Enterprise knowledge
Knowledge Management with access to all the enterprise info, understood, in its context, and interconnected.
Used to understand and to predict.
Including those predictions in automated processes that allow maximizing benefits, improving positioning and reducing risks (increasing resiliency in times of change)
Helping people action, based on their experience, but also in the access to the information and context, and empowered by the automatic processes.
Enterprise Knowledge Management with Knowledge Graphs
Make complex business queries, through all the data stores, with sensible questions and without the need for an army of experts.
Master the complexity of data: understand the meaning of data, in its context and with their connections with other data.
Undertake the real digital transformation, dealing with data in a way that can be used by both persons and machines, making the bridge with AI and ML.
Enterprise knowledge meaning, knowledge graphs, enterprise management, knowledge management
” Growing levels of data volume and distribution are making it hard for organizations to exploit their data asssets efficiently and effectively. Data and analytics leaders need to adopt a semantic approach to their enterprise data; otherwise, they will face an endless battle with data silos.”
Gartner
“Leverage Semantics to Drive Business Value from Data”
November 2021
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Knowledge Graphs Advantages
– Agregate scattered data to help identify trends and optimize investments.
– Find hidden facts in data and reduce operational risks.
– Facilitate employees engagement, knowledge discovery and its retention.
– Implement management models and data governance.
– Set the foundation for an AI based strategy.
Enterpise Knowledge Graphs Use Cases
Health and life sciences
Social networks
Recommendation Engines
Finance and banking services (I.e.: Fraud detection)
Manufacturing (Ej.: Stock management, product knowledge management)
Logistics and provision networks
Digital models to explore and optimize business
Knowledge Management
Knowledge Graphs baser Applications
Enterprise Knowledge Unification
Unifying the access to structured and unstructured data, virtualizing or integrating all DBs and data sources.
Building a semantic layer (with Knowledge Graphs) to give a full contextualized access and with the capability to infer and reason over the enterprise knowledge.
Use of sub-graphs to specialize access for every business unit.
Direct query of knowledge with intuitive no-code tools for domain experts use.
Data expoitation with classical BI and AI tools over the semantic layer.
Incorporate texts and unstructured sources to the Enterprise Knowledge
NLP plus semantic analysis to incorporate documents info to the Enterprise knowledge.
Search over all the structured data and documents with powerful filtering and faceted tools.
Knowledge based ChatBots
More efficient and precise.
Higher user satisfaction.
Can be used only for information purposes (for customers or for employees).
Or synchronized with the enterprise processes, acomplishing tasks following the users requests and the business rules.
Other services in Enterprise Knowledge Graphs
Data architecture design, using virtualization and integration
Ontology design (and reuse of common ontologies) to build the semantic layer
Analysis of specific queries and searchs over the Enterprise Knowledge and building required inference and reasoning rules queries
User training to gain access to the enterprise knowledge through simple intuitive interfaces
CDTI – Neotec SNEO-20231175 Project grant
INTEGRATION OF BUSINESS PROCESSES ON KNOWLEDGE
Subsidised by CDTI
