Remove Data Quality Remove Risk Management Remove Statistics
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Managing machine learning in the enterprise: Lessons from banking and health care

O'Reilly on Data

After the 2008 financial crisis, the Federal Reserve issued a new set of guidelines governing models— SR 11-7 : Guidance on Model Risk Management. Note that the emphasis of SR 11-7 is on risk management.). Sources of model risk. Model risk management. AI projects in financial services and health care.

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Why you should care about debugging machine learning models

O'Reilly on Data

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. Sensitivity analysis.

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Why HR professionals struggle with big data

CIO Business Intelligence

By collecting and evaluating large amounts of data, HR managers can make better personnel decisions faster that are not (only) based on intuition and experience. However, it is often unclear where the data needed for reporting is stored and what quality it is in.

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What is Data Lineage? Top 5 Benefits of Data Lineage

erwin

For that reason, businesses must think about the flow of data across multiple systems that fuel organizational decision-making. The CEO also makes decisions based on performance and growth statistics. Regulatory compliance places greater transparency demands on firms when it comes to tracing and auditing data. Data Quality.

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

Sisense

Data scientists usually build models for data-driven decisions asking challenging questions that only complex calculations can try to answer and creating new solutions where necessary. Programming and statistics are two fundamental technical skills for data analysts, as well as data wrangling and data visualization.

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7 Advantages of Using Encryption Technology for Data Protection

Smart Data Collective

If you trust the data, it’s easier to use confidently to make business decisions. Statistics show that poor data quality is a primary reason why 40% of all business initiatives fail to achieve their targeted benefits. Ponder the statistics and points of focus here as you plan how to proceed.

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Maximize your data dividends with active metadata

IBM Big Data Hub

Provide early indicators of data quality. Poor data quality is one of the top barriers faced by organizations aspiring to be data-driven. Most data quality management approaches are reactive, triggered only when consumers complain to data teams about the integrity of datasets.