Current information-management technology employed by large enterprises is plagued with extensibility and interoperability issues making it impossible to really understand all aspects of the enterprise and to accomplish enterprise level analytics. There is a need for a fundamental change in information-management technology solving the problems of highly distributed and unpredictable data. Analysis is usually several steps behind the business need; Emergent Analytics ™ is always ahead.
Semantic technology offers a new paradigm for managing enterprise information assets. A semantic system architecture lets customers understand all aspects of the enterprise and how each resource in the enterprise relates to all other resources. It will fundamentally change the way enterprise architecture and enterprise analytics are created.
A semantic architecture creates a graph of all information. The graph represents entities and relationships between entities (facts). It can be distributed or centrally located. A graph-based approach frees data from being locked into a rigid schema. Everything exists as a URI (a node in a graph) with properties attached that define what the thing is. Any data element or application anywhere in the enterprise can have an assertion made about it simply by referencing its URI.
Different parts of the enterprise can create their own descriptions of information resources and other artifacts regarding their domain and as long as they use RDF and OWL to create the descriptions, their graph will naturally federate with the RDF descriptions created by other parts of the enterprise or with business partners.
The RDF descriptions are organized using an OWL ontology, a formal (machine-readable) model that defines the precise meaning of the concepts and relationships that are relevant to a domain. When the precise meanings of the concepts are captured, reuse of data, integration of disparate data sets and extension of the ontology is straightforward. A person or machine is able to understand exactly what the different terms used to describe the data mean. Machines can inference over the ontologies enabling new facts and relationships to be inferred from asserted ones based on rules defined within the ontology or outside the ontology.
knoodl.com, is used to collaboratively construct, manage, and employ ontologies along with other information assets like relational databases or spreadsheets in a secure, scalable environment. Collaboration is important when developing ontologies because it enables organizations to reach a consensus on the meaning of things. A large enterprise or web-based community can take advantage of the network effect in building and evolving an ontology the same way that Wikipedia does. Individuals may only add a small number of assertions, but when these actions are performed on a large scale, as in an enterprise, new information begins to emerge. This is Emergent Analytics.
Transitioning your data and other information from being stored in siloed systems to being part of an information federation is a process in which the Revelytix team is expert. You can have an integrated, extensible view of all your information, enabling you to collect and analyze it in far more flexible, powerful ways than you can today. Emergent Analytics pulls your enterprise onto a new level.
Emergent Analytics allows IS to stay current with the needs of their business partners. It fundamentally changes enterprise data management and analytics.