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Expert Insights for Your 2025 Data, Analytics, and AI Initiatives

Precisely

Key Takeaways: Data integrity is required for AI initiatives, better decision-making, and more – but data trust is on the decline. Data quality and data governance are the top data integrity challenges, and priorities. Leverage AI to enhance governance. Focus on metadata management.

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Data Governance: Framework, Tools, Principles, Benefits

Knowledge Hut

Data governance refers to the set of policies, procedures, mix of people and standards that organisations put in place to manage their data assets. It involves establishing a framework for data management that ensures data quality, privacy, security, and compliance with regulatory requirements.

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Gain an AI Advantage with Data Governance and Quality

Precisely

Data observability continuously monitors data pipelines and alerts you to errors and anomalies. Data governance ensures AI models have access to all necessary information and that the data is used responsibly in compliance with privacy, security, and other relevant policies. stored: where is it located?

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Expert Insights for Your 2025 Data, Analytics, and AI Initiatives

Precisely

Key Takeaways: Data integrity is required for AI initiatives, better decision-making, and more – but data trust is on the decline. Data quality and data governance are the top data integrity challenges, and priorities. Leverage AI to enhance governance. Focus on metadata management.

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Unleashing GenAI — Ensuring Data Quality at Scale (Part 2)

Wayne Yaddow

Different schemas, naming standards, and data definitions are frequently used by disparate repository source systems, which can lead to datasets that are incompatible or conflicting. To guarantee uniformity among datasets and enable precise integration, consistent data models and terminology must be established.

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The Challenge of Data Quality and Availability—And Why It’s Holding Back AI and Analytics

Striim

Business Intelligence Needs Fresh Insights: Data-driven organizations make strategic decisions based on dashboards, reports, and real-time analytics. If data is delayed, outdated, or missing key details, leaders may act on the wrong assumptions. Poor data management can lead to compliance risks, legal issues, and reputational damage.

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The Intersection of GenAI and Streaming Data: What’s Next for Enterprise AI?

Striim

To achieve accurate and reliable results, businesses need to ensure their data is clean, consistent, and relevant. This proves especially difficult when dealing with large volumes of high-velocity data from various sources. Here are the critical steps enterprises should take to turn this vision into a tangible, scalable solution.