How Semantic-Based Knowledge Graphs Accelerate the Value of Data Lakes
We're excited to bring Transform 2022 back in person on July 19 and virtually from July 20-28. Join leaders in AI and data for in-depth discussions and exciting networking opportunities. Sign up today!
Democratizing data and generating insights has never been more important to gaining competitive advantage. Whether performing advanced analytics to guide decision-making or modeling complex relationships with data that is both too big and too big to describe people, places, things and their relationships, Knowledge graphs make a difference in how information is found, used and leveraged.
We may not realize it, but we're actually using a Knowledge Graph when we Google things like a nearby restaurant that has live music on Tuesdays. It is therefore not surprising that Enterprise Knowledge Graphs (EKG) are also gaining popularity in the workplace. By helping to extract, connect, and deliver knowledge as answers, then formulate recommendations and insights into each data-driven application, EKGs structure an organization's information so it can optimize BI and IT. analysis and generate better results from chatbots and recommendation engines.
However, a knowledge graph enabled with a semantic layer can take these benefits even further by providing organizations with the foundation for an enterprise Data Fabric architecture. This combination enables cross-functional, cross-enterprise, and/or cross-organizational teams to pose and answer complex queries across domain silos, then make data sharing easy and accessible to meet existing and future organizational needs. It may also be why Gartner believes that by 2024, Data Fabric deployments will quadruple the efficiency of data usage while halving human-driven data management tasks. .
Pulling back the curtain: Why data fabrics are growing in popularityMassive advancements in data management are enabling businesses to unlock untapped value by leveraging and connecting data internal and external to their organization. Open standards in the form of prescribed ontologies (i.e. semantic models), from FIBO in financial services to D3FEND in cybersecurity, further promote both data sharing and the concept data factory and make data reuse even more possible.
EventTransform 2022
Join us at the leading Applied AI event for enterprise business and technology decision makers on July 19 and virtually July 20-28.
register hereThe premise behind the concept of a semantic layer isn't new, having emerged over three decades ago thanks, in part, to BI vendors building purpose-built dashboards. Like other proprietary systems, adoption stagnated, largely because they were too rigid, too complex, and suffered from the same limitations as a physical relational database system that models data to take support its structured query language rather than to support how data is related in the real world. world (i.e. many-to-many). Similarly, the rigidity of relational or graphical database structures made it difficult to link and network complex resources...
We're excited to bring Transform 2022 back in person on July 19 and virtually from July 20-28. Join leaders in AI and data for in-depth discussions and exciting networking opportunities. Sign up today!
Democratizing data and generating insights has never been more important to gaining competitive advantage. Whether performing advanced analytics to guide decision-making or modeling complex relationships with data that is both too big and too big to describe people, places, things and their relationships, Knowledge graphs make a difference in how information is found, used and leveraged.
We may not realize it, but we're actually using a Knowledge Graph when we Google things like a nearby restaurant that has live music on Tuesdays. It is therefore not surprising that Enterprise Knowledge Graphs (EKG) are also gaining popularity in the workplace. By helping to extract, connect, and deliver knowledge as answers, then formulate recommendations and insights into each data-driven application, EKGs structure an organization's information so it can optimize BI and IT. analysis and generate better results from chatbots and recommendation engines.
However, a knowledge graph enabled with a semantic layer can take these benefits even further by providing organizations with the foundation for an enterprise Data Fabric architecture. This combination enables cross-functional, cross-enterprise, and/or cross-organizational teams to pose and answer complex queries across domain silos, then make data sharing easy and accessible to meet existing and future organizational needs. It may also be why Gartner believes that by 2024, Data Fabric deployments will quadruple the efficiency of data usage while halving human-driven data management tasks. .
Pulling back the curtain: Why data fabrics are growing in popularityMassive advancements in data management are enabling businesses to unlock untapped value by leveraging and connecting data internal and external to their organization. Open standards in the form of prescribed ontologies (i.e. semantic models), from FIBO in financial services to D3FEND in cybersecurity, further promote both data sharing and the concept data factory and make data reuse even more possible.
EventTransform 2022
Join us at the leading Applied AI event for enterprise business and technology decision makers on July 19 and virtually July 20-28.
register hereThe premise behind the concept of a semantic layer isn't new, having emerged over three decades ago thanks, in part, to BI vendors building purpose-built dashboards. Like other proprietary systems, adoption stagnated, largely because they were too rigid, too complex, and suffered from the same limitations as a physical relational database system that models data to take support its structured query language rather than to support how data is related in the real world. world (i.e. many-to-many). Similarly, the rigidity of relational or graphical database structures made it difficult to link and network complex resources...
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