Mansi Yadav
Founder and CEO
Delhi, India
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I am a Microsoft certified trainer and over 15+ years of experience in delivering 500+ training programs across diverse cohorts (B2B, B2C, B2I) and 35+ Bootcamps.
I am a founder and CEO of an Edtech company in India.
I am a Microsoft certified Power BI data Analyst, Fabric certified and Excel Expert. I am a youtuber, speaker, data enthusiast and a consistent learner.
AWARDS AND ACCOMPLISHMENTS
 C#Sharp Conference Speaker & Awardee - 2022, 2023
Recognized as a speaker for expertise in C#Sharp at the annual conference.
 Woman Economic Forum speaker and Awardee - 2022
Honored for notable contributions in the field of skill development and training.
 Multicloud Master mentor award in 2024
Links
Topics
Transitioning from Excel to Power BI for Data analysis and storytelling
Understand why and how to transition from Excel to Power BI for Data analysis and storytelling. Understand the importance of creating interactive visuals that go beyond static charts, turning complex data into clear, compelling stories. Leverage Power BI’s features help you connect with your audience to make strategic decisions and to draw meaningful outcomes using stories created in Power BI.
Implementing Data Governance in Power BI
Implementing Data Governance in Power BI: Most organizations make informed decisions based on historical data trends and patterns. Every organization needs to maintain security and confidentiality. The IT/BI team is responsible for designing data collection, cleaning, and cataloging processes. They also develop rules for report creation and distribution within the organization. Every organization needs a robust and secure data governance system and framework. Data governance involves defining and enforcing policies, procedures, and standards for data management.
Effective Power BI data governance ensures data integrity, security, and compliance, enabling data practitioners to utilize Power BI for analysis and reporting. Power BI provides tools to support this, ensuring data accuracy, reliability, and security. These include dataflows, datamarts, sensitivity labels, endorsement, discovery, and row-level security (RLS). Dataflows allow organizations to connect, clean, and transform data, while datamarts provide a centralized data repository. Sensitivity labels help to classify and protect sensitive data, while endorsement allows organizations to enforce data quality standards. Finally, Discovery helps organizations manage, monitor, and understand their data assets. Now we have Microsoft Fabric, which will be one source of truth for data in organizations implementing Microsoft Fabric.
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