datapine

SEPTEMBER 29, 2022

1) What Is Data Quality Management? 4) Data Quality Best Practices. 5) How Do You Measure Data Quality? 6) Data Quality Metrics Examples. 7) Data Quality Control: Use Case. 8) The Consequences Of Bad Data Quality. 9) 3 Sources Of Low-Quality Data.

Data Quality 241

Data Quality Metrics Data-driven Management 241

IBM Big Data Hub

JANUARY 9, 2024

Data ingestion must be done properly from the start, as mishandling it can lead to a host of new issues. The groundwork of training data in an AI model is comparable to piloting an airplane. The entire generative AI pipeline hinges on the data pipelines that empower it, making it imperative to take the correct precautions.

Enterprise 73

Enterprise Data Integration Data Quality Contextual Data 73

CIO Business Intelligence

JANUARY 30, 2024

“Always the gatekeepers of much of the data necessary for ESG reporting, CIOs are finding that companies are even more dependent on them,” says Nancy Mentesana, ESG executive director at Labrador US, a global communications firm focused on corporate disclosure documents.

Reporting 93

Reporting Data Quality Strategy Data-driven 93

Alation

FEBRUARY 17, 2023

This past week, I had the pleasure of hosting Data Governance for Dummies author Jonathan Reichental for a fireside chat , along with Denise Swanson , Data Governance lead at Alation. How do you get executives to understand the value of data governance? First, document your successes of good data, and how it happened.

Data Governance 52

Data Governance Data Quality Metadata Cost-Benefit 52

Alation

JULY 6, 2021

Modern data governance relies on automation, which reduces costs. Automated tools make data governance processes very cost-effective. Machine learning plays a key role, as it can increase the speed and accuracy of metadata capture and categorization. This empowers leaders to see and refine human processes around data.

Data Governance 52

Data Governance Metadata Data Quality Risk 52

Jet Global

FEBRUARY 23, 2024

The quick and dirty definition of data mapping is the process of connecting different types of data from various data sources. Data mapping is a crucial step in data modeling and can help organizations achieve their business goals by enabling data integration, migration, transformation, and quality.

Data Warehouse 52

Data Warehouse Reporting Data Transformation Sales 52

Domino Data Lab

OCTOBER 1, 2020

Combining the power of Domino Data Labs with Okera, your data scientists only get access to the columns, rows, and cells allowed, easily removing or redacting sensitive data such as PII and PHI not relevant to training models. For the compliance team, the combination of Okera and Domino Data Lab is extremely powerful.

Enterprise 105

Enterprise Metadata Cost-Benefit Data Processing 105