Narrative's Rosetta Stone is pivotal in unlocking the full potential of data by translating the data contained within any dataset into a universal Data Catalog.
Narrative's Rosetta Stone is pivotal in unlocking the full potential of data by translating the data contained within any dataset into a universal Data Catalog. Narrative's Rosetta Stone functions like an interpreter for a relational database, taking varied data and converting it into a uniform, comprehensible structure so that anyone can query, join and consume that data predictably and without the need for external mapping or cleaning processes.
Attributes in Narrative's platform are the building blocks of data, akin to fields in a database record. They provide specific details about the data, including name, description, tags, validations, and type, providing structure and clarity to the data.
One example of an attribute is the Unique Identifier, an attribute of type “object”. Unique Identifier is composed of three fields: type, value, and context. These components work together to categorize and clarify each data point that is mapped to Unique Identifier.
Mappings play a pivotal role in standardizing data from various sources. They translate and normalize the data, ensuring consistency and interpretability for all collaborator and easing the process of data interaction and utilization. Each mapping links a specific attribute to a column in a dataset, allowing for clear data categorization. Each mapping also defines a transformation, ensuring that each data point is represented consistently with the definition of the attribute.
Rosetta Stone mappings are automatically generated using advanced machine learning tactics to identify, label and transform the data in a dataset into the appropriate format. (Users can also suggest mappings, in cases where automatically generated mappings fail or users wish to build their own, private mappings in addition to the system-wide mapping.