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Metadata Management, Data Governance and Automation

erwin

erwin released its State of Data Governance Report in February 2018, just a few months before the General Data Protection Regulation (GDPR) took effect. Download Free GDPR Guide | Step By Step Guide to Data Governance for GDPR?. Data governance maturity includes the ability to rely on automated and repeatable processes.

Metadata 102
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What’s the Current State of Data Governance and Automation?

erwin

I’m excited to share the results of our new study with Dataversity that examines how data governance attitudes and practices continue to evolve. Defining Data Governance: What Is Data Governance? . 1 reason to implement data governance. erwin Named a Leader in Gartner 2019 Metadata Management Magic Quadrant.

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How to use foundation models and trusted governance to manage AI workflow risk

IBM Big Data Hub

This is where AI governance comes into play: addressing these potential and inevitable problems of adoption. AI governance refers to the practice of directing, managing and monitoring an organization’s AI activities. An AI governance framework ensures the ethical, responsible and transparent use of AI and machine learning (ML).

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The Impacts of Data Loss on Your Organization

Security Affairs

In a survey, it was found that 26% of businesses suffered some form of data loss in 2022, bringing to light worrisome statistics and further stressing the need for organizations to simply be more proactive in protecting their data. Examples : emails, social media posts, customer feedback, audio and video files, images, and documents.

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What is a data catalog?

Collibra

According to a survey conducted by Forrester, 84% of respondents see data as central to generating accurate business decisions. These seven must-have capabilities distinguish a robust, enterprise-grade, and governed data catalog from a data catalog that is tactical, siloed, and ultimately not successful across an enterprise. .

Metadata 107
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Bringing data quality and observability together: The ultimate stack to achieve healthy data

Collibra

The state of data quality in the 2020 O’Reilly survey marks the top issue as too many data sources, too little consistency. It leverages metadata for adding context around what is happening and what are its consequences. Examples: Did my data load on time? Example: Do my trades roll up into my positions?

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The 7 most common data quality issues

Collibra

Several surveys reveal the extent of cost damages across many verticals due to the problems associated with data quality. Some examples of poor data quality in business include: . Data quality significantly influences the organizational efforts in governance and compliance, leading to additional rework and delay. Hidden data.

Analytics 105