Conventional data analytics utilizes dashboards, visualizations, search, and other tools to determine appropriate data for integrated, targeted use cases. Smart data analytics 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.
Bolstered by a number of semantic technologies, smart data analytics methods consistently streamline, quicken, and improve outcomes for more effective insights. The result is seamless analysis that is virtually interchangeable with analytics itself.Wonder which is better?
Whether feeding applications or analytics, the effectiveness of smart data analysis 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 lets end users know how data relates to their questions while perceiving relationships 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 insights.
The rapidity of smart data analytics 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 required by other approaches. Therefore, the smart data analytics process allows for less time for maintenance and more for querying. 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 analytics 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 analytics 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".