At which steps of your data collection is MDC more time efficient than paper-based data collection?
This is the last of a series of 3 blog posts published to present some of the figures produced by CartONG for the Lessons Learned paper reflecting on the Terre des hommes’ experience of implementing Mobile Data Collection projects in their programs over the last five years. The study itself will be published on this blog in early September (now available here). The first blog post regarding a “Typology of the world of MDC” is accessible here, and the second one about “The advantages and the disadvantages of MDC: a visual summary” is accessible here.
Please note that is published under a CC BY-SA 2.0 license.
Following-up on the previous figure, let’s have a thorough look at the aspect of Time saving. This element is commonly used as one of the strongest arguments for promoting the use of Mobile Data Collection, but is often misunderstood. The above figure compares the time approximately spent during each step of a full data collection process using MDC versus paper-based data collection.
One always hears that MDC saves time by producing ready-to-share and ready-to-analyze data without the need for double data entry, a very time-consuming step, both in terms of HR and of survey timeline.
However, setting-up a proper Mobile Data Collection implies a longer preparation phase, requiring proper planning. This is particularly the case in the development and testing of forms. Nonetheless, in a quality-oriented approach, the more time you allocate in your tool ahead of its deployment, the less time you will need to spend addressing errors during the stressful deployment phase and the faster you will be able to access high quality results.
In summary, what is important to note is that, although overall the data collection workflow is quicker through the use of MDC, the time gained occurs mostly during and after the data collection; while the preparation phase, if done as it should (i.e.with a strong focus on data quality and analysis) becomes longer.
This visual (you may download a PDF version here) is of course not a one size fits all representation of the situation as every data collection has its own specificities. However it aims at giving a synthesized vision based on CartONG’s experience of data collection over the last decade. We hope that it can support other humanitarian actors in their organisation’s or operation’s strategy in terms of data collection processes. Don’t hesitate to share your feedback by commenting the blog post or by contacting us here!
Related CartONG resources
- Blog Post “Mobile data collection: more quality, less cleaning!“, CartONG & UNHCR, November 2018
- Blog Post “All about regex() in xls form: when, how and examples in the humanitarian and development fields“, CartONG, March 2018