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Messina Creates Solution Architecture for Major Retailer’s Cloud Data Warehouse

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Client Profile & Challenge

Client Profile & Challenge

Our Client is a major retailer of custom merchandise, food service, and beverage supply.

Challenge: Our Client faced major issues with reporting and general access to information across the enterprise and was unable to generate business insights on a timely basis, and was threatening both profitability and operational efficiency. Our deeper analysis uncovered that because there was no Cloud Data Warehouse (CDW) or Analytics foundation in place, reporting needs were completely fulfilled with custom, static extracts owned by single individuals. Because these custom extracts came from data silos within IT, additional common issues were uncovered, as most processes were manual, error-prone, and lacked the self-service delivery aspect that the business desired.

Client Profile & Challenge

Solution Overview

Technology Used:

Microsoft Power BI, SSRS, and QlikView

Execution:

Messina started with an Analytics Roadmap engagement, then created the solution architecture to build our Client an Enterprise Data Warehouse to provide the foundation for self-service reporting and Analytics. The CDW was built utilizing our proprietary Analytics Hub data integration platform, which shaved months off of the development time by leveraging our pre-built templates for ETL and Audit functionality. The CDW Roadmap was also designed to deliver the project in manageable phases, allowing our Client to invest incrementally, and realize business value at regular project intervals.

To expose data in the CDW and create a climate of interactive, self-service Analytics, Messina deployed a combination of Microsoft Power BI, SSRS, and QlikView to the end-user community to satisfy the vast array of reporting needs.

Client Profile & Challenge

“After” Business Value Creation

Within weeks of implementing the CDW and integrating several disparate sources, our Client was already saving hundreds of hours of operational efficiency by eliminating the need to manually extract and aggregate information. Operational managers now had the ability to directly and quickly address inventory management issues – reducing costs and increasing profitability. Additionally, company executives and management had on-demand access to powerful interactive dashboards, granting them never before seen visualizations of their business, leading to better decision making.

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