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...
According to CIO magazine, “diversity is critical to IT performance. Diverse teams perform better, hire better talent, have more engaged members, and retain workers better than those that don’t...
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....
