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Data visualisations for M&E reports

Monitoring & Evaluation (M&E) reports describe the outcomes and impacts of a project or programme along with conclusions and recommendations. It is typically encouraged to present outcomes and impacts using data visualisations since this can help to better understand the data, can increase engagement of stakeholders, and improves the overall readability of the M&E report.

Here I will share some data visualisations for quantitative data that go beyond the typical charts produced in Excel. Which type of visualisation best fits depends on the type of data and the information you want to convey. Generally, it is a good practice not to try to put too much information into one graph or figure and to include a clear descriptive title for all data visualisations. I also recommend to stick to one colour or colour palette throughout the M&E report so that it does not distract from the information in the data visualisation.

On a more general note, whenever presenting data, it is important to include information about how many data points were collected and a description of the methods used to collect the data. If the sample size is very small it can be misleading to show percentages in data visualisations. It may also be necessary to provide a breakdown of specific groups within the data, for instance women and men or different ethnic or age groups.

Below are a few examples of data visualisations for types of outcomes and impacts that are often reported in M&E reports. All graphs below have been made using Python, Matplotlib and Seaborn. All data is fictitious.

Visualising the performance of a key indicator against a target: stacked bar graph

A common example in M&E is showing the progress of a key performance indicator for a project or programme. To show how a key performance indicator has progressed against a target outcome, a horizontal stacked bar graph can be used.

The stacked bar graph below shows the performance of two key indicators: the number of youth enrolled and the number of youth employed, against the annual target for these two key indicators. It may be helpful to include several stacked bar graphs to show improvements over the duration of the project, for instance the performance of the key indicators against the annual target outcome for the different years of the project.

Figure 1. Percentage of youth (18 to 24 year old) enrolled and employed against the annual target.

Visualising the breakdown of a category: donut graph

The donut graph is a good way to visualise the contribution of individual categories to a whole. It can for instance be used to visualise the breakdown of a project budget or the percentage of different demographic groups enrolled in a training. A donut graph can be used to visualise a small number of categories, typically not more than 5 or 6. To compare a larger number of categories, a bar graph might be more useful.

The first donut graph below (figure 2) shows the breakdown of project spending on four different cost categories. The second donut graph below (figure 3) shows the breakdown of which expenses the project funding is used for.

Figure 2. Breakdown of project spending.
Figure 3. Breakdown of project funding.

Visualising the relation between two variables: heatmap

To visualise the relation between two variables, for instance differences in attitudes about vaccinations across different demographic groups, a heatmap can be used. A heatmap illustrates the magnitude of individual values (which can be counts or percentages) of individual values within a dataset as a colour hue or intensity.

The heatmap below shows the differences in the use of different types of risk management strategies across different types of organisations. The numbers indicate the percentages of the different types of risk management strategies used within each type of organisation. You can for instance easily see that the main risk management strategy used by NGOs is Transfer, and that the least used risk management strategy in the private sector is Reduction.

Figure 4. The use of risk management strategies per organisation type in %.

These were some data visualisations for impacts and outcomes that are commonly reported in M&E reports. It is important to chose the right chart type to ensure that the visualisation helps to effectively communicate the data in the M&E report. I will provide additional data visualisations for M&E in a future article.