• Creating Smaller Copies of Production Databases and Enabling Enterprise Data Privacy

    Integrated with the Informatica PowerCenter platform for built-in scalability, robustness, and enterprisewide connectivity to access any nonproduction database, Informatica® Data Subset™ and Informatica Persistent Data Masking™ offer a comprehensive data management solution for nonproduction environments regardless of database type, platform, or location.

  • Informatica Big Data Security

    By evaluating sensitive data’s location, protection, volume, proliferation, use, and value, organizations can create a risk-based view to help prioritize security investments and focus their data protection resources and processes.

  • Recommendations on How to Tackle the 'D' in GDPR

    This paper looks at common questions many organizations ask on their GDPR journeys. We call these entry point questions. To help answer each entry point question, we have laid out a set of capability requirements that we consider important and, aligned to each capability, is a technology use case for how each capability can be developed.

  • 20 Questions on the CCPA with Joe Bracken, Deputy General Counsel

    Personal data privacy is top of mind with massive data breaches in the news, along with new US privacy legislation taking effect. As high-profile regulation for the world’s fifth largest economy, the California Consumer Privacy Act (CCPA) starts enforcement on January 1, 2020. But how does the CCPA impact US organizations, their customers and ordinary California citizens, as well as other US States keeping a close eye with their own new rules? Unlike prior legislation that started with breach notifications, the CCPA now raises the stakes with data protection requirements and new user rights for transparency into how sensitive data is handled. Are you prepared for the CCPA?

  • AI-Driven Next Gen Analytics

    No matter which industry you’re in, analytics are critical to drive business insights and therefore results. However, 80% of time is spent on preparing data for analytics and AI/ML projects and only 20% on drawing insights from the data. Automation becomes a critical success factor for effective data lake management to drive faster insights.For faster analytics and insights, drive collaboration among your data user personas and operationalize AI in every single step of your data lake management supply chain:•Data discovery and cataloging•Data ingestion and integration•Data preparation and quality•Data security and governance