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10 most in-demand enterprise IT skills

CIO

Central to cloud strategies across nearly every industry, AWS skills are in high demand as organizations look to make the most of the platforms wide range of offerings. Oracle skills are common for database administrators, database developers, cloud architects, business intelligence analysts, data engineers, supply chain analysts, and more.

UI/UX 203
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What is data architecture? A framework to manage data

CIO

Data architecture vs. data modeling According to Data Management Book of Knowledge (DMBOK 2) , data architecture defines the blueprint for managing data assets as aligning with organizational strategy to establish strategic data requirements and designs to meet those requirements.

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Why thinking like a tech company is essential for your business’s survival

CIO

Aligning your culture, processes and technology strategy ensures you can adapt to a rapidly changing landscape while staying true to your core purpose. Investing in the future Now is the time to dedicate the necessary resources to prepare your business for what lies ahead. The pace of change isnt slowing down, and neither are we.

Company 186
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The key to operational AI: Modern data architecture

CIO

People : To implement a successful Operational AI strategy, an organization needs a dedicated ML platform team to manage the tools and processes required to operationalize AI models. The team should be structured similarly to traditional IT or data engineering teams.

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Fundamentals of Data Engineering

Xebia

The following is a review of the book Fundamentals of Data Engineering by Joe Reis and Matt Housley, published by O’Reilly in June of 2022, and some takeaway lessons. This book is as good for a project manager or any other non-technical role as it is for a computer science student or a data engineer.

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Comprehensive data management for AI: The next-gen data management engine that will drive AI to new heights

CIO

The challenges of integrating data with AI workflows When I speak with our customers, the challenges they talk about involve integrating their data and their enterprise AI workflows. The core of their problem is applying AI technology to the data they already have, whether in the cloud, on their premises, or more likely both.

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The success of GenAI models lies in your data management strategy

CIO

These data will be cleansed, labelled, and anonymized, with data pipelines built to integrate them within an AI model. The data preparation process should take place alongside a long-term strategy built around GenAI use cases, such as content creation, digital assistants, and code generation.

Strategy 215