Within Weeks Boat Manufacturer Gains Data-Driven Insights with Self-Service Reporting

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Client Profile & Challenge

Our client is a Private Equity-backed National Manufacturer of Premium Boats

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, threatening profitability and operational efficiency. Deeper analysis uncovered that because there was no Enterprise Data Warehouse or BI 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, more common issues were uncovered, as most processes were manual, error prone, and lacked the self-service delivery aspect that the business desired.

Solution Overview

Following a BI Roadmap engagement, Messina Group created a solution architecture to build the client an Enterprise Data Warehouse to provide the foundation for self-service reporting and analytics. The EDW was built utilizing our Analytics Hub data integration platform, which decreased development time by months by leveraging our pre-built templates for ETL and Audit functionality.

The EDW Roadmap was also created to deliver the project in manageable phases to allow the client to invest incrementally and realize business value at regular project intervals.

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

Solutions:

Business Advisory, Digital Roadmap, Analytics, BI Platform Selection

Technology Used:

Microsoft Power BI, SSRS, QlikView, Analytics Hub (AaaS)

“AFTER” BUSINESS VALUE CREATION

Within weeks of implementing the Analytics Hub and integrating several disparate source systems, 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.

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