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. 8] Data about individuals can be decoded from ML models long after they’ve trained on that data (through what’s known as inversion or extraction attacks, for example).

Machine Learning 276

Machine Learning Modeling Testing Risk Management 276

CIO Business Intelligence

JANUARY 11, 2024

IT leaders expect AI and ML to drive a host of benefits, led by increased productivity, improved collaboration, increased revenue and profits, and talent development and upskilling. Ensuring data integrity is part of a broader governance approach organizations will require to deploy and manage AI responsibly.

Enterprise 128

Enterprise Uncertainty Reporting Risk Management 128

CIO Business Intelligence

MARCH 19, 2025

However, embedding ESG into an enterprise data strategy doesnt have to start as a C-suite directive. Developers, data architects and data engineers can initiate change at the grassroots level from integrating sustainability metrics into data models to ensuring ESG data integrity and fostering collaboration with sustainability teams.

IT 59

IT Data Governance Data-driven Metrics 59

CIO Business Intelligence

NOVEMBER 21, 2023

After all, 41% of employees acquire, modify, or create technology outside of IT’s visibility , and 52% of respondents to EY’s Global Third-Party Risk Management Survey had an outage — and 38% reported a data breach — caused by third parties over the past two years.

IT 137

IT Risk Cost-Benefit Data Science 137

Smart Data Collective

OCTOBER 26, 2023

Specialists foster a culture of security awareness within the company by hosting training sessions and making educational resources available. They also uphold relevant regulations and protect systems, data, and communications. This empowers employees to adequately support the firm’s security goals.

Technology 123

Technology Risk Measurement Data-driven 123

Cloudera

SEPTEMBER 7, 2023

Perhaps the biggest challenge of all is that AI solutions—with their complex, opaque models, and their appetite for large, diverse, high-quality datasets—tend to complicate the oversight, management, and assurance processes integral to data management and governance. AI-ify risk management.

Insurance 83

Insurance Risk Data-driven Finance 83

Domino Data Lab

APRIL 3, 2019

The longer answer is that in the context of machine learning use cases, strong assumptions about data integrity lead to brittle solutions overall. Probably the best one-liner I’ve encountered is the analogy that: DG is to data assets as HR is to people. Those days are long gone if they ever existed. a second priority?at

Machine Learning 84

Machine Learning Data Governance Metadata Data Science 84