Customer data silos and dirty data hinder businesses from delivering exceptional customer experiences. Customer mastering can solve these issues.

The global supply chain has faced challenges due to the pandemic, leading to the need for proactive actions to mitigate risk and ensure business continuity.

Supply chains face daily disruptions due to external forces and data challenges, but Tamr Supplier Mastering helps address these issues.

Data-driven organizations are recognizing the importance of clean, integrated data and are embracing new roles and technologies for better data management.

The array discusses the need for organizations to embrace AI/ML for data mastering and highlights five principles for successful implementation.

Data mesh is growing in popularity as companies realize the need to enrich their data and debate centralization vs decentralization. Data mastering is crucial for success.

The Women Data Leaders Global Summit discussed the importance of diversity, data architecture, data culture, and the value of data-driven businesses.

Data mesh is a new approach to data management that emphasizes distributed data and ML-driven data mastering for success.

Companies investing in data management need seven critical functions: connectivity, schema mapping, data mastering, data enrichment, data stewardship, data transformation, and golden records.

Harnessing federal data and building a data-driven culture can improve fraud detection and decision-making in the U.S. government.

Data management is crucial for businesses to curate, clean, secure, and utilize data effectively for better decision-making and business value.

Data management drives efficiency by improving data quality, fostering trust, allowing scalability, promoting collaboration, and increasing data's value.

The U.S. Federal Government is working on a data-driven initiative to improve decision-making and efficiency across agencies.

Tamr and Dun & Bradstreet collaborate to offer Master Data as a Service, using machine learning to improve data quality and completeness.

DataOps is a data management method that emphasizes communication, collaboration, integration, automation, and measurement between data professionals.

Supervised machine learning combines the strengths of machines and humans to achieve the best outcomes in data mastering.

Fragmented healthcare provider data requires improved data entry standards and next-generation data mastering solutions for accurate reporting and enrichment.

Data-driven companies struggle with complex data, but data mastering can solve issues by enriching, categorizing, and creating golden records.

The article discusses the importance of clean data in a DataOps ecosystem and the role of data mastering tools.

The Gartner Data & Analytics Summit highlighted the importance of automated insights, value-centric data strategy, and MDM in driving business success.

The article discusses the problem of dirty data and suggests using a business context layer and machine learning to clean it.