The Smart Data Blog

Smart Data Discovery

Posted by Patrick Wall on Aug 2, 2016 12:30:00 PM

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Conventional data discovery utilizes dashboards, visualizations, search, and other tools to determine appropriate data for integrated, targeted use cases. Smart data discovery techniques, on the other hand, leverage linked data graphs, comprehensive data models, and a semantic standards-based approach to publish results to those same popular tools.

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Wonder which is better?

Bolstered by a number of semantic technologies, smart data discovery methods consistently streamline, quicken, and improve outcomes for more effective insights. The result is a seamless discovery process that is virtually interchangeable with analytics itself.

More Effective
Whether feeding applications or analytics, the effectiveness of smart data discovery methods is unprecedented for two reasons. The first is the foundation provided by semantic graph databases, which store data on an RDF graph. These graphs concentrate on the edges between nodes and their relationships. By linking enterprise or departmental data according to use case, this framework tells end users how data relates to their questions while perceiving relevance other users might not. The propensity to determine such a nuanced understanding of data at a granular level is augmented by semantic modeling, which encompasses all data formats, structures, and uses. These uniform semantic models standardize data in RDF, which enables the latter to pinpoint relationships between data based on consistent metadata, semantics, and attributes. Consequently, users can understand data for more meaningful analytics.

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More Rapid
The celerity of smart data discovery methods is underpinned by the preceding models. Their mutability encompasses all facets of data use and standardizes rules for deployments as these requirements arise - without the lengthy remodeling periods necessitated by other approaches. Therefore, the smart data discovery process involves less time for maintenance and more for question-asking. By asking more questions in less time, users can leverage the answers of previous queries to inform future ones while facilitating increased comprehension of data’s overall utility. Significantly, those answers are issued within the context of specific data-centric applications.

Commensurate with Analytics
Smart data discovery methods provide highly contextualized results for the most tailored use cases. They are predicated on an expeditiousness and efficacy that rivals the results of traditional analytics. The true worth of the semantic technologies that facilitate this process is that they include all data and their requirements, and are not circumscribed by typical schema or technological limitations. When deployed at scale across a variety of data types, smart data discovery techniques provide a detailed expression of data’s meaning - and how to exploit it.

To learn more about smart data, watch the on-demand webinar "Semantic Graph Databases: The Evolution of Relational Databases".

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Topics: Smart Data, Semantics, Semantic Web