Wefi
Company Size
1,000+
Product
- Sisense
- Sisense ElastiCube
Tech Stack
- SQL
- Sisense ElastiCube
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Customer Satisfaction
- Digital Expertise
- Productivity Improvements
Technology Category
- Analytics & Modeling - Big Data Analytics
- Analytics & Modeling - Predictive Analytics
- Application Infrastructure & Middleware - Data Visualization
Applicable Industries
- Software
- Telecommunications
Applicable Functions
- Business Operation
- Quality Assurance
Services
- Software Design & Engineering Services
- System Integration
About The Customer
WeFi provides technology for optimizing and managing Wi-Fi networks. For mobile operators, WeFi acts as a data offloading tool in overloaded cellular networks. For end users, WeFi makes it easier for them to find and enjoy optimal wireless connections around the world. WeFi’s tools allow its user community to map the global Wi-Fi network with minimal effort, as over 75,000,000 access points have been identified around the world. Millions of WeFi users are generating seriously big data, and the database team deals with an average table size of more than 5 million rows for behavioral data, and over 500 million rows for activity records of some of the most active clients. Reporting on this activity is essential to helping the company’s management team make decisions.
The Challenge
WeFi’s database team had been manually running SQL queries, but they struggled to generate the reports that gave the management team crucial feedback. WeFi needed to perform advanced analysis on large amounts of data in three categories: the behavior of millions of WeFi users, including retention activity and data acquisition activity; the performance and activity of wireless networks to which its users are connected; and the activity records of active clients. The average table sizes for these categories were more than 5 million rows, 70 million rows, and 500 million rows respectively.
The Solution
WeFi carried out its initial Sisense implementation by loading data as-is into a single Sisense ElastiCube, and additional stores were added to the server later on. A dedicated PC machine serves as the ElastiCube server, and today the WeFi team runs the majority of its business reporting and dashboards on Sisense, using a total of six ElastiCube data stores. Business users today have the autonomy to create custom dashboards and reports, which dramatically reduces the time it takes to get new data insights; lightning-fast query response times mean answers to key strategic questions are available faster. Meanwhile, the database team can focus on crucial tasks without spending hours writing and running SQL queries.
Operational Impact
Quantitative Benefit
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