements. Each statement is triples made up of a subject, predicate, and object. These triples serve to express data and the meanings of the terms in a vocabulary.
An ontology provides a universal language with unique identifiers to counteract the siloing of data. Furthermore, once there is an ontology in place, translating each data source is possible, thereby ensuring that a single query will cover all the sources.
When an ontology is first developed, adding a new data source requires building out the model to include uncovered concepts. As it matures, the ontology becomes easier to use and connect with new sources.
Moreover, ontologies make data more accessible by being both machine and human-readable by allowing queries in natural language. Allowing querying of where and when ontologies free up analysts to focus on interacting with the data rather than being entangled with database software.
Ontologies become more valuable with time. The more fluent an analyst becomes with the vocabulary of an ontology, the better the person will be in using it. Furthermore, because the ontology is format agnostic, enterprises do not get locked into other particular data structures.
In conclusion, ontologies allow enterprises to unlock the power of data without becoming mired by it. An ontology gives a universal language that can connect data, quickly adapt to changes in the data, and lower the barrier to entry to interact with data.
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