Data can be found almost anywhere, but unfortunately, it is often in an unstructured and unpolished way, which can diminish its values to stakeholders. Data Analytics & Visualization can be used in such cases, to support policy makers and organizations to make informed decisions. In this article series, we will use data visualization to better understand and derive insights from the Canadian School Expenditures throughout the period of 1973 to 2016.
Our goal is to understand the historical Canadian School Expenditure, provide data visualization insights and help support decision making. Although we appreciate our mobile readers, the visualizations in this article is better to see and easier to navigate on a desktop.
Data visualization simplifies the understanding of data, making it easier to identify patterns, trends, and outliers in a dataset. The human brain is more efficient in identifying data pattern based on visual representation than on numerical data as seen in a spreadsheet format (see example below).
Without Data Viz
With Data Viz
However, to transform a series of lines in to a meaningful report, it is necessary to apply the correct data analytics and visualization principles that augment the potential insights that can be extracted from the data source. For instance, let us briefly discuss “School Board Expenditures” available at by the government of Canada (Government of Canada).
While analysing the “School Board Expenditures” by the government of Canada, our team identified that the following Key Data Identifiers (KDI) were relevant to enhance the analysis of the data source. Therefore, a visualization report was created surrounding the following data points.
Upon further analysis, the following configurations were installed in order to optimize data insight:
It is important to understand that all charts are impacted directly or indirectly by changes to the four slices: 1) Year; 2) Geo; 3) Economic Classification; and 4) Functions.
Once initial analysis is conducted and the proper configurations are inserted, it is possible to quickly reach the following insights:
Further, through the right configurations, it is possible to zoom in (increase the data granularity) and analyse data on a yearly basis. For instance, by changing the “Year Slice” (item 2) to “2016 to 2016”, the visualization is now updated with only data that is associated with the year of 2016. As such, the initial constructed report would look like the following:
As such, the following insights can be reached when each part of the report is analysed independently:
In cases like the one above, pie charts are often easier to visualize (see below). Since our aim is to use a single dashboard to walkthrough Canadian school expenditures, do not expect ‘the best for the occasion’ to always show up. Therefore, some charts might have to be changed when analysed independently to better showcase the data.
As an example of how different types of visualization can improve the overall understanding of the data, below is a pie chart that reflect the above stack chart for easier visualization.
Data can come in different shapes and sizes, which can lead to less efficient, manual work done to understand the meaning of the data. However, through data analytics and visualization, decision makers are better equipped to make decisions that instill progress and efficiency. Gathering data is as equally important as knowing how to read and make sense of it. And with the right tools and skills, data does not have to be scary. In the future, this series will analyze the output from the dashboard to gain insights on provincial and territorial school expenditure.
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