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White Paper: Enabling Business Optimization and Expense Reduction Through the Use of Augmented Analytics

Smarten

No matter the reason or the goal, when an enterprise chooses the right Augmented Analytics solution and carefully plans for and executes its implementation, it can optimize business results, reduce expenses and improve its market position, customer satisfaction and user adoption, and it is key to transforming business users to Citizen Data Scientists (..)

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The Benefits of a Hub-and-Spoke Integration Model

CDW Research Hub

Because they are not built to scale, dated integration solutions such as point-to-point integration models cannot keep up with the demands of enterprises that rely on a growing number of applications to perform their business processes. Learn more about modern application integration with a hub-and-spoke model by reading our white paper.

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Trust: The foundation for successful digital transformation

CIO Business Intelligence

In this way, teams gain the ability to optimize value delivery and adapt more quickly. Recalibrating enterprises around cohesive digital operating models sustained by a singular real-time data source is essential in establishing a solid foundation for trust.

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Strengthen the manufacturing supply chain with task specific GenAI tools and Microsoft AI

CIO Business Intelligence

Priorities include: Optimizing demand planning tools to better forecast demand changes Reducing lag time in responding to supply chain disruptions Automating tasks from manufacturing to logistics Generative artificial intelligence (GenAI) capabilities can help achieve these priorities by focusing on specific supply chain tasks and processes.

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Business AI will change the way businesses are run

CIO Business Intelligence

We have agreements with more than 25,000 customers to use their data in an anonymized way to train our own models. Using this data to contextualize generative AI models results in very task-specific relevant outcomes for business users.

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Data confidence begins at the edge

CIO Business Intelligence

Without a way to define and measure data confidence, AI model training environments, data analytics systems, automation engines, and so on must simply trust that the data has not been simulated, corrupted, poisoned, or otherwise maliciously generated—increasing the risks of downtime and other disasters.

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Want AI? Here’s how to get your data and infrastructure AI-ready

CIO Business Intelligence

Getting the right and optimal responses out of GenAI models requires fine-tuning with industry and company-specific data. That includes solid infrastructure with the core tenets of scale, security, and performance–all with optimized costs. That’s because data is often siloed across on-premises, multiple clouds, and at the edge.