article thumbnail

Financial Intelligence vs. Business Intelligence: What’s the Difference?

Jet Global

This practice, together with powerful OLAP (online analytical processing) tools, grew into a body of practice that we call “business intelligence.” A few decades ago, technology professionals developed methods for collecting, aggregating, and staging their most important information into data warehouses.

article thumbnail

Navigating Data Entities, BYOD, and Data Lakes in Microsoft Dynamics

Jet Global

Consultants and developers familiar with the AX data model could query the database using any number of different tools, including a myriad of different report writers. Data warehouses gained momentum back in the early 1990s as companies dealing with growing volumes of data were seeking ways to make analytics faster and more accessible.

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

Unlock scalability, cost-efficiency, and faster insights with large-scale data migration to Amazon Redshift

AWS Big Data

About the authors Chanpreet Singh is a Senior Lead Consultant at AWS, specializing in Data Analytics and AI/ML. The data warehouse is highly business critical with minimal allowable downtime. We hope this post provides you with valuable guidance. We welcome any thoughts or questions in the comments section.

article thumbnail

Reduce IT Dependence for SAP S/4HANA Reporting

Jet Global

As the first in-memory database for SAP, HANA was revolutionary, bringing together the best characteristics of both traditional online transaction processing and online analytical processing.