Data Visualization with Tableau

Data is the most important resource in the modern organisation. However, it may not be very useful if interpreted incorrectly. An organisation can use data to its advantage by putting it in the right context. This is why Data Visualisation has grown in both prevalence and importance in the modern day. Tableau Data Visualisation – the feature of Data Visualisation offered by the data analysis tool Tableau – is one of the most in-demand skills in the market right now. This blog will help you with all you need to know about Tableau Data Visualisation, including its advantages and disadvantages, its importance and the different visualisations it provides. 

Table of Contents 

1) What is Tableau Data Visualisation? 

2) What are the advantages and disadvantages of Tableau Data Visualisation?  

    a) Advantages 

    b) Disadvantages 

3) Why is Tableau Data Visualisation important? 

4) What are the different types of Data Visualisations? 

5) Conclusion 

What is Tableau Data Visualisation? 

Data Visualisation can be defined as the graphical representation of information and data. Using visual elements such as charts, graphs and maps, Data Visualisation provides an accessible method to see and understand trends, patterns and outliers in data.   

In addition, Data Visualisation provides an excellent way for employees or business owners to present their data to non-technical audiences without any confusion. In the modern world driven by Big Data, Data Visualisation tools and technologies are essential for analysing huge amounts of information and making data-driven decisions.  

Data Visualisation with Tableau can be defined as visually presenting the information. As more and more data can be processed and interpreted to profit organisations, the need to visualise data has grown over the years. This is why Data Visualisation with Tableau is now welcomed by most decision-makers across organisations. It helps decision-makers determine the relevance between millions of variables and communicate concepts, which helps in better-informed decision-making.   

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What are the advantages and disadvantages of Tableau Data Visualisation? 

A feature as convenient as Data Visualisation may not seem to have any downsides. However, sometimes data can be misrepresented or misinterpreted when contextualised incorrectly. This is why it is important to keep the advantages and disadvantages of Data Visualisation in mind when creating a visual report of data such as charts, graphs and maps. 

Advantages 

The visual element that Data Visualisation provides makes it an integral part of the working processes of modern organisations. Our eyes are drawn to colours and patterns much more than text and numbers. We also tend to believe things we can see, including everything from art and advertisements to movies.   

Similarly, visualised data is another form of visual art that piques our interest in a particular topic and keeps our eyes on the message. We can spot trends and outliers quickly whenever we see an informative graphical representation such as a chart or a graph. Some of the many advantages that Data Visualisation in Tableau provides are: 

1) It is intuitive: Many people find visuals much easier to comprehend than numbers or words. This means that people find Data Visualisations much more intuitive than any other method of representing data.   

2) It facilitates simple data sharing: As Data Visualisation is easier to understand, it naturally leads to better analysis. This is because people can readily comprehend and draw conclusions from visualisations. Visualisation is easy to use if you want to identify patterns, trends and outliers, which can help you analyse the data to make meaningful conclusions.   

3) It facilitates better analysis: As Data Visualisation is easier to understand, it naturally leads to better analysis. This is because people are readily able to comprehend and draw conclusions from visualisations. It is easy to use a visualisation if you want to identify patterns, trends and outliers, which can help when you are analysing the data to make meaningful conclusions.  

4) It helps make quicker decisions: The easier the data is to understand and analyse, the quicker people can reach conclusions and make decisions. This saves time and enables more people to see and comprehend data to make the best decisions possible. 

Data Visualisation serves visual storytelling's purpose, making it easier for organisations to make better-informed decisions. Some other advantages that data visualisation provides are that it easily shares information, interactively explores opportunities and visualises patterns and relationships. All these advantages help an organisation gain better insight into their data resources, which in turn helps them operate more efficiently and generate better revenue.   

Disadvantages 

While there are many advantages that Data Visualisation brings to an organisation, there are a few disadvantages which should be considered as well. The disadvantages that Data Visualisation in Tableau has are: 

1) Improper visualisation: Many of the issues and disadvantages of Data Visualisation with Tableau arise because one may have improper visualisations that do not properly convey the data. This can lead to confusion and issues down the line. Sometimes the visualisation is inappropriately designed, which makes it seem biased, confusing or uninterpretable. 

2) Incorrect conclusions: A risk of using Data Visualisation is that the viewer might draw an incorrect conclusion. For example, it is often easy to make an incorrect assumption or derive an inaccurate interpretation when viewing a visualisation with many different data points. 

3) The information may be inexact: The risk of creating an inexact perception of the data is inherent with a visual representation of numerical data. The possibility of biased or inaccurate information is the biggest downside of Data Visualisation, while other disadvantages exist. While interpreting visualised data, one must remember that correlation does not always mean causation. Another disadvantage of Data Visualisation is that core messages may get lost in translation.



