Faster Power BI Reports and Better Use of Premium Capacity: How We Helped Pernod Ricard Do It

40%

Improvement in overall capacity consumption (CUs)

75%

Faster query times in reports

46%

Reduction in S&OP data model size

Background

Pernod Ricard is a leading global wine and spirits manufacturer with over 10,000 employees and over $10B in revenue annually. Headquartered in Paris, France, the company has a significant global presence, operating in over 160 countries. With operations and distribution networks spanning the globe, Pernod Ricard has a strong foothold in key markets across the Americas, Asia-Pacific, Europe, the Middle East, and Africa. Pernod Ricard owns a wide array of internationally recognized brands, including Jameson, Absolut, and Malibu.

Challenge

Our customer has grown their Power BI analytics environment significantly in recent years. It is now supporting thousands of users globally across every department. With this growth, they've started to have challenges managing their Power BI Premium capacities which supply the horsepower to analyze large complex datasets. This caused challenges including slow reports, refresh failures, and platform downtime.

Approach

Keyrus began by analyzing the existing workloads, prioritizing the highest impact reports and dataflows, and then began analyzing to identify next steps. Optimizations were implemented, and a process and best practices were documented to ensure the client can continue to apply this methodology to future workloads, ensuring long-term scalability

Key results

Reduced CU consumption improves platform stability, reducing downtime, and decreases the need for adding costs to handle additional reports and workloads

Faster query times improve user experience, improve adoption, and help the organization continue being more data driven.

Reducing data model size improves query times, refresh times, platform stability, and ensure scalability in the long term.

Benefits

The work done by our experts helped set up Pernod Ricard for success in the long run by stabilizing their analytics platform, providing capacity and best practices to continue scaling to meet the data analytics needs of the business.