You will notice that the first 39 date rows in the Excel file do not contain any data. Look at the screenshot below showing the Excel file. Using Data Interpreter to Remove Extra Rows in Source Data As I mentioned, this is an Excel file that contains foreign exchange data. But before we get into the details of Tableau Prep, first let’s have a quick look at the sample data that we are going to use. In this section, we will use the cleaning step available as part of the Tableau Prep data flow. Additionally, I will also use a data file that was downloaded from the Statistics Canada Website. this file was downloaded from the Bank of Canada website. As a case study, I will use an Excel file that contains foreign exchange rates against the Canadian dollar. Having read the previous articles is recommended but not a pre-requisite.
It assumes that the reader has fundamental knowledge about data visualization and has some basic level of exposure two one or more tools for data visualization and analytics.
Tableau prep joins series#
This article is part of a series of 6 articles on Tableau Prep and focused on Cleaning, Grouping and Replace. To ensure that this data can be aggregated in a meaningful and consistent manner for visualization and analytical purposes, data must be cleaned as part of the data preparation step. Because data is coming from different sources it could be in different formats, may have different meanings for the same data values, may have different purpose for the same data field, and may have different levels of data quality. In real life, data that is used for visualization is typically sourced from a variety of different sources. Data is like blood flowing through an organization and, just like blood quality is directly linked with the health of the body, organizations only function effectively when the quality of their data meets the minimum quality standards.
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