O'Reilly on Data

JULY 15, 2019

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.

Machine Learning 52

Machine Learning Management Enterprise Risk Management 52

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. Sensitivity analysis.

Machine Learning 285

Machine Learning Modeling Testing Risk Management 285

CIO Business Intelligence

FEBRUARY 20, 2025

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.

Big Data 84

Big Data Measurement Visualization Machine Learning 84

erwin

APRIL 29, 2020

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.

Key Performance Indicator 111

Key Performance Indicator Metadata Data Governance Data Quality 111

Sisense

SEPTEMBER 3, 2020

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.

Statistics 97

Statistics Metrics Visualization Finance 97

Smart Data Collective

SEPTEMBER 25, 2019

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.

Technology 102

Technology Statistics Strategy Insurance 102

IBM Big Data Hub

NOVEMBER 28, 2022

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.

Metadata 56

Metadata Data Quality Data-driven Data Governance 56