Data Quality Archives - Tiger Analytics

Blog Tags: Data Quality

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.

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.

What is Data Observability Used For?

Learn how Data Observability can enhance your business by detecting crucial data anomalies early. Explore its applications in improving data quality and model reliability, and discover Tiger Analytics’ solution. Understand why this technology is attracting major investments and how it can enhance your operational efficiency and reduce costs.

Migrating from Legacy Systems to Snowflake: Simplifying Excel Data Migration with Snowpark Python

Discover how Snowpark Python streamlines the process of migrating complex Excel data to Snowflake, eliminating the need for external ETL tools and ensuring data accuracy.

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.

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.

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.