Blog Tags: Great Expectations
Building Trusted Data: A Comprehensive Guide to Tiger Analytics’ Snowflake Native Data Quality Framework
Challenges in data quality are increasingly hindering organizations, with issues like poor integration, operational inefficiencies, and lost revenue opportunities. A 2024 report reveals that 67% of professionals don’t fully trust their data for decision-making. To tackle these problems, Tiger Analytics developed a Snowflake native Data Quality Framework, combining Snowpark, Great Expectations, and Streamlit. Explore how the framework ensures scalable, high-quality data for informed decision-making.
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How Tiger’s Data Quality Framework unlocks Improvements in Data Quality
Accurate data is crucial for informed decisions. Organizations must set clear data quality objectives, implement early data quality processes, and deploy IT solutions aligned with business goals to achieve this. Read how utilizing the Tiger Data Quality framework for automation can help enhance efficiency and eliminate manual data quality checks for better outcomes.
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