Unlock your data for fast insights: dimensionless modeling with in-memory column store
Key drivers or architectural goals for dimensional model
Date : March 23, 2016

New analytical data modeling approach that can drastically lower technical complexity of enterprise analytics data platform, reduce development costs, shorten time to value and support agile, incremental development.

Dimensional modeling (also known as star or snowflake schema) was pioneered by Nielsen in 70s, and is considered the standard data modeling technique for business intelligence and analytics with nearly every BI ad reporting tool on the market supporting or requiring it.  Both leading data warehousing methodologies, Inmon’s Corporate Information Factory and Kimball’s dimensional modeling, prescribe to use a dimensional model for exposing data to users. The Dimensional model provides a logical grouping of data by reporting usage (facts/dimensions), delivers good performance, enables cross-system integration and supports MDM (master data management). Why are we challenging something that has been proven to work well for over 40 years? 

This whitepaper explores key drivers or architectural goals for dimensional models.

West Monroe Insights
Improving and Optimizing Cross Dock Operations
View More
Related Insights
Driving business transformation with new customer experience and data analytics capabilities
Date : June 15, 2017

Partnering with West Monroe, BridgeHealth transformed its organization—establishing Salesforce, analytics, reporting, and contact center capabilities essential to further change. These new capabilities have improved efficiency and insight, and more importantly, they will help BridgeHealth deliver a better member experience and fulfill its mission of enabling high-quality care at a better value.

Date : June 15, 2017

BridgeHealth, a benefit-management organization, gained critical new Salesforce, contact center, analytics, and reporting capabilities to boost efficiencies and standardize for growth.

In a world of ever-increasing, real-time information and mounting demands from informed consumers, water utilities face exponentially greater difficulty when making business decisions.
Date : May 17, 2017

Water utility executives are starved for more insightful operational data. As a result, many water utilities are considering or investing in automated metering infrastructure (AMI) and related analytics capabilities.