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Your Generative AI LLM Needs a Data Journey: A Comprehensive Guide for Data Engineers

DataKitchen

However, the foundation of their success rests not just on sophisticated algorithms or computational power but on the quality and integrity of the data they are trained on and interact with. The Imperative of Data Quality Validation Testing Data quality validation testing is not just a best practice; it’s imperative.

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Data governance in the age of generative AI

AWS Big Data

Working with large language models (LLMs) for enterprise use cases requires the implementation of quality and privacy considerations to drive responsible AI. However, enterprise data generated from siloed sources combined with the lack of a data integration strategy creates challenges for provisioning the data for generative AI applications.

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What is data governance? Best practices for managing data assets

CIO Business Intelligence

The Business Application Research Center (BARC) warns that data governance is a highly complex, ongoing program, not a “big bang initiative,” and it runs the risk of participants losing trust and interest over time. Informatica Axon Informatica Axon is a collection hub and data marketplace for supporting programs.

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Why You’re Not Ready for Knowledge Graphs!

Ontotext

Data integration If your organization’s idea of data integration is printing out multiple reports and manually cross-referencing them, you might not be ready for a knowledge graph. Data quality Knowledge graphs thrive on clean, well-structured data, and they rely on accurate relationships and meaningful connections.

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The Enduring Significance of Data Modeling in the Modern Data-Driven Enterprise

erwin

Let’s explore the continued relevance of data modeling and its journey through history, challenges faced, adaptations made, and its pivotal role in the new age of data platforms, AI, and democratized data access. Embracing the future In the dynamic world of data, data modeling remains an indispensable tool.

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8 data strategy mistakes to avoid

CIO Business Intelligence

“Establishing data governance rules helps organizations comply with these regulations, reducing the risk of legal and financial penalties. Clear governance rules can also help ensure data quality by defining standards for data collection, storage, and formatting, which can improve the accuracy and reliability of your analysis.”

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You Cannot Get to the Moon on a Bike!

Ontotext

And each of these gains requires data integration across business lines and divisions. Limiting growth by (data integration) complexity Most operational IT systems in an enterprise have been developed to serve a single business function and they use the simplest possible model for this. We call this the Bad Data Tax.