Blog Industry: Insurance
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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GenAI for ITSM – 4 Ways LLMs Improve IT Ticket Handling and User Experience
Large Language Models (LLMs) are transforming IT service management by automating ticket categorization, improving prioritization, and speeding up resolutions. This article explores how LLMs enhance efficiency, empower users, and support agents in handling complex issues, all while streamlining workflows and improving response times.
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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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From the Olympics to Product Releases, How Project Managers Can Go for Gold with GenAI
Generative AI is making a real impact in project management by helping teams work more efficiently and stay on track. In this blog, we explore how project managers can use GenAI to address common challenges like scope creep and budgeting issues, and optimize workflows, all while ensuring ethical and privacy considerations are met.
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Building Dynamic Data Pipelines with Snowpark: Our Framework to Drive Modern Data Transformation
Learn about the challenges of traditional data transformation methods and how a dynamic approach using metadata configuration can help address these issues. By defining transformation rules and specifications, enterprises can create flexible pipelines that adapt to their evolving data processing needs, ultimately accelerating the process of extracting insights from data.
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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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Tiger’s Snowpark-Based Framework for Snowflake: Illuminating the Path to Efficient Data Ingestion
In the era of AI and machine learning, efficient data ingestion is crucial for organizations to harness the full potential of their data assets. Tiger’s Snowpark-based framework addresses the limitations of Snowflake’s native data ingestion methods, offering a highly customizable and metadata-driven approach that ensures data quality, observability, and seamless transformation.
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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.
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AI-Powered Insurance Wins: Unlocking Process Efficiencies with NLP and Generative AI
Explore the synergy of Natural Language Processing (NLP) and Generative AI in the insurance sector. Discover how these technologies accelerate Pricing and Underwriting, simplify Claims Processing, improve Contact Center Operations, and strengthen Marketing and Distribution, initiating a digital transformation journey.
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How Insurance companies are using NLP to streamline application approval
Insurance companies are using Natural Language Processing (NLP) to speed up the approval of applications. NLP helps to pull out important details from text, making it easier to decide on approvals. By adding AI to their current systems, companies have seen faster renewals, showing that NLP can help make the approval process smoother and quicker.
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