Powering Smart Media Buys with Sisense
Customer Company Size
Mid-size Company
Region
- America
Country
- United States
Product
- Sisense
- Tableau
Tech Stack
- PHP
- .Net
- Oracle
- MySQL
- MongoDB
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Productivity Improvements
- Customer Satisfaction
- Innovation Output
Technology Category
- Analytics & Modeling - Predictive Analytics
- Analytics & Modeling - Real Time Analytics
- Application Infrastructure & Middleware - Data Visualization
Applicable Functions
- Business Operation
- Sales & Marketing
Use Cases
- Real-Time Location System (RTLS)
Services
- System Integration
- Software Design & Engineering Services
About The Customer
Ignite Media Solutions is dedicated to enhancing the customer experience by partnering with clients to maximize the effectiveness of media expenditures. They help their partners improve brand performance, sales, and profitability through innovative tools and technology at every point of the consumer lifecycle. The company processes massive amounts of data, including transaction, demographic, and media performance data, to provide valuable insights and reporting. With a focus on improving customer experience and media effectiveness, Ignite Media Solutions is a key player in the media and entertainment industry.
The Challenge
Ignite Media processes massive amounts of data, maintaining approximately 3 TB of transaction, demographic, and media performance data. They had been building all their reporting internally using PHP and .Net, but it was becoming increasingly difficult to scale. Writing new reports from scratch to follow a 'hunch' could take weeks, making it impractical to test new ideas. The company had valuable data but lacked the resources to fully leverage it. Mazda Ebrahimi, the VP of Application Development, sought a solution that would allow them to produce results faster and more easily without sacrificing their intellectual property.
The Solution
Mazda Ebrahimi tested various solutions, including Tableau, before selecting Sisense for its ease of use, power, cost-effectiveness, and support. He set up a virtual machine with 128GB of RAM and loaded a table with 320 million rows and 500 columns for processing. Sisense was installed and set up within hours, and the results were nearly instantaneous for any queries. Ebrahimi created Elasticubes centered around specific topics, allowing analysts to work within those cubes without creating multiple reports. The system maintains these subsetted views of the data, enabling clients and internal users to use predefined dashboards to browse and customize queries. Sisense simplified complex data relationships by creating 'views' stored in the database, allowing users to create new cubes quickly and easily.
Operational Impact
Quantitative Benefit
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