Wherever you need to work with the localization data, maps are a preferable visualization technique. They allow you to easily present the geographical exposition of your data to your recipients. The point map is the basic way to visualize geographical data, where you use points to represent the location (of the client, facilities, store, factory, collection point) \u2013 this can be the exact location (coordinates), or a post-code or administrative unit. The size of the data point represents the selected measure. In the below example you can see the sales for the United States: <\/p>\n\n\n\n <\/p>\n\n\n\n The second basic type of maps is filling. In this case the areas are the essence (usually administrative areas such as counties\/states, but also smaller like provinces\/municipalities), which is filled with color depending on the value of the specific measure: <\/p>\n\n\n\n <\/p>\n\n\n\n Which solution is better? It all depends on your business needs. In the case of filling, the interpretation is slightly more difficult \u2013 it\u2019s harder for human beings to notice a difference in the color density. It is easier to compare the sizes \u2013 data points. But also, here it\u2019s more difficult to compare the sizes of wheels than bars. Moreover, users can miss smaller areas which optically fade when placed near larger areas. For these reasons, maps are the best choice; they can also serve as the additional, illustrative source of information rather than the main source of data. The above examples don\u2019t cover all applications of maps in data visualization, especially in Tableau. You can combine both these approaches and create a map with two layers \u2013 filling and points \u2013 and visualize two measures at the same time: <\/p>\n\n\n\n <\/p>\n\n\n\n In addition to the informative function, maps can also support the optimization of the geographical exposition. For this purpose, you can use such functions as buffer<\/em> (to mark the area around the data point) and makeline<\/em> (to create links between points). The layer function that has been recently added to maps, helps combine several types of maps in order to create practical tools: <\/p>\n\n\n\n <\/p>\n\n\n\n You can insert not only data points to your maps, but also whole charts. Below is an example map with donut charts: <\/p>\n\n\n\n <\/p>\n\n\n\n Tables<\/strong><\/p>\n\n\n\n Tables are certainly associated with Excel and data visualization seems to be the area that avoids using tables, as they seem to be an unattractive medium of communication. However, they can be successfully used to facilitate the transition Excel \uf0e0 BI Tools, but also to provide access to the desired detailed data. In addition, it is pretty easy to animate tables in Tableau and make them more attractive. Below you can see a standard table and a visually enhanced highlight table: <\/p>\n\n\n\n <\/p>\n\n\n\n This simple operation will allow you to focus the user\u2019s attention on the biggest values or deviations. When working with a large amount of data, and when the general position of the desired areas is more important than the exact values, you can remove the values and leave the colors to create a heat-map: <\/p>\n\n\n\n <\/p>\n\n\n\n Tables, similarly to maps, are best suited as the additional function in visualization. They enable us to access detailed information, which might be important for some users. For this reason, tables are one of the tools used in data visualization, however they are not at the core of it.<\/p>\n\n\n\n KPI Card<\/strong><\/p>\n\n\n\n When designing dashboards, we usually use a top-down approach. We start from general, overall KPIs and we gradually go into details that enable us to understand the indicators, their deviations and evolution. In order to quickly find the most essential information in a dashboard we use KPI Cards, which usually contain:<\/p>\n\n\n\n Such a card allows you to quickly evaluate the content of the KPI, whether its performance falls above or below the reference, and understand the trend. From the management point of view, it\u2019s an excellent starting point for further exploration of results, in particular that it doesn\u2019t take up much space on the dashboard: <\/p>\n\n\n\n <\/p>\n\n\n\n Sky is the limit<\/strong><\/p>\n\n\n\n When it comes to other visualization techniques \u2013 there are plenty of them such as Sankey Chart, Likert Scale, Marimekko, bump, flow, stream or rose chart \u2013 the possibilities are unlimited. However, when visualizing data, you need to make sure the communication is simple, intuitive and easy to grasp by users. Therefore, in business applications it is preferable to rely on the basic charts, depending on the type of data you are working on. However, if your objective is to attract the user\u2019s attention and to focus more on the visual aspect than on the functional one \u2013 then not even sky is the limit.<\/em><\/p>\n\n\n\n Mateusz Karmalski Tableau Author <\/p>\n","protected":false},"excerpt":{"rendered":" In the previous posts of the series \u201c60 Charts in Tableau\u201d, I described visualization methods for presenting the category and time data, the elements of the whole and relationships. Let\u2019s recall them \u2013 bar charts are recommended for category data and line charts are suitable for time data. There were more options for the elements […]<\/p>\n","protected":false},"author":1,"featured_media":3074,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"_genesis_hide_title":false,"_genesis_hide_breadcrumbs":false,"_genesis_hide_singular_image":false,"_genesis_hide_footer_widgets":false,"_genesis_custom_body_class":"","_genesis_custom_post_class":"","_genesis_layout":"content-sidebar","footnotes":""},"categories":[9,5],"tags":[],"class_list":{"0":"post-3071","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-blog","8":"category-tableau","9":"entry"},"yoast_head":"\n![\"\"](\"https:\/\/newdatalabs.com\/en\/wp-content\/uploads\/2024\/01\/1-6.png\")
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