IBM Journey to AI blog

OCTOBER 22, 2024

Organizations require reliable data for robust AI models and accurate insights, yet the current technology landscape presents unparalleled data quality challenges, specifically as the growth of data spans multiple formats: structured, semistructured and unstructured.

Data Silos 102

Data Silos Data Pipeline DataOps Business Intelligence 102

IBM Journey to AI blog

SEPTEMBER 5, 2024

Access to high-quality data can help organizations start successful products, defend against digital attacks, understand failures and pivot toward success. Emerging technologies and trends, such as machine learning (ML), artificial intelligence (AI), automation and generative AI (gen AI), all rely on good data quality.

Data Governance 75

Data Governance Data Quality DataOps Artificial Intelligence 75

Precisely

JUNE 26, 2023

Those who have already made progress toward that end have used advanced analytics tools that work outside of their application-based data silos. Successful organizations also developed intentional strategies for improving and maintaining data quality at scale using automated tools. The biggest surprise?

Data Quality 52

Data Quality Data Silos Data Governance Analytics 52

Precisely

FEBRUARY 9, 2024

They’re where the world’s transactional data originates – and because that essential data can’t remain siloed, organizations are undertaking modernization initiatives to provide access to mainframe data in the cloud. That approach assumes that good data quality will be self-sustaining.

Data Quality 69

Data Quality DataOps Data Silos Artificial Intelligence 69

Precisely

JULY 15, 2024

Even without a specific architecture in mind, you’re building toward a framework that enables the right person to access the right data at the right time. However, complex architectures and data silos make that difficult. It’s time to rethink how you manage data to democratize it and make it more accessible.

Data Governance 72

Data Governance DataOps Data Silos Data Pipeline 72

Alation

OCTOBER 12, 2021

Enterprise data analytics integrates data, business, and analytics disciplines, including: Data management. Data engineering. DataOps. … In the past, businesses would collect data, run analytics, and extract insights, which would inform strategy and decision-making. Evaluate and monitor data quality.

Analytics 52

Analytics Analytics Data Governance DataOps 52