Explanation:
1. Only use MDC if you have the time to do it properly
Why? We have seen too many data collections where forms have not been conceived properly, the paper form being “simply” put on mobile with no reaping of the benefits of MDC.
As a consequence, enumerators might be blocked from doing their work in the field, if the GPS point is mandatory but the phone has a technical issue making it impossible to capture it for example, or a question does not have a comprehensive list of all possible answers – and this just should not happen!
Lesson 1: Stick to paper if you are not able to allocate sufficient time to plan your MDC You may also check out our “Mobile data collection: more quality, less cleaning!” blog post.
2. Focus on “analysis” from the beginning
Why? Building your analysis into your data collection tool helps you become more efficient before, during and after the data collection. In the first place, it encourages the right level of reflection before the data collection actually begins to ensure that all you will need (and only what you need!) in your analysis is indeed included.
It then makes it possible to check your results regularly while the data collection is still ongoing without making the process too long and dreary and -finally- it makes your analysis quicker and more efficient after the data collection is over.
Lesson 2: MDC is an enabler to improve the way you analyze your data
3. Don’t over complicate tools if you want to be sustainable
Why? Consider the time for capacity building of your teams and the turnover rate of your field operations before choosing to deploy a very advanced statistical or mapping tool. It may seem that these sophisticated solutions perfectly answer your need at a given moment but they are often not maintainable over time – due to its financial or long-term knowledge-building cost.
Same goes for your form conception: aim for “good enough” rather than “perfection”: above all, aim to ensure that the form is useful, without being overloaded with constraints and tweaks that a non tech-savvy form author might not be in a capacity to understand.
Lesson 3: “Easier is better” when it comes to analysis tools
4. Only use MDC when you need it
Why? MDC can sometimes be a victim of its success and used to answer all data collections needs, even when it is not appropriate or relevant. However, its usage should be dependent on the data collection needs and the M&E plan of a project and used concurrently with the qualitative methods that make sense (observation, key informant interviews, focus group discussions, etc.).
These qualitative methods – often under-used as they require more reflection or are less enticing in the approach – often provide information of great value to many of the questions asked in the context of a humanitarian project.
Lesson 4: Integrate the MDC implementation in the M&E approach to make it more impactful
5. Be sure to assign roles & responsibilities!
Why? Defining roles and responsibilities can be seen as the cornerstone of any new process that requires change management, and this is most certainly the case when one wants an institutional MDC approach. MDC should not only require the involvement of the Information Management or M&E teams but should be done with a strong commitment of all program teams.
This will help support a higher-quality data collection by ensuring a proper ownership of the process by the program teams. It will also optimize the technical skills needed by each team, encouraging program teams to use MDC for other purposes than M&E, such as the collection of data for the daily running of an activity – an often underused application of MDC.
Lesson 5: Ensure roles and responsibilities between M&E teams and Program Managers are clearly spelled out
6. Use MDC to improve the monitoring of your teams
Why? When you have an internet connection that permits it, MDC enables you to have a better understanding of what is going on in the field. It can support you in checking up on the content of the data collected regularly to see if anything has been misunderstood or isn’t being done as it should so as to make timely feedback before this impacts data quality. It is particularly useful to monitor data collections that occur in remote areas or in several locations at once.