Datapony is a versatile, yet very simple tool that makes using Tableau much easier. Small businesses can achieve very good individual analytics results with the help of the Datapony developers. Datapony dashboards are adapted directly to their own needs, which can lead to very good results in evaluating the displayed data. But for many small companies, customized dashboards are often unnecessary. This article is therefore intended to shed light on automatic dashboard creation in Datapony.
The basis of the data analysis are datasets that should be well organized at best. If you want to use the automatic dashboard creation feature in Datapony, your data must be formatted correctly and stored in .CSV or .xlsx files. If you have unprepared data, you can access an additional Datapony service to format the data. However, data preparation is not included in the basic version and must be purchased separately. If the dataset is ready, the visualization can begin. For this purpose, the main interface of the Datapony app is available, in which the data must be placed using drag and drop.
If there are multiple spreadsheets in a file, Datapony asks which table you want to visualize. If the files are small and contain only one table, this step will be skipped by the program.
You will then be taken to a window asking you to arrange your data. Since Datapony was intelligently designed, you only need to check that the individual rows of the table have been interpreted correctly. However, in most cases the interpretation works correctly, so you can skip this step on known data sources. Do not forget to give your dashboard a name. When you think everything is fine, you can continue with the button "Create new datasource".
After the data has been successfully passed to Tableau, you can choose the dashboard of your choice from the available choices. Here we choose the "General Analysis" because our data source is quite versatile and large. Other dashboards tend to better show smaller details, which is not important to us in this case.
Now you will be taken to a final edit page where changes to the information displayed in the dashboard can be made. In this case, you can accept the uploaded data and stop making changes or exclude some dimensions or data aggregation. We decide not to exclude anything in the uploaded dataset as we want to display a lot of details in our dashboard.
However, we would like to use the filter function to integrate some filters into our dashboard. These can later be used to more accurately filter the displayed results for more valuable results. Here we choose a filter based on the shipping date and a state-based filter.
Now we click on "Generate" to get to our finished dashboard. Datapony does the technical stuff in the background and creates your chosen Tableau Dashboard in seconds. You can then analyze your data against the selected filters and compare different time periods. For example, we notice on the dashboard that our fictitious business in Newark loses money. In addition, New York City can be seen as the most profitable city in our dataset.
If you want to share the dashboard with your colleagues now, this is not a problem either. On the lower left side of the dashboard is a small button available that provides various export options. We click on it and select PDF as the format, because we want to send the dashboard in an e-mail later on.
After that a window opens, asking if we want to create a PDF in landscape format. Here we simply select our preferences and confirm.
Automatic dashboard creation in Datapony is of interest to all companies that want to visualize data in the shortest possible time without sacrificing analytic depth. The feature is very popular with both beginners and tableau experts, so we believe a good middle way is best here. For companies that use custom dashboards, the automatically generated dashboards can also be of interest in getting information quickly. Especially the possibility to download the dashboard directly as a PDF can be useful for sharing analyzes on, for example, customer segments with colleagues.
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