Blog Industry: Healthcare
Reimagine and Reengineer the Enterprise with AI
This article is a guide to enterprise AI transformation. It proposes a dual strategy of reimagining business functions while simultaneously reengineering the technology landscape with a scalable AI platform and agents. True value is unlocked by merging this top-down strategy with a bottom-up, human-centric “AI for Personas” approach to drive widespread adoption and solve real-world integration challenges. This framework enables organizations to unlock efficiencies, foster innovation, and sustain a competitive advantage.
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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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Exploring GitHub Copilot for Data Engineering
As GitHub Copilot’s popularity grows among Fortune 100 companies, we explore this AI-powered coding assistant’s potential impact on development workflows. Built by GitHub and OpenAI, this tool integrates with IDEs like Visual Studio Code, suggesting contextually relevant code snippets in real-time. While Copilot can boost productivity, developers must carefully review its suggestions to ensure they align with their needs. Read on to explore how this tool can enhance your coding experience.
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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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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.
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11 Ways Data Engineers Can Leverage an Agile Mindset to Help Drive Value Efficiently and Effectively
This comprehensive guide explores how Agile methodologies can be applied to data engineering within the Tiger Gene framework. It outlines key principles such as welcoming change, working in small increments, and continuous improvement. The article provides practical scenarios, pros and cons, and solutions for implementing Agile practices in data engineering projects. Data engineers can learn how to drive efficient and effective value through enhanced collaboration, flexibility, and iterative development, ultimately improving their project outcomes and team dynamics.
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Implementing Context Graphs: A 5-Point Framework for Transformative Business Insights
This comprehensive guide outlines three phases: establishing a Knowledge Graph, developing a Connected Context Graph, and integrating AI for auto-answers. Learn how this framework enables businesses to connect data points, discover patterns, and optimize processes. The article also presents a detailed roadmap for graph implementation and discusses the integration of Large Language Models with Knowledge Graphs.
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Connected Context: Introducing Product Knowledge Graphs for Smarter Business Decisions
Explore how Product Knowledge Graphs, powered by Neo4j, are reshaping data analytics and decision-making in complex business environments. This article introduces the concept of Connected Context and illustrates how businesses can harness graph technology to gain deeper insights, improve predictive analytics, and drive smarter strategies across various functions.
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