In the world of data analysis, accuracy is paramount. As we dive deeper into our analysis, it becomes crucial to verify the numbers and ensure their integrity. Today, we will explore a data check technique using Tableau, a powerful visualization tool, to validate the accuracy of our data.
Contents
Checking Sales Transactions
Let’s start by checking the number of sales transactions. We can easily do this by dragging the “measure values” field into the workspace area. Upon doing so, we are greeted with the expected number: 110,570. This confirms that our sales transactions align with our expectations.
Verifying Audiobook Reviews
Next, let’s turn our attention to the “Audiobook reviews” data and verify the number of ratings we have. Upon attempting to drag and drop the “Rating” field, Tableau displays an error. This error indicates that we need to establish a relationship between our primary data source and the “Audiobook reviews” data source.
To resolve this, we simply click on the link icon next to “Transaction ID” and select it as the linking field. By establishing this linking field, we ensure the proper blending of our data sources.
Accurate Ratings Count
With the linking field in place, we can now accurately count the number of ratings left by students. This count should not be summed; instead, we are interested in the precise number of ratings. Upon performing the count, we find that there are 10,798 ratings, exactly as expected.
Time for a Test
Now, let’s embark on a true test of our data accuracy. We will plot the number of reviews against the “Date of purchase” field from the “Audiobook” sales file. By adding “Date of purchase” in columns and selecting a monthly breakdown, we can examine the data closely.
A Potential Setback
Uh-oh! It seems that once again, December 2017 is missing from our data. But fear not, we have the tools to solve this issue.
Solving the Problem
To address the missing data, we head to the “Data” menu and select “Edit Relationships.” Here, we choose the primary data source as “Audiobook sales” and establish the necessary linking fields.
By linking the year of the review date with the year of the purchase date, and the month of the review date with the month of the purchase date, we create a new relationship between the data sources.
Success!
With the relationships adjusted, we eagerly check if our efforts have paid off. Our number of reviews per month now reveals 653 reviews for December, instead of zero. This is excellent news and reinforces the accuracy of our visualizations.
Conclusion
Accuracy is the cornerstone of effective data analysis. By employing Tableau’s data check techniques, we can ensure that our numbers are correct and our insights are reliable. We learned the importance of establishing linking fields, customizing relationships, and validating our data visually.
Stay tuned for our next lesson, where we’ll delve into creating captivating charts for our dashboard. At Techal, we are passionate about empowering you with the knowledge and skills to leverage technology for maximum impact.
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FAQs
Q: Why is it important to verify data accuracy in data analysis?
A: Data accuracy is crucial because it forms the foundation for reliable insights and informed decision-making processes. Without accurate data, visualizations and analysis can lead to incorrect conclusions.
Q: How can Tableau help in verifying data accuracy?
A: Tableau provides various tools and techniques to validate data accuracy. It allows users to establish relationships between data sources, perform data blends, and visually inspect the data through interactive visualizations.
Q: What should I do if I encounter missing or incorrect data in Tableau?
A: If you come across missing or incorrect data, you can explore options such as adjusting relationships, creating custom blends, or employing data cleaning techniques to rectify the issue. Tableau’s flexibility allows you to troubleshoot and enhance data accuracy.
Q: Can I trust Tableau’s visualizations for accurate data representation?
A: Yes, Tableau’s visualizations are a reliable means to represent data accurately. By ensuring the accuracy of the underlying data and employing best practices in data visualization, Tableau enables users to create impactful and trustworthy visual representations of their data.
Q: Is data accuracy the sole responsibility of Tableau?
A: Data accuracy is a joint effort between Tableau as a tool and the data analysts or engineers working with it. Tableau provides the tools and functionalities, while users must validate and verify the accuracy of their data to ensure reliable analysis and insights.
Q: How can I further enhance my data analysis skills?
A: To enhance your data analysis skills, consider exploring additional resources, such as online courses, books, and practice exercises. Engaging in data analysis communities and staying up-to-date with the latest trends and techniques will also help you refine your skills over time.
Conclusion
In the realm of data analysis, accuracy reigns supreme. Through Tableau’s data check techniques, we can confidently verify the integrity of our numbers and produce reliable insights. By establishing relationships, validating counts, and resolving discrepancies, we ensure accurate visualizations and empower ourselves with invaluable knowledge. Stay tuned for more engaging content and insightful analysis from Techal as we continue to explore the ever-evolving world of technology.
