Blog Industry: Healthcare
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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Why India-Targeted AI Matters: Exploring Opportunities and Challenges
The scope for AI-focused innovation is tremendous, given India’s status as one of the fastest-growing economies with the second-largest population globally. Explore the challenges and opportunities for AI in India.
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Enhancing Mental Healthcare: Machine Learning’s Role in Clinical Trials
Unveil the pivotal role of machine learning in revolutionizing mental health care through advanced clinical trials. Discover how innovative AI solutions, like speech analytics, enhance the evaluation of mental health treatments, contributing to more accurate and efficient healthcare outcomes.
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Strategic Guide: Maximize the Power of Bayesian Belief Networks
Examine the use of Bayesian Belief Networks for event prediction, driver analysis, and intervention assessment. Get actionable insights on the construction and practical applications of these networks in the healthcare sector – and how to enhance predictive accuracy while making data-driven decisions.
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Epidemic Forecasting: Applications of Mathematical Modeling
Understand the role of mathematical models in analyzing epidemics and how models are outlined to predict disease spread, assess intervention strategies, and support public health decisions. Learn how these models contribute to effective epidemic management.
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