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 282

Machine Learning Modeling Testing Risk Management 282

CIO Business Intelligence

MARCH 24, 2023

The Business Application Research Center (BARC) warns that data governance is a highly complex, ongoing program, not a “big bang initiative,” and it runs the risk of participants losing trust and interest over time. Informatica Axon Informatica Axon is a collection hub and data marketplace for supporting programs.

Data Governance 142

Data Governance Management Metadata Data Quality 142

erwin

APRIL 29, 2020

Many large organizations, in their desire to modernize with technology, have acquired several different systems with various data entry points and transformation rules for data as it moves into and across the organization. Regulatory compliance places greater transparency demands on firms when it comes to tracing and auditing data.

Key Performance Indicator 111

Key Performance Indicator Metadata Data Governance Data Quality 111

datapine

FEBRUARY 22, 2022

Improved risk management: Another great benefit from implementing a strategy for BI is risk management. IT should be involved to ensure governance, knowledge transfer, data integrity, and the actual implementation. Clean data in, clean analytics out. Because it is that important. It’s that simple.

Business Intelligence 218

Business Intelligence Strategy Cost-Benefit Dashboards 218

erwin

FEBRUARY 4, 2021

However, organizations still encounter a number of bottlenecks that may hold them back from fully realizing the value of their data in producing timely and relevant business insights. Overcoming Data Governance Bottlenecks. Put data quality first : Users must have confidence in the data they use for analytics.

Data Governance 89

Data Governance Metadata Data Quality Risk Management 89

CIO Business Intelligence

APRIL 28, 2022

Its success is one of many instances illustrating how the financial services industry is quickly recognizing the benefits of data analytics and what it can offer, especially in terms of risk management automation, customized experiences, and personalization. .

Analytics 98

Analytics Insurance Big Data Uncertainty 98

CIO Business Intelligence

SEPTEMBER 2, 2022

Finance companies collect massive amounts of data, and data engineers are vital in ensuring that data is maintained and that there’s a high level of data quality, efficiency, and reliability around data collection.

Finance 98

Finance IT Software Reporting 98