In Use case



As part of their mission for child welfare, C-SEMA operates a toll-free “National Helpline” (116).Witnesses to Violence, Abuse, Neglect, and Exploitation (VANE) against children are able to make a report through the helpline, where they are connected to C-SEMA counselors who record and evaluate cases. Counselors may then provide advice to the caller, forward the incident to appropriategovernment agencies, or assign specialized C-SEMA staff to follow up.All reported incidents are recorded in a decentralized standalone systemthat C-SEMA shares with the government. C-SEMA usesthese data to understand (1) legitimate vs non-legitimate calls, (2) call categorization, and (3) counselor caseloads.



The toll-free nature of the National Helpline means that not all calls coming to C-SEMA are actual reports of VANE. Some are prank calls; some are disconnected before the caller speaks; some are abusive calls aimed at counselors. Such calls utilize C-SEMA resources in ways not intended or designed for proper monitoring and analysis, a situation that prevents the counselors – especially newly hired ones – and the system from operating at their optimal level.

C-SEMA realized it was not possible to block all non-legitimate calls at the network level, and it looked for ways to mitigate, monitor, and analyze what it could do with current call data. The biggest advantages that C-SEMA identified was to monitor performance of its counselors against legitimate calls and using this data for orientation of new

While the need to perform this analysis was clear, C-SEMA as an organization lacked the analytical skills and requisite tools necessary to analyze and visualize their data. Consequently, it was difficult for C-SEMA to provide evidence based analysis of the performance of the helpline and link that performance tothe organization’s programmatic targets.



Supported by dLab, C-SEMA is engaged in deeper data analysis leading to visualizations that will serve as decision support tools linked to the helpline. Their aim is to analyze disaggregated data to obtain a high-level understanding of theircounselors’ performance and the overall service delivery they have been providing over a period of three years (2013 to



Data scientists at thedLabbegan with a decentralized Excel-based system that C-SEMA used to track both legitimate and non-legitimate calls from 2013 to 2015. Joining the data allowed permitted analysis that was impossible with decentralized records. For example, for the first time, C-SEMA has been able to understand the performance  of  their counselors, in particular the efficiency with which their counselors handled non-legitimate  . On a broader scale, this insight reflects how the organization as a whole is handling the systematic disturbances created by non-legitimate  .


  • Since locations & callers numbers are not tracked in not-legitimate call records; tracking of the frequency of individual callers and distribution of total callers by location was a .


  • There are records for legitimate calls minus location details, sex, correct C-SEMA work hours, age ranges beyond the service level. These issues brought challenges in correct interpretation of


  • In early days C-SEMA had unique codes for each counselor(ANG, CMW, FKA, JFL, MMA, TCD, VTP) but in late 2014 use of unique codes for each counselor was phased out and more than one counselor was usingnew codes (XX1, XX2, XX3, …). This became a challenge in evaluating performance of an individual counselor beyond November



As a result of the analysis and visualization products developed by the dLab Data Science team in collaboration with C-SEMA, the organization will

Ability to improve the service by capturing secondary / derived data from the analysis such as how long people take to get acquainted with the helpline before reporting their incidences (silent & blank calls) .



C-SEMA implementsaNational Helpline service as part of its mission to protect child welfare in the Tanzanian


Tanzania Data Lab (dLab) isa national data hub to promote data innovations, literacy, data use and multi-stakeholder data collaborators.The dLab is promoting innovation and data literacy through a premier center of excellence


The Ministry of Health, Community Development, Gender, Elderly and Children (MoHCDGEC)are the first beneficiary of the interventions conducted by C-SEMA as C-SEMA reports to the ministry on the successes, failures and status of the helpline .

National Emblame







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