Data Processing Data Quality Risk Risk Management 
CIO Business Intelligence
MARCH 18, 2024
There are a lot of risks and a lot of land mines to navigate,” says the analyst. Coming to grips with risk The first step in making any bet — or investment — is to understand your ability to withstand risk. This ensures that none of our sensitive data and intellectual property are availed to an outside provider.”
Cloudera
JULY 15, 2021
We recently hosted a roundtable focused on o ptimizing risk and exposure management with data insights. For financial institutions and insurers, risk and exposure management has always been a fundamental tenet of the business. Observe what the model has to offer even if not the intended output.
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DataRobot Blog
MAY 26, 2022
To start with, SR 11-7 lays out the criticality of model validation in an effective model risk management practice: Model validation is the set of processes and activities intended to verify that models are performing as expected, in line with their design objectives and business uses.
erwin
MARCH 25, 2021
erwin recently hosted the second in its six-part webinar series on the practice of data governance and how to proactively deal with its complexities. Led by Frank Pörschmann of iDIGMA GmbH, an IT industry veteran and data governance strategist, the second webinar focused on “ The Value of Data Governance & How to Quantify It.”.
O'Reilly on Data
DECEMBER 12, 2019
1] This includes C-suite executives, front-line data scientists, and risk, legal, and compliance personnel. These recommendations are based on our experience, both as a data scientist and as a lawyer, focused on managing the risks of deploying ML. That’s where model debugging comes in. Sensitivity analysis.
Cloudera
SEPTEMBER 7, 2023
But it’s also fraught with risk. This June, for example, the European Union (EU) passed the world’s first regulatory framework for AI, the AI Act , which categorizes AI applications into “banned practices,” “high-risk systems,” and “other AI systems,” with stringent assessment requirements for “high-risk” AI systems.
IBM Big Data Hub
DECEMBER 4, 2023
Clearly define the objective of the implementation project and determine its scope, timeline and budget as well as create a risk management plan. This is also the time to determine which data will be migrated, as some older data may be best stored in a secure archive.
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