 

Why is Tableau Data Visualisation important? 

Technological developments in the modern world have enhanced the importance of Data Visualisation. Data Visualisation helps people analyse, interact with and understand data better. Whether simple or complex, the right Data Visualisation can help streamline working processes by bringing everyone on the same page – regardless of how experienced they are.  

There are a lot of reasons why businesses adopt Data Visualisation, a few of which are listed as follows: 

1) Visualisation helps individuals and organisations gain a clearer understanding and better insights into a topic. 

2) Visualisation helps individuals, and organisations predict the future quickly and make better-informed decisions.   

3) Visualisation of data facilitates the convenient and efficient interpretation of data in large volumes. 

4) Data Visualisation is known to convey information universally. 

5) Data Visualisation makes it simpler to share one’s ideas with other individuals during the working process in an organisation. 

6) Data Visualisation lets the concerned individuals know where they can adjust or make changes to their business working process for better results. 

7) Data Visualisation provides scalability or the ability of a computing process to be used or produced in a range of capacities. Scalability refers to a computer application's ability to continue functioning at an optimum level even when the contextual data is altered in size or volume. 

8) Data Visualisation makes interpretation easy for individuals in an organisation to track and identify trends and patterns and work on them accordingly.   

It is difficult to think of any professional industry that is not positively impacted by data being made more understandable with the help of Data Visualisation. Almost every field benefits from a proper understanding of its data resources, including – government, finance, marketing and the history of consumer goods, service industries, education and sports. The practical, real-life application of Data Visualisation is undeniable, and visualising data in the proper context is one of the most useful professional skills one could develop.   

The better one can convey their points visually – whether on a dashboard or a slide deck – the better their career in Data Analysis. As people rapidly update their skill sets to excel in an evolving data-driven world, it is increasingly valuable for professionals to know how to use data to make well-informed decisions. They can use visuals to explain the who, what, when, where, and how's. Data visualisation bridges the gap between traditional and visual learning.   

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What are the different types of Data Visualisations?

Whenever one thinks of Data Visualisation, their first thought is likely to be a simple bar graph or a pie chart. While these may be an important part of Data Visualisation and a common ground for many data graphics, the right visualisation should always be paired with the appropriate information set. A whole set of visualisation methods can be used to present data effectively and interestingly. You can use the following types of infographics to present your data: 

1) Chart: A chart refers to information presented in a tabular and graphical form with data displayed along two axes. A chart can be in the form of a graph, diagram or map.   

2) Table: A table can be defined as a set of figures that are displayed in rows and columns. 

3) Graph: A graph is a diagram of points, segments, lines, curves or areas that are used to represent certain variables in comparison to each other. The information is visually represented – usually along two axes at a right angle.  

4) Dashboards: A dashboard can be defined as a collection of visualisations and data displayed in one place, which usually helps with analysing and presenting data. 

5) Geospatial: Geospatial is a visualisation that shows data in a mapped form using various shapes and colours to show the relationship or correlation between different pieces of data and specific locations. 

6) Area Map: An area map is a form of geospatial visualisation. Area maps usually represent specific values set over a map of a country, state or other geographic location. Two of the most common types of area maps are choropleths and isopleths. 

7) Bar Chart: A bar chart represents numerical values compared to each other. The length of the bar in the bar chart represents the value of each variable.   

8) Box-and-whisker Plots: A box-and-whisker plot represents a selection of ranges (referred to as the box) across a set measure (referred to as the bar). 

9) Bullet Graph: A bullet graph can be defined as a graph that features a bar marked against a background. The bullet graph represents progress or performance when compared against objectives, usually denoted by a line on the graph. 

10) Gantt Chart: A Gantt chart is typically used in Project Management. Gantt charts can be defined as bar chart depictions of timelines and tasks.   

11) Heat Map: A heat map can be defined as a type of geospatial visualisation mapped in a form that displays specific data values in different colours. 

12) Highlight Table: A highlight table is a form of table that uses colours to categorise similar data. This allows the viewer to read it with better ease and intuition. 

13) Histogram: A histogram is a type of bar chart that splits a continuous measure into different bins. Splitting the continuous measure helps analyse the distribution. 

14) Pie Chart: A pie chart is a circular chart with triangular segments representing data as a percentage of a whole.   

15) Tree Map: A tree map represents different and related values in rectangles nested together.    

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Conclusion 

As the age of Big Data progresses, visualisation is an increasingly used key tool to interpret trillions of data produced each day correctly. Tableau Data Visualisation helps tell stories by curating the data into a simpler form, making it easier to understand and identify trends and outliers. A well-made visualisation helps tell a story, as it removes the additional, unnecessary noise from raw data and highlights the information that can be used for profit.   

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