Remove Data Architecture Remove Data Quality Remove Risk Management
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Are enterprises ready to adopt AI at scale?

CIO Business Intelligence

The path to achieving AI at scale is paved with myriad challenges: data quality and availability, deployment, and integration with existing systems among them. Another challenge here stems from the existing architecture within these organizations.

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How to Manage Risk with Modern Data Architectures

Cloudera

To improve the way they model and manage risk, institutions must modernize their data management and data governance practices. Up your liquidity risk management game Historically, technological limitations made it difficult for financial institutions to accurately forecast and manage liquidity risk.

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Using Strategic Data Governance to Manage GDPR/CCPA Complexity

erwin

It also helps enterprises put these strategic capabilities into action by: Understanding their business, technology and data architectures and their inter-relationships, aligning them with their goals and defining the people, processes and technologies required to achieve compliance. How erwin Can Help.

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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. Informatica Axon Informatica Axon is a collection hub and data marketplace for supporting programs.

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Very Meta … Unlocking Data’s Potential with Metadata Management Solutions

erwin

Addressing the Complexities of Metadata Management. The complexities of metadata management can be addressed with a strong data management strategy coupled with metadata management software to enable the data quality the business requires. With erwin, organizations can: 1.

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

erwin

Here are six benefits of automating end-to-end data lineage: Reduced Errors and Operational Costs. Data quality is crucial to every organization. Automated data capture can significantly reduce errors when compared to manual entry.

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4 Steps to Data-first Modernization

CIO Business Intelligence

From a policy perspective, the organization needs to mature beyond a basic awareness and definition of data compliance requirements (which typically holds that local operations make data “sovereign” by default) to a more refined, data-first model that incorporates corporate risk management, regulatory and reporting issues, and compliance frameworks.