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The future of data: A 5-pillar approach to modern data management

CIO

It was not alive because the business knowledge required to turn data into value was confined to individuals minds, Excel sheets or lost in analog signals. We are now deciphering rules from patterns in data, embedding business knowledge into ML models, and soon, AI agents will leverage this data to make decisions on behalf of companies.

Data 167
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The key to operational AI: Modern data architecture

CIO

Recent research shows that 67% of enterprises are using generative AI to create new content and data based on learned patterns; 50% are using predictive AI, which employs machine learning (ML) algorithms to forecast future events; and 45% are using deep learning, a subset of ML that powers both generative and predictive models.

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From Machine Learning to AI: Simplifying the Path to Enterprise Intelligence

Cloudera

Thats why were moving from Cloudera Machine Learning to Cloudera AI. Why AI Matters More Than ML Machine learning (ML) is a crucial piece of the puzzle, but its just one piece. It means combining data engineering, model ops, governance, and collaboration in a single, streamlined environment.

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What is data architecture? A framework to manage data

CIO

Data architecture definition Data architecture describes the structure of an organizations logical and physical data assets, and data management resources, according to The Open Group Architecture Framework (TOGAF). An organizations data architecture is the purview of data architects.

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From legacy to lakehouse: Centralizing insurance data with Delta Lake

CIO

Finally, refine and aggregate the clean data into insights that directly support key insurance functions like underwriting, risk analysis and regulatory reporting. Step 3: Data governance Maintain data quality. Enforce strict rules (schemas) to ensure all incoming data fits the expected format.

Insurance 164
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Specialized tools for machine learning development and model governance are becoming essential

O'Reilly Media - Ideas

Why companies are turning to specialized machine learning tools like MLflow. A few years ago, we started publishing articles (see “Related resources” at the end of this post) on the challenges facing data teams as they start taking on more machine learning (ML) projects. Model governance.

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IT leaders: What’s the gameplan as tech badly outpaces talent?

CIO

Gen AI-related job listings were particularly common in roles such as data scientists and data engineers, and in software development. Were building a department of AI engineering, mostly by bringing in people from data engineering and training them to work with gen AI and AI in general, says Daniel Avancini, Indiciums CDO.