Bringing Data Management for Analytics into the Era of AI
Webinar Speaker: David Stodder, Senior Director of Research for BI, TDWI
Date: Thursday, September 14, 2023
Time: 12:00 p.m. PT / 3:00 p.m. ET
We have entered the era of artificial intelligence (AI). To compete, organizations are under pressure to update data management for analytics. It’s vital to take advantage of AI-infused automation in tools and data platforms to accelerate growth in next-generation data applications and the development of game-changing data insights. Users are demanding integrated and unified experiences across data discovery, integration, cleansing, and governance. TDWI research shows that modernizing data governance—for improving data trust and adherence to data privacy regulations—is a critical common priority.
Microsoft Fabric provides an end-to-end intelligent data platform to drive BI, analytics, and data science insights across diverse data landscapes. How do you augment these analytics with modern data management to create a unified, AI-powered analytics platform? What are best practices for maximizing the value of AI-infused automation capabilities to streamline data life cycles, improve self-service analytics experiences, and realize higher return on data assets?
Join this TDWI Webinar to learn answers to these questions and bring your data management for analytics into the era of AI. Along with TDWI research perspectives, you will hear how Informatica, a design partner of Microsoft Fabric, addresses requirements for improved data understanding and identification of data quality and privacy issues to deliver trusted data for analytics.
Topics this webinar will cover include:
- TDWI insights into how to address challenges and successfully bring data management for analytics into the era of AI
- How Informatica’s AI-powered platform can simplify and automate data management tasks for Microsoft Fabric
- Tips for integrating modern data management with Microsoft Fabric analytics services
- The role of AI techniques such as natural language processing (NLP) for creating flexible yet automated data cleansing rules
- Best practices for curating and governing data for analytics so you can make best use of a data marketplace