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. Leverage AI to enhance governance. 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. Leverage AI to enhance governance. Focus on metadata management.

Data Analytics 59

Data Analytics Data Governance Data Integration Government 59

Databand.ai

JUNE 21, 2023

By doing so, data integrity tools enable organizations to make better decisions based on accurate, trustworthy information. The three core functions of a data integrity tool are: Data validation: This process involves checking the data against predefined rules or criteria to ensure it meets specific standards.

Data Integration 52

Data Integration Data Cleanse Data Validation Telecommunication 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

Precisely

JANUARY 8, 2024

Some businesses have heavily leveraged location intelligence for years – like real estate for property valuation, insurance for property risk assessment, and telecommunications for service coverage mapping. It’s worth noting, though, that to maximize the potential of location data, you need to address data quality issues first.

Insurance 64

Insurance Telecommunication Retail Data Integration 64