Data Quality Metrics Risk Management Strategy 
IBM Big Data Hub
FEBRUARY 28, 2024
Successful strategic sourcing often results in process optimization, cost management, customer satisfaction, risk management , increased sustainability and other benefits. Sourcing teams are automating processes like data analysis as well as supplier relationship management and transaction management.
CIO Business Intelligence
MARCH 18, 2024
When it comes to AI, Nafde sees risks in the vendors selected, the business-worthiness of the use case, and the cost of the initiative. The CIO has strategies in place to address all three. Our data team uses gen AI on Amazon cloud to explore sustainability metrics.
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FineReport
APRIL 9, 2024
Additionally, BI tools enable organizations to adopt a data-driven approach to strategy formulation, leading to more informed decision-making at all levels. The implementation empowered the organization with predictive modeling capabilities, enabling proactive risk mitigation and personalized customer engagement strategies.
O'Reilly on Data
DECEMBER 12, 2019
In addition to newer innovations, the practice borrows from model risk management, traditional model diagnostics, and software testing. There are at least four major ways for data scientists to find bugs in ML models: sensitivity analysis, residual analysis, benchmark models, and ML security audits. Data augmentation.
Alation
AUGUST 17, 2022
Data governance policy should be owned by the top of the organization so data governance is given appropriate attention — including defining what’s a potential risk and what is poor data quality.” It comes down to the question: What is the value of your data? Enterprise risk management.
DataRobot Blog
MAY 26, 2022
In this post, we will dive deeper into how members from both the first and second line of defense within a financial institution can adapt their model validation strategies in the context of modern ML methods. Figure 4: DataRobot provides an interactive ROC curve specifying relevant model performance metrics on the bottom right.
DataRobot Blog
JULY 21, 2022
Data scientists need to understand the business problem and the project scope to assess feasibility, set expectations, define metrics, and design project blueprints. If there is no forward-looking predictive component to the use case, it can probably be addressed with analytics and visualizations applied to historical data.
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