Real-world events demonstrate our collective inability to rapidly and accurately observe, process, and interpret information in support of decision making. Additionally, one can argue that any sizable enterprise struggles to “know what it knows”. In other words, we often cannot answer questions completely or with certainty, despite the fact that we may hold the requisite data.
Individuals and organizations ceaselessly collect, analyze, and ultimately act on knowledge to combat uncertainty and gain a competitive advantage. Data management platforms today collect and generate incalculable amounts of raw data. However, the state-of-the-art for data interpretation requires substantial manual analysis, prioritization and dissemination.
As the proliferation of sensors and bandwidth increases, the quantity of available data increases; and the overabundant data imposes additional time and manpower overhead to interpret the data for decision-makers. Increasingly, aspects of decision-making are leaving the realm of human senses, and crossing outside the limits of human reaction times. Eventually it may become impossible for humans to absorb and discern knowledge value from the mounting data glut. The result: decisions based on incomplete information (i.e. conjecture).
The technologies and methodologies of the Semantic Web allow machines to help humans interpret the data glut, and incorporate more data into the construction of domain knowledge. Machine interpretable data is essential to constructing knowledge; which, in turn, is a key enabler to “decision dominance” and the speed at which we act on knowledge is fundamental to success. Because organizations and individuals desire the highest possible degree of certainty from which to make decisions, they pursue knowledge wherever it is perceived to exist. While this pursuit of knowledge is justified theoretically, it is extremely difficult, time consuming and complex to realize with current Web technologies. Semantic Web technologies abstract more of the complexities, and mitigate difficulties associated with data integration and knowledge representation, all of which are employed in the construction and interpretation of knowledge to support decision making.
Cambridge Semantics enjoys the ability to leverage machine interpretable data to construct and process knowledge to assist decision makers in achieving a higher degree of certainty than was previously possible - all with decreased human intervention. Cambridge Semantics applies W3C Semantic Web technologies to overcome data management challenges, integrate data, and create machine interpretation and learning. Cambridge Semantics’ application of semantic data integration liberates data from application-specific silos – files, databases, cloud data stores, etc. – to create context which improves an organization's ability to “know what they know.”
To learn more about Semantic Graph databases, view our on-demand webinar "Semantic Graph Databases: The Evolution of Relational Databases".