erwin

DECEMBER 20, 2018

The driving factors behind data governance adoption vary. Whether implemented as preventative measures (risk management and regulation) or proactive endeavors (value creation and ROI), the benefits of a data governance initiative is becoming more apparent. Defining Data Governance.

Data Governance 40

Data Governance ROI Risk Management Metadata 40

erwin

JULY 12, 2019

In light of recent, high-profile data breaches, it’s past-time we re-examined strategic data governance and its role in managing regulatory requirements. for alleged violations of the European Union’s General Data Protection Regulation (GDPR). Given this, Oppenheimer & Co. How erwin Can Help.

Data Governance 110

Data Governance Management Metadata Risk Management 110

erwin

OCTOBER 31, 2019

percent of its global annual revenue, because 330 million hotel guests were affected by a breach in 2018. The Regulatory Rationale for Integrating Data Management & Data Governance. We don’t want you to be caught off guard when it comes to protecting sensitive data and staying compliant with data regulations.

Data Governance 105

Data Governance Metadata Risk Data Lake 105

Cloudera

SEPTEMBER 29, 2021

Through processing vast amounts of structured and semi-structured data, AI and machine learning enabled effective fraud prevention in real-time on a national scale. . This resulted in staff spending more time on more complex tasks while also reducing human errors and security risks.

Digital Transformation 80

Digital Transformation Data Governance Data-driven Uncertainty 80

Domino Data Lab

APRIL 3, 2019

Paco Nathan ‘s latest column dives into data governance. This month’s article features updates from one of the early data conferences of the year, Strata Data Conference – which was held just last week in San Francisco. In particular, here’s my Strata SF talk “Overview of Data Governance” presented in article form.

Data Governance 84

Data Governance Machine Learning Metadata Big Data 84

Cloudera

FEBRUARY 14, 2018

We share the same obstacles our customers face – most of which are around data collection, data quality (or lack thereof), data governance as well as misalignment or miscommunication about who is responsible and accountable for managing and analyzing different datasets and analytical outcomes.

Data-driven 43

Data-driven Data Quality Risk Interactive 43

Cloudera

FEBRUARY 15, 2024

From stringent data protection measures to complex risk management protocols, institutions must not only adapt to regulatory shifts but also proactively anticipate emerging requirements, as well as predict negative outcomes. This results in enhanced efficiency in compliance processes.

Insurance 85

Insurance Data Architecture Risk Management Risk 85