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Digital twins at scale: Building the AI architecture that will reshape enterprise operations

CIO Business Intelligence

This involves understanding the needs of stakeholders, identifying key performance indicators (KPIs) and establishing success criteria. Determine what problems it aims to solve and what benefits it will bring. Collect data Data collection is critical to the accuracy and efficacy of the digital twin.

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Have we reached the end of ‘too expensive’ for enterprise software?

CIO Business Intelligence

These can highlight trends, anomalies, and key performance indicators that are valuable to both technicians and managers. GenAI as a standard component in enterprise software Companies need to recognize generative AI for what it is: a general-purpose technology that touches everything.

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Managing machine learning in the enterprise: Lessons from banking and health care

O'Reilly on Data

Depending on the reward structure within an organization, some parties might be less likely to challenge models that help elevate their own specific key performance indicators (KPIs). Continue reading Managing machine learning in the enterprise: Lessons from banking and health care. Governance, policies, controls.

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The Future of Enterprise Architecture

erwin

The business challenges facing organizations today emphasize the value of enterprise architecture (EA) , so the future of EA is closer than you think. See also: What Is Enterprise Architecture? . Let’s dig into the first two and then look at the role of enterprise architects and how to ensure your EA tools are up to the tasks ahead.

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Making your enterprise architecture framework work for you

CIO Business Intelligence

More organizations than ever have adopted some sort of enterprise architecture framework, which provides important rules and structure that connect technology and the business. The results of this company’s enterprise architecture journey are detailed in IDC PeerScape: Practices for Enterprise Architecture Frameworks (September 2024).

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Data’s dark secret: Why poor quality cripples AI and growth

CIO Business Intelligence

I aim to outline pragmatic strategies to elevate data quality into an enterprise-wide capability. Key recommendations include investing in AI-powered cleansing tools and adopting federated governance models that empower domains while ensuring enterprise alignment. Compliance-heavy environments, enterprise reporting.

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Demystify data sharing and collaboration patterns on AWS: Choosing the right tool for the job

AWS Big Data

However, enterprises often encounter challenges with data silos, insufficient access controls, poor governance, and quality issues. Embracing data as a product is the key to address these challenges and foster a data-driven culture. At the core of this ecosystem lies the enterprise data platform.

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