Charts Vs Tables: How To Best Represent Your Data
“There is no such thing as information overload. There is only bad design,” statistician and professor Edward Tufte had famously said. To this date, everyone in this field of analytics staunchly agrees with this statement. Choosing the correct visualisation might seem like a very unimportant task, but people from the analytics background know how crucial it is to decide what visualisation would suit which type of data. Visualisations are important to reflect large and complicated datasets information used in businesses or research to be conveyed very easily, at a quick glance. Selecting the most appropriate visualisation tools depends on a number of parameters of the available data, and the decision has to be made such that the maximum useful output is extracted from the available data. Just making the data look appealing and neat is not important, the visualisation that you choose must also be easier to understand and interpret. The Visual War It is often a subject of confusion regarding which one of the two — tables or charts — should be used for a particular problem. Here is a guide to choosing the right data representation for your problem. 1.Exact vs Approximate: Tables are used for situations where you need the precise details, like the exact numerical value. They are ideal for data that cannot be easily presented visually, or when the data requires more specific attention.Chart visualisations are suitable when you are willing to sacrifice a little of exactness in numbers or details and are comfortable with putting up approximate values or information for representations. 2.Data Comparison: Tables are used when you want individual values to be compared to one another. Charts can be used when you do not necessarily have to compare one value to each but to represent relationships out of sets of available values. Also, if you have more than one set of values that have a direct relationship, you may use a table to organise the data. Tables are easy to compare pairs of related values and also when multiple units of measurement are involved. 3.Raw vs Processed: Tables present to you the data in a form as raw as possible. Charts use abstraction to focus on trends and relationships between the data. The data is smoothed to a certain extent an a refined version of it is finally presented. 4.Data Representation: If there is a lot of data containing many different features and parameters, presenting them in a table would give a complex visualisation of the information. Whereas, using forms of charts to represent the same data with many parameters will make the data look easy to look at and understand. This is because charts collect a large number of data in one place, highlighting only the important aspects like numerical patterns, trends, distributions and similar abstract insights. 5.Information: If the data that you have to be represented have numerical values, as well as details in text format, tables will represent it better. Charts do not have a way to represent text information without limitations. It can play with numbers to a large extent but has restrictions when it comes to text. But, when there is more than one kind of data that have a direct relationship, tables come in handy for some cases. For example, to display meeting agendas, time, topics, location, speakers, tables are preferred over charts because it becomes very complicated to use forms of charts in this case. 6.Insights:To look at a summarised version of the data, charts are more useful because they can show the shape of the data and lets us know the trends of the dataset at one glance, as opposed to tables, which as mentioned before present to you a data as raw as possible and hence will not provide trends and shape of the data. You will need to go through each set of information to study them. 7.Types: Finally, the different types of each of these data visualisation tools are: Tables: Simple table, multi-dimensional table, wide and narrow tables: simple table, multi-dimensional table, wide and narrow tables. Charts: histogram, bar chart, line chart, pie chart, timeline chart, organisational chart, tree chart, flow chart, cartogram, pedigree chart, bubble chart, stream graph, Kagi chart, scatterplot. Conclusion Data journalist David McCandless had said, “By visualising information, we turn it into a landscape that you can explore with your eyes. A sort of information map. And when you’re lost in information, an information map is kind of useful.” Both the visualisation tools of tables and graphs offer specific set of advantages over one other depending on the purpose and no one tool is always better than the other. Which one of the two you want to use depends entirely on your purpose and how you want the data to be used. It is important to choose the right visualisation based on your requirements and make the best use of the available data for your businesses. Visualisation indeed gives the answers to questions we didn’t know had.
“There is no such thing as information overload. There is only bad design,” statistician and professor Edward Tufte had famously said. To this date, everyone in this field of analytics staunchly agrees with this statement. Choosing the correct visualisation might seem like a very unimportant task, but people from the analytics background know how crucial […]
Selecting the most appropriate visualisation tools is a very important decision to make when representing a data.
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["Go", "TPU", "programming_languages:R", "AI", "programming_languages:Go", "analytics", "GAN", "R"]
["AI", "analytics", "TPU", "R", "Go", "GAN", "programming_languages:R", "programming_languages:Go"]
Found a way to Data Science and AI though her fascination for Technology. Likes to read, watch football and has an enourmous amount affection for Astrophysics.