Revolutionizing Analytics with Semantic Data Lakes

Posted by John Rueter on Jan 25, 2016 5:34:00 PM

Recent developments in big data technologies have significantly impacted the prowess of contemporary analytics; the most profound of these involves the deployment of semantically enhanced semantic data lakes. These centralized repositories have revolutionized the scope and focus of analytics by enabling organizations to analyze all data assets with a specificity and speed that wasn’t previously available. The value derived from such an approach improves the analytics process at both the granular and macro levels, expediting everything from conventional data preparation to informed action.

Granular Level Benefits

lunar-lake-475819_640.jpgContinually extracting analytic insight from data lakes at speeds commensurate with big data ingestion requires orderly data management for metadata and semantic consistency, data discovery, and tailored integration efforts. Semantic data lakes facilitate these prerequisites via semantic models based on an OWL ontology that provides descriptions of data that all users can understand. Additionally, these models are visually represented with semantic graphs that illustrate relationships and attributes between data elements. Subsequently, users can discern data’s meaning, context, and relationships to other data before performing analytics.

The heightened understanding of data that the semantic model and semantic graph produces simplifies a number of requirements for sustainable, expeditious analytics. Such models are readily linked to governance protocols for metadata and semantic consistency regardless of if data is structured, semi-structured or unstructured. The overall context and meaning of data optimizes data discovery efforts and allows end users to discern which data—across disparate sources and structures—requires integration for targeted use cases.

High Level Benefits

Organizations truly reap these granular level benefits at a higher level. The combination of ontological descriptions and visual representation of elements considerably refines analytics and their nature. By understanding data’s meaning prior to conducting analytics, users can vastly improve the type of analytics performed while pinpointing results for specific uses.

Nonetheless, the primary benefit of utilizing semantic data lakes is the relatively newfound ability to incorporate an organization’s entire information assets—filtered through the aforementioned discovery and integration efforts—with the notion of scalable semantics. Semantics at scale is the concept that an RDF graph query engine can now analyze billions of triples in negligible amounts of time, obliterating limitations on the amount of data used and the time in which results are derived. The result is the newfound ability to issue more queries, utilize more data and get results quicker, so that all enterprise data becomes relevant.

To learn more, download our whitepaper "Data Lake Trends - the Rise of Data Lakes".

Download  Data Lake Trends - the Rise of Data Lakes


Tags: Semantics, Data Integration, Big Data, Data Lake, Analytics, Smart Data Lake

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