Due to the low technology base, most African national statistical systems use mostly manual, paper-based data collection methods for specialized surveys, which provide the bulk of statistical data. Paper-based methods involve printing of the paper questionnaires, transporting them across to the fieldworkers, and getting them back to a central location. The lengthy processes not only delay the production of data for decision making, but also require a lot of personnel for data collection and capture, thereby exacerbating the financial constraints. Due to these and other problems, computer assisted interviewing (CAI) methods are increasingly replacing pen-and-paper methods of survey data collection. The advantages of CAI methods include: automatic transfer of the survey to central database; automatic validity checks; automatic data cleaning, more control of question sequencing by the interviewer; easier to scale up (or down) and adapt for other surveys; more privacy due to the reduction in intermediate processing and cleaning, and concomitant reduction in operators involved. One of the disadvantages of CAI methods is the initial and running costs of computer hardware and related infrastructure. However, the increasing power of handheld devices such as mobile phones, tablets and personal data assistants (PDAs) has resulted in affordable CAI alternatives. While accepting that the problems of statistical capacity are multi-faceted, this project targets the problems associated with data collection, particularly the technology issues. It makes use of the existing statistical structures in national statistical systems (NSS) to test, adapt and where necessary develop new mobile data collection systems. It seeks to ensure that NSSs are empowered to produce reliable, comprehensive, timely and integrated statistics by utilizing CAI methods on android powered devices. The android OS is being adopted because it is currently the most commonly used platform for tablets and smartphones, compared to similar platforms such as iOS and windows mobile and is supported by many different device manufacturers. The scope of the project includes developing a model data import and processing system for consolidating the collected data into a single database, based on agreed processes and data exchange mechanisms.