Monitoring and Evaluation professionals are responsible for designing, coordinating and implementing the learning framework of a project. While an M&E professional might be able to conduct project planning and data analysis on their own, they need the collaboration of field teams to provide data that is trustworthy and of high quality.
M&E researchers have a very important and symbiotic relationship with their data collection teams. The quality of the data collected is vital for these professionals to evaluate outcomes, tell their story and plan future interventions. While an M&E professional might be able to conduct project planning and data analysis on their own, they need the collaboration of field teams to provide data that is trustworthy and of high quality.
The data supply chain is in essence a team sport. By monitoring the data collection at every stage, and maintaining effective communication with their field teams, M&E professionals can mitigate the risk of “garbage in, garbage out” and maximize the impact of their work.
This article seeks to cover four simple measures that M&E professionals can implement to increase the success and utility of data collection.
Thorough testing of the data collection tool is fundamental for any research project. The chance of a tool not working properly in specific settings - i.e. an area with no internet connection - or an unreliable tool that reports erroneous data is a risk that is too high for any professional. Therefore piloting the platform and visualising the data is an important step in increasing the success of your data collection.
A common issue in field data collection, especially in remote and rural areas, is internet connectivity. Simple preliminary steps can be taken, for example, investigate the internet coverage of where the field team will be working. If data collection will occur in a settings with slow or no internet, researchers can use an offline-enable data collection app, like Teamscope.
Planning is key. One of the most forward-thinking measures a development organization can take is to disseminate (publicly) their M&E data. Data sharing allows organizations to increase transparency with stakeholders, donors and their communities. While data dissemination is a straightforward step towards openness, organizations can only share data if they are protecting the confidentiality of participants and respecting their rights.
Data sharing is further only possible if it was appropriately planned early on in the project. An organization may only realise they want to share data when it is already too late. Have consent forms for sharing de-identified data been collected from participants? How will the data be shared with stakeholders? You can find simple steps to answering these questions on our blog post on how to How to Successfully Share Research Data.
An M&E professional’s worst nightmare is to realize after data collection, that data is plagued with errors and cannot be used. This realization often comes at a point where teams have returned from the field, and resources have been spent. At that moment, unfortunately, it’s too late, and the damage is done.
Piloting and thoroughly testing your forms only mitigates the possibility of things going wrong, but in reality, risks will always exist. To completely assure that issues are detected early on, learning professionals can make use of data capture applications that support Query Management.
Query management is the ability of data collection platforms to identify data entries with issues and isolate them into a report. For every out of range or inconsistent value, the data capture tool generates a data query. Each data issue becomes an entity in itself and thus can be tracked over time to see if it is still present, or if it has been resolved by someone in the team.
A query management system substantially minimizes and potentially eliminates the risk of invalid data remaining unnoticed.
The goal of data collection is to facilitate the understanding of the world around us, and ultimately to help us tell a story. The primary outcomes of data collection are spreadsheets of tabulated data, and this certainly does not help in insight generation, on the contrary, it makes it harder.
To optimize the effectiveness of data collection, M&E professionals should become familiar with data visualization tools, learn how to build data dashboards and ideally be capable of visualizing their data continuously as it is collected by their teams on the ground.
While paper-based data collection requires data to be manually transcribed for it to be visualized, modern computer-assisted personal interviewing (CAPI) or Electronic Data Capture (EDC) applications have built-in data visualization features or allow the data to be integrated with other tools for real-time data visualization.
Monitoring and evaluation is only a part of the data supply chain. By understanding the entire workflow, from designing a digital tool, data analysis and dissemination, learning professionals can avoid common issues with data collection and assure their work is maximized through effective dissemination of results.
For M&E professionals that are interested in expanding their project design skills a must go resource are the Principles of Digital Development, a set of guidelines that promote best-practices in technology-enabled programs for international development.