The Guide to Preparing Data for Tableau
Published on 26 May 2022
If you're like the majority of data analysts today, developing rich data visualizations is a crucial step in the analytical process. Visualizations allow you to discover the hidden potential in your data so that you may get the answers you need and make the best data-driven business choices.
However, developing excellent data visualizations involves extensive effort behind the scenes to prepare the data, which may be more time-consuming than creating the visualizations themselves.
Before you can see your data, you must:
- Combining and cleansing information from an increasing number of diverse sources
- Complement your own data with other data sources, and then execute advanced spatial or predictive analytics.
- Transform data such that it is readily digestible for Tableau visualization.
Combine and organize your data
Alteryx makes it easy to swiftly combine your data in a repeatable drag-and-drop process without the need for specialized tools or programming knowledge. Merge data from many, divergent data sources, including Excel, data warehouses, social networking and cloud applications, and Big Data. Then, inside the same process, clean your data using programs that automatically deduplicate, parse, and remove unnecessary data.
Improve your data
Frequently, internal data needs additional context. Alteryx's third-party data makes it possible to get the broad picture in minutes.
Need to identify how your clients differ from the general population? To segment your consumers, use Experian Mosaic categories with Alteryx. Then, compare your consumers to these groups so that you can target leads with comparable characteristics.
Which possible retail sites will provide the most profit? You may answer this question using the Experian data in Alteryx by determining how many consumers reside within a reasonable driving distance of the shop.
Perform accurate predictive analytics
According to conventional opinion, only data scientists and programmers with extensive training can do predictive analytics; nevertheless, Alteryx equips the data analyst with potent predictive analytic tools. Self-service analytics are enabled by a variety of technologies, including market basket analysis, linear regression, A/B testing, decision trees, and forest models.
In addition, any predictive analytic model may be expressed and shown with ease in Tableau. In A/B testing, you may depict treatment vs non-treated items using bar charts. Or, use bubble charts to demonstrate market basket analysis and illustrate how the purchase of one item by a client might impact the purchase of other items.
Leverage Spatial Analytics
Alteryx provides the data analyst with drag-and-drop capabilities for spatial analyses. You can monitor a buyer's physical location when she makes a mobile transaction, analyze store locations, and more using spatial analytics.
If you want to construct any form of a map in Tableau, Alteryx spatial analytics may assist you in generating the appropriate data set for your business decision. Using Alteryx, you can generate polygons that collect data inside a certain region, and then send that data straight to Tableau for visual representation in a drive-time map, cell tower coverage area, disease outbreak area, and more.
Spend less time preparing data and more time displaying data
Creating visuals is an integral element of data analysis, and their significance is growing. But if you spend fifty to seventy-five per cent of your effort preparing your data for visualization rather than developing the visualization itself, you are not providing consumers with the information they need to see patterns.
When necessary, they may identify patterns, identify hazards, and take advantage of time-sensitive possibilities. Developing the Alteryx and Tableau are required for rich visuals that lead to rapid responses.
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