Data visualisation is the graphical representation of information and data. It helps us deal with more complex information and enhance memory (we earlier covered a post here on data visualisation).
"Human beings understand things that are visually well descriptive and interesting."
According to the Global Data Visualisation Market (2019) Report, the data visualisation industry was valued at USD 4.51 billion in 2017 and is expected to reach USD 7.76 billion by 2023 (CAGR of 9.5% over forecast from 2018 to 2023).
Tableau is one of the fastest growing data visualisation tools. Tableau can connect to files, relational data sources and big data sources to get and process data. It can help anyone see and understand their data through a drag and drop approach.
Using live connection, Tableau sends queries to your database, and retrieves data in real-time or in near real-time. Tableau helps companies analyse future data without any future goals in mind. You will be able to explore visualisations and observe data from different approaches.
“Visualisation gives you answers to questions you didn’t know you had.” – Ben Shneiderman
There are many alternatives to Tableau such as Sisense, Qlikview, SAS Business Intelligence, Qualtrics Research Core and others. But Tableau still leads the business intelligence software pack with its well-designed interface and fast reports. In this post, we will not cover the differences between the business intelligence softwares, but focus on Tableau.
1) Tableau is able to consolidate and handle large amounts of data
Technically, there is no limit to the amount of data Tableau can handle for visualisation. But it is important for the datasource to be fast to get Tableau to display the data in reasonable speed.
For example, Tableau can be used to access data stored in Hadoop, where processing speed is faster. Hadoop is an open-source distributed processing framework that manages data processing and storage for big data applications in scalable clusters of computer servers. It can handle various forms of structured and unstructured data. It is more flexible than relational databases and data warehouses.
You will only need to have Hive installed on your Hadoop cluster, which is a common component that provides a SQL interface to Hadoop. You can then connect with no programming and use Tableau's drag-and-drop interface to visualise your data.
It also supports other platforms such as Spark and NoSQL. This removes the need of having advanced knowledge of query languages. This makes Tableau flexible.
Tableau can also consolidate data and removes the need to pull individual reports from multiple software or databases. In other words, it allow data blending. Data blending brings in additional information from a secondary data source and displays it with data from the primary data source directly in the view.
2) It is user-friendly and provides quick data visualisation
Tableau makes interacting with data easy. It is intuitive. Anyone can use Tableau for visualisation. No coding knowledge is required. It provides a similar feel as a pivot table in Excel and supports quick calculation. It answers questions to data in real-time, big or small.
Tableau dashboards are also mobile friendly. With touch-optimized controls, visualisations are automatically streamlined for mobile devices such as tablets and phones. The optimisation of dashboards for mobile devices has a huge impact on readability and clarity for users who are working on a small screen.
Users will need to download the Tableau mobile app to access the dashboards on their mobile.
3) Tableau and Python integration enhances its analytics application
Tableau developed the Tableau and Python integration called TabPy. TabPy is a new API that enables evaluation of Python code from within a Tableau workbook. The split approach allows you to execute Python code on the fly and display results in Tableau visualizations, so you can quickly deploy advanced analytics applications.
In other words, it allows users to run Python scripts within calculated columns in Tableau. It leverages the power of a large number of machine-learning libraries in Python right from the visualisations.
Users can tune parameters and evaluate their impact on the analysis in real time as the dashboard updates. This provides some advanced analytics capabilities and enables powerful scenarios.
Complex functions are easier to maintain, share, and reuse as deployed methods in the predictive-service environment. Tabpy allows data scientists to work on the backend.
Tableau simplifies the way data can be visualised and communicated. Hence, it benefits big data analysis. There are however downsides to Tableau. It is very expensive to scale across a large organisation and it is also performs less well in customisation.
Tableau is becoming more powerful especially with the acquisition of Salesforce. Salesforce is a business intelligence company which will provide more analytics capability.
Do you use Tableau? If so, what do you find most useful about the program in your business? Share by leaving us a comment. If you require more information or assistance on digitalisation activities, contact us. We want to be an extension of our clients. Subscribe to our newsletter for regular feeds.
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References
Intellipaat, What is Tableau, https://intellipaat.com/blog/what-is-tableau/, published 7 January 2020
Tableau, Blend Your Data, https://help.tableau.com/current/pro/desktop/en-us/multiple_connections.htm, Version April 2019
Tableau, Leverage the Power of Python in Tableau with TabPy, https://www.tableau.com/about/blog/2016/11/leverage-power-python-tableau-tabpy-62077, published 4 November 2016