Tamr and Microsoft Azure partner to break down data silos and enable cloud-based data mastering for analytics-ready data.

The article discusses the importance of DataOps in the life science industry and how Tamr plays a role in it.

Ontologies are used to share standardized information, and Tamr has developed a data model to maximize their potential.

Cloud providers are emphasizing the use of cloud-native capabilities, machine learning, and data migration to improve business functions and value.

The array discusses common blunders in data analytics, including not utilizing the cloud, neglecting data cleaning, and outsourcing new technologies.

Financial institutions face challenges in adopting public cloud partnerships due to data sovereignty concerns, talent acquisition difficulties, and upfront costs.

The US government struggles with transparency and efficiency in contract spending, but data mastering can help optimize spending.

Financial institutions are increasingly migrating to public cloud due to forced digital transformation, proof of concept, data flexibility, and industry-specific solutions.

DataOps is a methodology that combines people, process, and technology to efficiently manage and leverage data for organizations.

AWS revolutionized IT by allowing developers to set up cloud infrastructure easily, but faced issues with server management. DevOps improved reliability and collaboration.

A survey by Tamr reveals that many US financial institutions are struggling with digital transformation and data management.

This array describes Tableau extensions that help increase analytic adoption and drive value from data during uncertain times.

The global pandemic has exposed the fragility of the global supply chain, requiring supply chain leaders to take proactive actions to mitigate risk.

The array discusses how Chief Data Officers can positively impact businesses during the COVID-19 crisis by leveraging data for decision-making.

Tamr helps analyze and manage complex data in the managed care industry, providing insights for business strategy and decision-making.

Traditional approaches to data mastering in spend analytics are outdated and ineffective, while Agile Data Mastering offers scalability, speed, and accuracy.

Data is crucial for financial companies, and reference data has evolved to include external sources. AI/ML solutions can help manage reference data efficiently.

Tamr's machine learning capabilities simplify data management, metadata enrichment, schema discovery, and data integration for organizations.

Financial Services organizations face increasing pressure to improve their AML compliance programs to prevent illicit activities and reduce non-compliance risks.

Organizations invest in software to track datasets, but most data catalog solutions do not deliver at scale. Tamr's metadata enrichment helps solve this problem.