Blog Tags: Data Governance
The Data Leader’s Guide to Responsible AI: Why Strong Data Governance Is Key to Mitigating AI Risks
AI has moved from science fiction to everyday reality, but its success hinges on strong data governance. In this blog, we explore why effective governance is crucial for AI, how data leaders can build effective data governance for AI, and practical steps for aligning data governance with AI initiatives, ensuring transparency, mitigating risks, and driving better outcomes.
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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 to Simplify Data Profiling and Management with Snowpark and Streamlit
Learn why data quality is one of the most overlooked aspects of data management. While all models need good quality data to generate useful insights and patterns, data quality is especially important. In this blog, we explore how data profiling can help you understand your data quality. Discover how Tiger Analytics leverages Snowpark and Streamlit to simplify data profiling and management.
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Invisible Threats, Visible Solutions: Integrating AWS Macie and Tiger Data Fabric for Ultimate Security
Data defenses are now fortified against potential breaches with the Tiger Data Fabric-AWS Macie integration, automating sensitive data discovery, evaluation, and protection in the data pipeline for enhanced security. Explore how to integrate AWS Macie into a data fabric.
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Enabling Cross Platform Data Observability in Lakehouse Environment
Dive into data observability and its pivotal role in enterprise data ecosystems. Explore its implementation in a Lakehouse environment using Azure Databricks and Purview, and discover how this integration fosters seamless data management, enriched data lineage, and quality monitoring, empowering informed decision-making and optimized data utilization.
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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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How to Design your own Data Lake Framework in AWS
Learn how you can efficiently build a Data Lakehouse with Tiger Data Fabric’s reusable framework. We leverage AWS’s native services and open-source tools in a modular, multi-layered architecture. Explore our insights and core principles to tailor a solution for your unique data challenges.
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Why Organizations need a Cloud Data Engineering Council
With enterprise cloud modernization plans, organizations face the pressure to boost IT spending on cloud data strategy. A Cloud Data Engineering Council is essential for reducing IT costs, enhancing data management, enforcing security policies, and ensuring compliance. Read how to set up a Cloud Data Engineering Council and more…
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Unlocking the Potential of Modern Data Lakes: Trends in Data Democratization, Self-Service, and Platform Observability
Learn how self-service management, intelligent data catalogs, and robust observability are transforming data democratization. Walk through the crucial steps and cutting-edge solutions driving modern data platforms towards greater adoption and democratization.
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Ringing in the Future: How Advanced Analytics is Transforming the Telecom industry
Explore how Analytics is helping the Telecom industry uncover growth opportunities for customer acquisition, while simultaneously growing the value of existing customers.
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