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... 1:26:23
Tabular Editor in the World of AI, Fabric, TMDL, and Semantic Link (with Daniel Otykier)

Analytic Endeavors

1 month ago 2K views

The discussion centers around recent advancements in analytics tools, specifically the integration of AI within data models, highlighted by the introduction of Tabular Editor as a central feature for managing semantic models. The speaker emphasizes the significance of semantic modeling as a foundational element for analytics, reflecting on its evolving relevance amidst emerging technologies such as AI and the importance of maintaining high-quality data management practices. The features of Tabular Editor are discussed, including performance enhancements and tools designed to aid developers in efficient model construction for Enterprise applications. The conversation also touches on community engagement and the growth trajectory of the tool's user base.
Big Data Analytics Research & Innovations
... 20:6
AI & Skills for Data Analyst Jobs in 2025 (Honest Talk)

Rishabh Mishra

1 month ago 9K views

Discussing the evolving landscape of Data Analyst careers in 2025, the focus is on the significance of adapting to new technologies and skill sets in the age of AI. This involves understanding AI's impact on data roles, the necessary competencies for future analysts, and the potential for AI to automate certain aspects of analysis while enhancing decision-making capabilities. Encouraging viewers to stay informed and proactive in developing essential skills for this changing environment is crucial for career advancement in data analysis.
Big Data Analytics Education
... 9:14
Data Lakehouse, Data Quality and Scaling AI

The Ravit Show

1+ months ago 4K views

Data lake house advancements reflect significant industry progress over the past two years. Enterprises are increasingly focused on customizing AI models with their proprietary data, incorporating both structured and unstructured data to enhance operational productivity and market share. Data quality remains critical, emphasizing the importance of training models effectively with reliable data. Key misconceptions involve viewing structured and unstructured data as separate entities, rather than integrating them for improved accuracy in AI applications. As AI continues to evolve, the lake house architecture will be integral to capturing and utilizing diverse data sources.
02:44 - 03:40

Misconception exists in treating structured and unstructured data as separate; integration is key.

Big Data Analytics Research & Innovations
... 6:52
Agentic AI, Analytics Workflows and Customer Stories

The Ravit Show

1+ months ago 40K views

AI dominates the conversation at this event, with leaders emphasizing the importance of data quality. Businesses are navigating the challenges of agentic AI while acknowledging its transformative potential in decision-making processes. Key discussions highlight the need for clean data, understanding geopolitical nuances, and evaluating readiness for autonomous AI systems. The shift from traditional analytics to predictive models demands a new mindset, as organizations must prepare for the implications of AI decisions, including the risks of erroneous outputs. The drive towards generative AI is reshaping user expectations and business strategies, illustrating a significant trend in the industry.
04:00 - 04:14

Transitioning to predictive AI requires a major shift from retrospective analytics mindsets.

Qlik Big Data Analytics Research & Innovations
... 37:13
How my AI Agent Team Analyses 427,000 rows in Seconds with n8n (Claude 3.7 and ChatGPT 4.5)

Frank Nillard | AI Studio

1+ months ago 2K views

Data analysts often spend extensive time pulling and cleaning data from large databases to gain insights. The promotion of a new system allows this entire process to be automated, enabling quick access to insights using natural language. The system is designed with an orchestrator agent that connects to a SQL database, extracts data, and generates visualizations efficiently. It utilizes advanced models such as GPT 4.5 and Claude 3.7, leveraging their capabilities to improve the data analysis process and reduce reliance on traditional data analysis teams, ultimately driving better business decisions.
01:39 - 01:54

Introduces orchestrator agent, SQL agent, and visualization agent.

n8n Big Data Analytics Research & Innovations
... 32:14
Andy MacMillan, Alteryx | AI & Analytics: Shaping the Future With Alteryx

SiliconANGLE theCUBE

1+ months ago 5K views

Alter is positioned at the forefront of AI data preparation, enabling organizations to optimize their data for AI applications. As businesses face a pivotal shift towards AI, the focus is on ensuring that data is ready and efficiently organized across departments. The role of analysts is evolving into that of data superheroes, leveraging tools like Alteryx to bring together diverse data sources for actionable insights, ultimately enhancing business processes. This transformation is critical as companies look to harness the power of AI while maintaining traceability and security in their data operations.
09:11 - 09:43

Businesses must ensure data traceability for effective AI application and analysis.

Alteryx Big Data Analytics AI Startups
... 57:25
AI & Analytics- Shaping the Future With Alteryx CEO & Tom Davenport

SiliconANGLE theCUBE

1+ months ago 4K views

AI is reshaping how businesses manage data, focusing on preparing it for the evolving landscape. Companies need to organize and structure their data differently to leverage AI effectively. The transition will involve platforms that facilitate automation, data democratization, and collaboration across departments. Professionals in business and operations will require new skills to optimize data utilization, and organizations must address challenges related to architecture and governance. The emphasis should be on turning data into actionable insights while ensuring traceability and transparency, especially when integrating AI solutions into existing business processes.
04:25 - 04:47

Explores the evolving role of business analysts amidst AI advancements.

Alteryx Big Data Analytics AI Startups
... 7:20
Data Scientist vs Data Analyst vs Data Engineer: Roles, Responsibilities, Salary, AI Impact !!

CareerRide

1+ months ago 1K views

Data is essential in today's business landscape, driving decision-making and innovation. Companies globally leverage data to enhance performance, reduce costs, and improve customer experiences. As data roles gain prominence, there is a significant demand for data professionals, including data analysts, data engineers, and data scientists, each with distinct skills and responsibilities. Data analysts focus on visualizing and reporting data, while data engineers build data pipelines, and data scientists apply advanced techniques in AI and machine learning to predict outcomes. The future of data roles will incorporate AI technologies, enhancing strategic tasks and decision-making.
Big Data Analytics Education
... 7:20
Data Scientist vs Data Analyst vs Data Engineer: Roles, Responsibilities, Salary, AI Impact !!

CareerRide

1+ months ago 1K views

Data is essential in today's business landscape, driving decision-making and innovation. Companies globally leverage data to enhance performance, reduce costs, and improve customer experiences. As data roles gain prominence, there is a significant demand for data professionals, including data analysts, data engineers, and data scientists, each with distinct skills and responsibilities. Data analysts focus on visualizing and reporting data, while data engineers build data pipelines, and data scientists apply advanced techniques in AI and machine learning to predict outcomes. The future of data roles will incorporate AI technologies, enhancing strategic tasks and decision-making.
Big Data Analytics Education