Precisely

NOVEMBER 18, 2024

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 challenges, and priorities. Focus on metadata management.

Data Analytics 72

Data Analytics Data Governance Data Integration Government 72

Precisely

NOVEMBER 18, 2024

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 challenges, and priorities. Focus on metadata management.

Data Analytics 59

Data Analytics Data Governance Data Integration Government 59

Snowflake

JANUARY 23, 2024

Snowpark ML Operations: Model management The path to production from model development starts with model management, which is the ability to track versioned model artifacts and metadata in a scalable, governed manner. The Snowpark Model Registry API provides simple catalog and retrieval operations on models.

Machine Learning 120

Machine Learning Metadata Python Telecommunication 120

Precisely

MAY 23, 2024

The same is true of systems that monitor trading exchanges, ATM transactions, telecommunications network outages, suspicious network traffic, and more. Real-time visibility of data is a competitive advantage. A notable capability that achieves this is the data catalog. That’s where real-time integration makes a difference.

Data Integration 64

Data Integration Metadata Telecommunication Data Governance 64

Precisely

MAY 23, 2024

The same is true of systems that monitor trading exchanges, ATM transactions, telecommunications network outages, suspicious network traffic, and more. Real-time visibility of data is a competitive advantage. A notable capability that achieves this is the data catalog. That’s where real-time integration makes a difference.

Data Integration 52

Data Integration Metadata Telecommunication Data Governance 52

Precisely

JULY 31, 2023

In the context of improving their organizations’ data integrity , respondents cite data quality and data integration as priorities for 2023 and as challenges to data integrity. Let’s explore more of the report’s findings around data integrity maturity, challenges, and priorities.

Data Integration 52

Data Integration Telecommunication Data Governance Government 52

Precisely

OCTOBER 5, 2023

When we think about the big picture of data integrity – that’s data with maximum accuracy, consistency, and context – it becomes abundantly clear why data enrichment is one of its six key pillars (along with data integration, data observability, data quality, data governance, and location intelligence).

Raw Data 69

Raw Data Insurance Datasets Telecommunication 69