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Unified Semantic Layers, Unifying Business and IT

Data Virtualization

In this post I want to talk about a simple but powerful concept in business intelligence and analytics called a “unified semantic layer.” You might be a business user or analyst who works with a variety of tools, like Tableau.

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Case study: Policy Enforcement Automation With Semantics

Ontotext

They are expected to understand the entire data landscape and generate business-moving insights while facing the voracious needs of different teams and the constraints of technology architecture and compliance. Data-centric approach In the data-centric approach, metadata serves as a layer of interoperability between the data sources.

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As insurers look to be more agile, data mesh strategies take centerstage

CIO Business Intelligence

In an era of business where every industry requires its stakeholders to be capable of quick pivots and sharp turns, siloed information that ultimately slows decision-making can be the ultimate vulnerability. The unified need Despite these criticisms, the need for unified, integrated data solutions at the enterprise level remains.

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DataOps Enables Your Data Fabric

DataKitchen

For example, one could say that data fabrics address complexity, eliminate silos, unify toolchains and help organizations derive insights. Data fabrics purport to offer a unified approach to manage the cacophony of heterogeneous toolchains being thrown at data problems. This is happening to the term “data fabric.” Process Constraints.

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How Knowledge Graphs Power Data Mesh and Data Fabric

Ontotext

Most organizations today languish at the information layer of the DIKW pyramid , unable to make the leap to the knowledge and wisdom layer, where the real value of data is. Teams can’t access data to build their business use cases. Additionally, there’s a lack of semantics for the data. Not a drop of insight.”

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Why Establishing Data Context is the Key to Creating Competitive Advantage

Ontotext

Central to today’s efficient business operations are the activities of data capturing and storage, search, sharing, and data analytics. Poor data management, data silos, and a lack of a common understanding across systems and/or teams are the root cause that prohibits an organization from scaling the business in a dynamic environment.

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KGF 2023: Bikes To The Moon, Datastrophies, Abstract Art And A Knowledge Graph Forum To Embrace Them All

Ontotext

It enriched their understanding of the full spectrum of knowledge graph business applications and the technology partner ecosystem needed to turn data into a competitive advantage. ” With new business lines, leading to new tools, a lot of diverse and siloed data inevitably enters enterprise systems. Cunningham.