Remove Cost-Benefit Remove Metadata Remove Risk Remove Risk Management
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How to use foundation models and trusted governance to manage AI workflow risk

IBM Big Data Hub

As more businesses use AI systems and the technology continues to mature and change, improper use could expose a company to significant financial, operational, regulatory and reputational risks. It includes processes that trace and document the origin of data, models and associated metadata and pipelines for audits.

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Automating Model Risk Compliance: Model Development

DataRobot Blog

Addressing the Key Mandates of a Modern Model Risk Management Framework (MRM) When Leveraging Machine Learning . The regulatory guidance presented in these documents laid the foundation for evaluating and managing model risk for financial institutions across the United States.

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How Data Governance Protects Sensitive Data

erwin

With more companies increasingly migrating their data to the cloud to ensure availability and scalability, the risks associated with data management and protection also are growing. Lack of a solid data governance foundation increases the risk of data-security incidents. Data Security Starts with Data Governance.

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What is data governance? Best practices for managing data assets

CIO Business Intelligence

The Business Application Research Center (BARC) warns that data governance is a highly complex, ongoing program, not a “big bang initiative,” and it runs the risk of participants losing trust and interest over time. The program must introduce and support standardization of enterprise data.

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Success Stories: Applications and Benefits of Knowledge Graphs in Financial Services

Ontotext

This shift of both a technical and an outcome mindset allows them to establish a centralized metadata hub for their data assets and effortlessly access information from diverse systems that previously had limited interaction. internal metadata, industry ontologies, etc.) The solution brings many business benefits.

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Top 6 Benefits of Automating End-to-End Data Lineage

erwin

It’s paramount that organizations understand the benefits of automating end-to-end data lineage. Here are six benefits of automating end-to-end data lineage: Reduced Errors and Operational Costs. The actual job of backing things up will be managed by the system processes you set up for consistency and clarity.

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What Does Data Archiving Bring To Healthcare Intelligence?

Smart Data Collective

Many healthcare organizations also retain data for future research into care improvements or related projects, in which case it’s critical to ensure that when you decommission a data system , you also export and appropriately store any associated metadata. Consider, for example, an old piece of software used to manage healthcare data.