Remove Data Architecture Remove Data Quality Remove Risk Remove Risk Management
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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.

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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. Strengthen data security. How erwin Can Help.

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How to Manage Risk with Modern Data Architectures

Cloudera

To ensure the stability of the US financial system, the implementation of advanced liquidity risk models and stress testing using (MI/AI) could potentially serve as a protective measure. To improve the way they model and manage risk, institutions must modernize their data management and data governance practices.

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3 new steps in the data mining process to ensure trustworthy AI

IBM Big Data Hub

How does a data architecture impact your ability to build, scale and govern AI models? To be a responsible data scientist, there’s two key considerations when building a model pipeline: Bias: a model which makes predictions for people of different group (or race, gender ethnic group etc.) Data risk assessment.

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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.

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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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Analyst, Scientist, or Specialist? Choosing Your Data Job Title

Sisense

Programming and statistics are two fundamental technical skills for data analysts, as well as data wrangling and data visualization. Data analysts in one organization might be called data scientists or statisticians in another. See an example: Explore Dashboard.