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213 Results for Keywords data lineage

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    Technical and Strategic Best Practices for Building Robust Data Platforms

    In the AI era, organizations are eager to harness innovation and create value through high-quality, relevant data. Gartner, however, projects that 80% of data governance initiatives will fail by 2027. This statistic underscores the urgent need for robust data platforms and governance frameworks. A successful data strategy outlines best practices and establishes a clear vision for data architecture, […]

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    Data Governance Trends in 2025

    As organizations enter 2025, it is time to reflect on the significant trends that shaped data governance in 2024 and anticipate what lies ahead. Multiple factors have driven the rapid evolution of data governance frameworks, including advancements in technology, increased regulatory oversight, and shifting business priorities.  To continually improve data governance, it is critical to review key […]

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    From Input to Insight: How Quality Data Drives AI and Automation

    More and more enterprises are looking to automation and AI to deliver new efficiencies and give their organizations an edge in the market. Data is the engine that powers both automation and AI. But data must be clean and user-friendly for these systems to work effectively and deliver on their promise.  Lots of organizations are […]

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    Data Modeling Trends in 2025: Simplifying Complex Business Problems

    Data modeling, the practice of diagramming business requirements, has grown significantly. According to a 2024 DATAVERSITY® survey on data management trends, 64% of organizations actively use data modeling, a 13% increase since 2023.   This trend will only increase in 2025 as companies face heightened opportunities and risks. Newer technologies and AI are set to disrupt predictive analytics, especially with huge volumes of […]

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    Data Architecture Trends in 2025

    A modern data architecture is required to support the data-driven organization that every enterprise wants to be. Without a solid data architecture – composed of the models, policies, rules, and standards you set for how data is collected, stored, managed, and used – your ability to attain a holistic view of your business, make informed […]

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    Data Observability 101

    Understanding data health involves monitoring and analyzing diverse aspects of data systems – referred to as data observability – to ensure optimal performance and reliability. Metrics are crucial, providing quantitative insights into data flow, processing times, and system resource utilization. They also help identify patterns and detect anomalies in real time. Logs offer a historical record of […]

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