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Critical Data Elements: Your Shortcut to Data Governance That Actually Works

DataKitchen

The Harsh Reality of Data Governance 💥 80% of data governance initiatives fail. But because the business isn’t involved, and no one agrees on what data truly matters. That’s where Critical Data Elements (CDEs) change everything. Not because of tools. Not because of frameworks.

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Navigating data governance and classification in generative AI with NetApp

CIO Business Intelligence

In today’s data-driven world, the proliferation of artificial intelligence (AI) technologies has ushered in a new era of possibilities and challenges. One of the foremost challenges that organizations face in employing AI, particularly generative AI (genAI), is to ensure robust data governance and classification practices.

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The Symbiotic Relationship Between Data Governance and AI

David Menninger's Analyst Perspectives

Data governance has always been a critical part of the data and analytics landscape. However, for many years, it was seen as a preventive function to limit access to data and ensure compliance with security and data privacy requirements. Data governance is integral to an overall data intelligence strategy.

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AI-Driven Data Governance and Compliance Best Practices

KDnuggets

By KDnuggets on August 11, 2025 in Partners Sponsored Content Organizations that manage large volumes of data are increasingly turning towards artificial intelligence-backed solutions for efficient, scalable data governance and compliance. As data continues to grow, AI will be the critical partner businesses need to thrive.

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How to Foster Data Culture (with Data Intelligence Technology!)

Speaker: Aaron Kalb, Co-Founder and CDAO at Alation

What ingredients are needed to create a data culture, including data search & discovery, data literacy, and data governance. How to leverage a data catalog to create a data culture. Watch this webinar to learn: The surprising role of technology in shaping culture.

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

CIO Business Intelligence

The proposed model illustrates the data management practice through five functional pillars: Data platform; data engineering; analytics and reporting; data science and AI; and data governance. This development will make it easier for smaller organizations to start incorporating AI/ML capabilities.

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It’s 2025. Are your data strategies strong enough to de-risk AI adoption?

CIO Business Intelligence

Despite decades of investment in data management solutions, many continue to struggle with data quality issues, either through their failure to modernise legacy investments or through the outcomes of acquisitions and business decisions, which in either instance have led to data existing in multiple silos across their organisations.

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Top Considerations for Building an Open Cloud Data Lake

The primary architectural principles of a true cloud data lake, including a loosely coupled architecture and open file formats and table structures. The key prerequisites for meeting the needs of non-technical users while adhering to data governance policies.

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Using a Machine Learning Data Catalog to Reboot Data Governance

Speaker: David Loshin, President, Knowledge Integrity, Inc, and Sharon Graves, Enterprise Data - BI Tools Evangelist, GoDaddy

Traditional data governance fails to address how data is consumed and how information gets used. As a result, organizations are failing to effectively share and leverage data assets. To meet the needs of the business and the growing number of data consumers, many organizations like GoDaddy are rebooting data governance.

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Partner Webinar: A Framework for Building Data Mesh Architecture

Speaker: Jeremiah Morrow, Nicolò Bidotti, and Achille Barbieri

In this session, you will learn: How the silos development led to challenges with data growth, data quality, data sharing, and data governance (an example of datamesh paradigm adoption). Leveraging Dremio for data governance and multi-cloud with Arrow Flight.

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Why Modern Data Challenges Require a New Approach to Governance

A healthy data-driven culture minimizes knowledge debt while maximizing analytics productivity. Agile Data Governance is the process of creating and improving data assets by iteratively capturing knowledge as data producers and consumers work together so that everyone can benefit.

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How to Foster Data Culture (with Data Intelligence Technology!)

Speaker: Aaron Kalb, Co-Founder and CDAO at Alation

What ingredients are needed to create a data culture, including data search & discovery, data literacy, and data governance. How to leverage a data catalog to create a data culture. Watch this webinar to learn: The surprising role of technology in shaping culture.

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A Practical Guide to Business Intelligence Governance

Speaker: Marius Moscovici, CEO Metric Insights & Mike Smitheman, VP Metric Insights

While the proper governance of data is clearly critical to the success of any business intelligence organization, focusing on data governance alone is a huge mistake. Organizations continually fail to generate ROI on their governance initiatives because they are too narrow in scope.

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LLMOps for Your Data: Best Practices to Ensure Safety, Quality, and Cost

Speaker: Shreya Rajpal, Co-Founder and CEO at Guardrails AI & Travis Addair, Co-Founder and CTO at Predibase

However, productionizing LLMs comes with a unique set of challenges such as model brittleness, total cost of ownership, data governance and privacy, and the need for consistent, accurate outputs. Large Language Models (LLMs) such as ChatGPT offer unprecedented potential for complex enterprise applications.