Business data analysis

The Unparalleled Utility of Graph Databases

The Unparalleled Utility of Graph Databases

Many aspects of data management—particularly concerning big data—hinge upon the utility of graph databases. When deployed with additional semantic technologies such as ontologies, taxonomies and vocabularies, there are few analytic feats an RDF graph cannot achieve. In most instances, end users are largely unaware of the degree of complexity that semantic graphs account for when linking and contextualizing disparate data elements for unified results.

Graph databases initially gained prominence with use cases involving social media and facets of sentiment analysis; this technology gained credence by provisioning ‘360 degree views’ of customer and product information in MDM systems. Other commonly found uses of graph databases include applications of time-sensitive data such as recommender engines for e-commerce, fraud detection for finance, search engine augmentation, and ERP optimization.

But as valuable and as proven as these individual uses cases are they only hint at, and do not truly attest to, the full array of possibilities of databases powered by semantic graphs.

Read Also:
Rise In Cognitive Computing In 2017

Today,semantic graphs are greatly expanding the utility of data lakes in a sustainable manner. The enhanced analytic capabilities of semantic graphs are integral to cognitive computing analytics, as well as to analysis of integrated unstructured and structured data.

The true potential of semantic graphs is realized in linking the entire information assets of an organization for comprehensive analytics of overall internal data as well as public data and third-party data. The underlying architecture for such an undertaking commonly involves Hadoop or some other other data lake to account for issues of scale; ontologies are required for a semantically consistent model, and terminology systems are needed to clarify terms and definitions.

Once this architecture is in place, an RDF graph links the data via the semantic model to distinguish points of relevance between all data throughout the enterprise. Laymen end users in the business can access all of their company’s data when performing analytics.

Read Also:
Push Your Analytics Out to Customers

Best of all, semantic graphs are responsible for denoting just how a particular node relates to another node to inform analytics with a critical element of context. End users need not understand those relationships themselves to benefit from the propensity of semantic graphs to link and identify relationships between any type of datain a comprehensive semantic model.

The comprehensive nature of semantic graph-based analytics partly pertains to its incorporation of structured data alongside unstructured and semi-structured data.

Once sources are loaded, semantic graphs enable expedient data integration based on their ability to link different data elements.

 



Data Science Congress 2017

5
Jun
2017
Data Science Congress 2017

20% off with code 7wdata_DSC2017

Read Also:
Rise In Cognitive Computing In 2017

AI Paris

6
Jun
2017
AI Paris

20% off with code AIP17-7WDATA-20

Read Also:
Push Your Analytics Out to Customers

Chief Data Officer Summit San Francisco

7
Jun
2017
Chief Data Officer Summit San Francisco

$200 off with code DATA200

Read Also:
Data Is Dominating Emerging Tech
Read Also:
Why Forrester Considers Adobe a Leader in Customer Analytics

Customer Analytics Innovation Summit Chicago

7
Jun
2017
Customer Analytics Innovation Summit Chicago

$200 off with code DATA200

Read Also:
Rise In Cognitive Computing In 2017

Big Data and Analytics Marketing Summit London

12
Jun
2017
Big Data and Analytics Marketing Summit London

$200 off with code DATA200

Read Also:
Rise In Cognitive Computing In 2017

Leave a Reply

Your email address will not be published. Required fields are marked *