Client: Wireless Telecommunications
System Requirement: Data quality and integrity
Technology Platform: Business Objects

Description:
As a national wireless telecommunications provider, our client generates a high volume of data regarding the state of their network equipment and cell sites. They required a business intelligence solution that would present analytical information regarding the quality and integrity of this data in the form of reports and management dashboards. The data is held in numerous source systems and requires significant analysis and transformation.

The first phase of the project entailed creating a presentation layer to display the analytic information enabling national, regional and local views on the analytics and the aggregated and detailed data that drive them. Utilizing the Business Objects Enterprise platform including Dashboard Manager and Performance Manager, Mantis quickly created a multi-tiered set of dashboards and InfoView reports to represent the information.

The second phase of the project required the automated extraction of the source data, followed by transformation and analysis of this data to understand issues including data quality, completeness and integrity. Mantis then worked with the client to design a dimensional model to hold the extracted and transformed source data in a data warehouse to be used as the source for the dashboards and reports. Business Objects Data Integrator provided the underlying ETL platform to complete the automated process. Using Data Integrator, Mantis was able to create an automated and scheduled system to extract data on a daily basis from source systems, analyze and transform the data and load it into the dimensional model for processing by the analytics, dashboards and reports.

Two key benefits resulted from this effort: (i) the presentation layer created now affords tremendous flexibility and power in visualizing the trends in the data at all layers in the organization – stakeholders at a national, regional and local level are all able to drill directly into the information they need to improve their business processes, (ii) the manual effort of extracting data form numerous sources and working through a process that took weeks to generate a single aggregated data point across the organization, now runs as an automated process on a scheduled nightly basis – reducing the cost of labor and providing a daily level of granularity on the data rather than weekly or bi-weekly.