The success of any data analysis or visualization project depends on how accurate and clean the data is or how efficient the data source is. In the case of Tableau software, the accuracy of data visualization and tableau dashboard also depends largely on the data quality. Any inconsistency or inaccuracy in data will obviously reflect in the visualization. In this article, we will talk about how to mitigate data issues in Tableau. Let’s jump right in.
Data Quality Issues
Let’s start with data quality. This is not an ideal world. You will often receive datasets with inconsistent values, missing information, null values and outright incorrect data. Can you solve data quality issues with Tableau? By all means!
Tableau comes with a data-cleaning tool called Tableau Prep. You can clean and correct your data using this tool. Let’s give you two examples-
- Dealing With Null Values
Null values are those missing data in a table. It can be so frustrating to see data missing from the cells in the table. But that’s life.
With Tableau Prep, you can automate the way you want to handle null values.
Look at this movie data set –
There are null values, both in the ‘movies’ column as well as in the ‘rating’ column. A standard way to deal with these null values is to either delete them or adopt a middle ground.
In the case of the ‘Movies’ column, it is better to delete the null value, while in the ‘Ratings’ column, let’s replace null values with 5 (the rating is on a scale of 1-10. So the middle ground is 5).
We just have to right-click on the null value to delete or modify it all at once.
- Removing Unnecessary Punctuation
If your data in any field contains too much punctuation, which makes the data messy and hard to work with, you can easily clean the data using Tableau prep.
Just select the column where the offending data resides. Click on the three dots and select clean. Lastly, select “Remove Punctuation.”
These are just two examples. With Tableau software, it’s possible to automate various kinds of data cleansing. From resolving punctuation issues to using custom fiscal years, from splitting values to joining tables based on common data points- the possibilities are endless.
Solving- Connection to Tableau Server Data Source Denied
If you need to connect to a data source in Tableau, you must have the connect capability to do that. But that’s not all. As you know, a Tableau workbook remains connected with a data source. There can be two ways to set Connect access for a viewer or explorer of that workbook. Either you can let Tableau use the Connect access of the publisher of that workbook, or you can set Tableau to ask for the credential of the viewer of that workbook. In the second case, anyone who does not have access to the specific published data source will see ‘connection denied’ errors.
If You Are Joining Two Tables, Male Sure That the Common Column in Two Tables Has the Same Data Type
Suppose you are joining two tables. One table contains – book titles and the ISBNs pertaining to those titles. The second table contains the ISBNs and the sales data against the ISBNs. Now since the ISBN column is common in both tables, you can use it to join them. However, the join will fail if, in one table, the data type for the ISBN column is marked as a string while in the other table, the data type for the same column is marked as a number. You have to modify the data type for one column in one table so that it matches that for the same column in the other table.
Tableau dashboard is a powerful tool that does not just visualize data. You can use Tableau to iron out the inconsistencies in the dataset. If your organization is still relying on traditional business software, it’s time to contact a Tableau partner in India and introduce true digital transformation in terms of data analytics.