IBM Big Data Hub

AUGUST 15, 2022

How does a data architecture impact your ability to build, scale and govern AI models? To be a responsible data scientist, there’s two key considerations when building a model pipeline: Bias: a model which makes predictions for people of different group (or race, gender ethnic group etc.) Data risk assessment.

Data mining 60

Data mining Risk Modeling Risk Management 60

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

DataKitchen

OCTOBER 11, 2021

First, you must understand the existing challenges of the data team, including the data architecture and end-to-end toolchain. Based on business rules, additional data quality tests check the dimensional model after the ETL job completes. A DataOps implementation project consists of three steps.

Testing 152

Testing Metadata Dashboards Statistics 152

CIO Business Intelligence

JULY 28, 2023

This strategic initiative also makes data consistently available for insight and maintains its integrity. Without a coherent strategy, enterprises face heightened security risks, rocketing storage costs, and poor-quality data mining. Many enterprises have become data hoarders, however.

Risk 98

Risk Management Data mining Cost-Benefit 98

CIO Business Intelligence

AUGUST 9, 2022

Data engineers and data scientists often work closely together but serve very different functions. Data engineers are responsible for developing, testing, and maintaining data pipelines and data architectures. Data engineer vs. data architect.

Analytics 131

Analytics Data Science Statistics Unstructured Data 131

AWS Big Data

AUGUST 1, 2024

Migrating to Amazon Redshift offers organizations the potential for improved price-performance, enhanced data processing, faster query response times, and better integration with technologies such as machine learning (ML) and artificial intelligence (AI). This exercise is mostly undertaken by QA teams.

Data Warehouse 99

Data Warehouse KPI Optimization Cost-Benefit 99

Jedox

OCTOBER 22, 2020

In addition to using data to inform your future decisions, you can also use current data to make immediate decisions. Some of the technologies that make modern data analytics so much more powerful than they used t be include data management, data mining, predictive analytics, machine learning and artificial intelligence.

Data Analytics 69

Data Analytics Analytics Unstructured Data Data mining 69