Key Takeaways: Data integrity is required for AI initiatives, better decision-making, and more – but data trust is on the decline. Data quality and data governance are the top data integrity...
Key Takeaways: Automation adoption is gaining momentum as a core component of digital transformation strategies – but integration and multi-dimensional complexity remain top challenges. Most...
Key Takeaways: MDM delivers a unified holistic view of your data across domains, so you can make faster, more accurate decisions. Challenges around data literacy, readiness, and risk exposure need to...
Key Takeaways: Data used for personalization must be of high quality—accurate, up-to-date, and free of redundancies. 4 Practical Tips for Implementing Data-Driven Personalization in your...
Key Takeaways: In the face of ransomware attacks, a resilience strategy for IBM i systems must include measures for prevention, detection, and recovery. Built-in security features and enterprise-wide...
Key Takeaways: Data quality is the top challenge impacting data integrity – cited as such by 64% of organizations. Data trust is impacted by data quality issues, with 67% of organizations saying...
Key Takeaways: Data mesh is a decentralized approach to data management, designed to shift creation and ownership of data products to domain-specific teams. Data fabric is a unified approach to data...
Key Takeaways: In the insurance industry, customer satisfaction has a direct impact on your bottom line. Efficient claims processing and transparent communications are key to customer satisfaction....
Key Takeaways: Cloud migration enhances agility, cuts operational costs, and helps you stay compliant with evolving regulations. Maintaining data integrity during cloud migration is essential to...
