IBM Journey to AI blog

SEPTEMBER 5, 2024

To maximize the value of their AI initiatives, organizations must maintain data integrity throughout its lifecycle. Every organization aims for up-to-date information, real-time market awareness, and insights to achieve optimal business results. Managing this level of oversight requires adept handling of large volumes of data.

Metadata 189

Metadata Data Quality Auto-classification DevOps 189

IBM Journey to AI blog

JULY 1, 2024

Extraction, transformation and loading (ETL) tools dominated the data integration scene at the time, used primarily for data warehousing and business intelligence. The first two use cases are primarily aimed at a technical audience, as the lineage definitions apply to actual physical assets.

ETL 100

ETL Automation Metadata Business Intelligence 100

ODSC - Open Data Science

SEPTEMBER 27, 2023

Understanding Data Lakes A data lake is a centralized repository that stores structured, semi-structured, and unstructured data in its raw format. Unlike traditional data warehouses or relational databases, data lakes accept data from a variety of sources, without the need for prior data transformation or schema definition.

Big Data 98

Big Data Metadata ETL Business Intelligence 98

Pickl AI

JULY 19, 2024

They enhance data integrity, security, and accessibility while providing tools for efficient data management and retrieval. A Database Management System (DBMS) is specialised software designed to efficiently manage and organise data within a computer system. Indices are data structures optimised for rapid data retrieval.

Data Integration 52

Data Integration Metadata Big Data Business Intelligence 52

Pickl AI

OCTOBER 16, 2024

Summary: Relational Database Management Systems (RDBMS) are the backbone of structured data management, organising information in tables and ensuring data integrity. Introduction RDBMS is the foundation for structured data management. Introduction RDBMS is the foundation for structured data management.

Data Integration 52

Data Integration Big Data Business Intelligence Machine Learning 52

Pickl AI

AUGUST 11, 2024

Consider factors such as data volume, query patterns, and hardware constraints. Document and Communicate Maintain thorough documentation of fact table designs, including definitions, calculations, and relationships. Use slowly changing dimension (SCD) techniques to capture historical changes and maintain data integrity.

Data Quality 52

Data Quality Business Intelligence ETL Data Integration 52

TransOrg Analytics

AUGUST 9, 2024

Data Integration: Integrates data from multiple sources, providing a comprehensive view for business intelligence. Consistency and Accuracy : Ensures high data quality with consistent formatting and validation. Historical Data Analysis : Analyzing historical data trends and patterns.

Machine Learning 52

Machine Learning ETL Big Data Data Analysis 52