We have almost reached the end of our journey by arriving at the last phase of the M.E.A.L. system, data utilization, which many believe is the most important phase. By getting to this point, some of the important steps in the data analysis process are complete, but the data you’ve just analyzed can be difficult to understand because it is extensive, complex, and is scattered across a collection of spreadsheets and documents. For this, there is a need to do some work on data visualization and interpretation.
As we anticipated in the series on data analysis, data visualization is the process of taking data and displaying it in a graph, image, or table to make it easier to understand. Through analysis, then, you can discover relationships and patterns in the data. Interpreting the results, on the other hand, involves thinking about the patterns that emerged from the data set. You can visualize the data through a series of steps: the first is to define the audience, identifying who you want to reach, so you can make the visualization for that audience. Then define the content of the visualization, check your communication plan for stable “need-to-know” content for each of the identified stakeholders (audiences). Once these aspects are defined you can design and test your visualization, remembering to keep it simple. For each identified key audience, you may need to design different visual tools.
The last step is to build your visualization. Some simple charts and graphs can be created in Microsoft Excel, but for more complex visualizations you may need the assistance of an expert who is skilled in digital software and data visualization.
After you have collected, analyzed, and visualized your data (both quantitative and qualitative), you must interpret it. This process results in an explanation to convey when you share the results with stakeholders or need to make a project decision. On the M.E.A.L. planning documents you will also find guidance on when to interpret the data. Typically, it occurs after analysis and visualization, although the process is often iterative. In fact, an initial interpretation may lead to the need for further data collection, which would lead to further analysis and interpretation and so on. There are several recommended practices to make the data interpretation process as quality as possible. First, you will need to create visualizations of the results to help people understand the data, ensuring that your visualizations give a complete picture and are not misleading. Then you will need to triangulate the quantitative and qualitative results together so you can compare them. At this point, you will need to convene a stakeholder meeting to compare and have multiple perspectives on the data you obtained. This is needed because any type of recommendation suggested during the comparison group will be different depending on whether it comes from a person on the project team in the field, a beneficiary, or one of the central staff. The next step is to plan an appropriate amount of time to analyze and interpret the data. In fact, these processes take time and it is important that they be part of the overall project implementation plan. Lastly, make sure the roles and responsibilities in the interpretation process are clear. Normally, the M.E.A.L. team does the initial analysis while project staff organizes and facilitates the interpretation process.
Your interpretation will be considered more valid if you can clearly demonstrate that it is based on data that directly support it. It will also be considered more integral if you can demonstrate that it is based on data collection and analysis processes that are as free from error and bias as possible.
To validate your data analysis conclusions and to validate the themes that emerged, the easiest way is to ask the data sources directly. They will be able to tell you if you were able to correctly capture their opinions and thoughts through the themes you generated. To promote this dynamic you might ask others, or do it yourself, to take on the role of the “skeptic” by asking interested parties things like, “What if what I found was NOT true?”. Through this process of further exchanging views and opinions with participants in your data collection, you will be able to make your findings actually valid, reliable, and usable, in the sense that they can actually inform changes (in project implementation, but also in policies).
In this last module, we will take revisit the concept of learning in the M.E.A.L. system to then explore data limitations, learn from data, and how to create reports for accountability.
MODULE 8: Using M.E.A.L. Data – Download
Find the previous modules here.
If you are interested in knowing more about project writing and evaluation and would like to have the assistance of professionals, you can email us at email@example.com. At SIGNIS Services Rome we are experts in the sector and have been involved in project writing for the creation and development of communications projects all over the world for decades.
*This content was curated by Valeria Appolloni and inspired by the materials published on Kaya, published for non-commercial and educational use.