Tableau vs PowerBI: 10 Big Differences

The concept of using pictures to understand patterns in data has been around for centuries. From existing in the form of graphs and maps in the 17th century to the invention of the pie chart in the mid-1800s, the idea has been exquisite. The 19th century witnessed one of the most cited examples of data visualization when Charles Minard mapped Napoleon’s invasion of Russia. The map depicted the size of Napoleon’s army along with the path of Napoleon’s retreat from the city of Moscow – and tied that information to temperature and time scales for a more in-depth understanding of the event.

Read more about data Visualisation in our previous blog – Practices on Data Visualisation.

In the modern world, when it comes to the search for a Business Intelligence (BI) or Data Visualisation tool, we come across two front runners. They are PowerBI and Tableau. These are the top data visualization tools. Both of these products are equipped with a set of handy features like drag-and-drop, data preparation amongst many others. Although similar, each comes with its particular set of strengths and weaknesses, and hence very often articles titled Tableau vs PowerBI are encountered. The following comparisons provide insights into which data visualization tool is best for different purposes.

The tools will be compared on the following grounds:

  • Cost
  • Licensing
  • Visualization
  • Integrations
  • Implementation
  • Data Analysis
  • Functionality

Cost remains a significant parameter when these products are compared. This is because at one end PowerBI is priced around 100$ a year while Tableau can be rather expensive up to 1000$ a year. PowerBI is more affordable and economical than Tableau and is suitable for small businesses. Tableau, on the other hand, is built for data analysts and offers in-depth insight features. So, when it comes to Tableau vs PowerBI cost comparison, Tableau is a better alternative to PowerBI.

Tableau should be the first choice in this case. To explain why Tableau over PowerBI, the final choice is considered that is, whether one wants to pay upfront cost for the software or not. If yes, then Tableau should be chosen else one should opt for PowerBI.

When it comes to visualization features, both the products have their strengths. PowerBI can prove to be better if the desired outcome is data with better visuals. PowerBI lets you easily upload datasets. It gives a clear and elegant visualization. However, if the prime focus is visualization, Tableau leads by a fair margin. Tableau performs better with more massive datasets and gives users efficient drill-down features.

PowerBI has API access and pre-built dashboards for speedy insights for some of the most widely used technologies and tools like Salesforce, Google Analytics, and Microsoft Products. On the contrary, Tableau has invested heavily in integrations and widely-used connections. A user can view all of the connections included right when he/she logs into the tool.

This parameter along with maintenance is primarily dependent on factors like the size of the company, the number of users, and others. Power BI comes out to be fairly more straightforward on the grounds of implementation and requires a low level of expertise. However, Tableau, although is a little more complex, offers more variety. Tableau incorporates the use of quick-start applications for deploying small scale applications.

Data Analysis
Power BI with Excel offers speed and efficiency and establishes relationships between data sources. On the other hand, Tableau provides more extensive features and helps the user in hypothesizing data better.

For the foreseeable future, any organization which has users spending more than an hour or two per day using their Business Intelligence tool might want to go with Tableau. Tableau offers a lot of features and minor details that are unmatched.

Feature Power BI Tableau
Date Established 2013 2003
Best Use Case Dashboards & Ad-hoc Analysis Dashboards & Ad-hoc Analysis
Best Users Average Joe/Jane Analysts
Licensing Subscription Subscription
Desktop Version Free $70/user/month
Investment Required Low High
Overall Functionality Very Good Very Good
Visualisations Good Very Good
Performance With Large Datasets Good Very Good
Support Level Low (Or through partner) High

It all depends upon who will be using these tools. Microsoft powered Power BI is built for the joint stakeholder, not necessarily for data analyticsThe interface relies on drag and drop and intuitive features to help teams develop their visualizations. It’s a great addition to any organization that needs data analysis without getting a degree in data analysis or any organization having smaller funds.

Tableau is more powerful, but the interface isn’t quite as intuitive, which makes it more challenging to use and learn. It requires some experience and practice to have control over the product. Once this is achieved, Tableau is better than PowerBI and can prove to be much more powerful for data analytics in the long run.

Data Visualization: 6 Best Practices

Our world is progressively filling up with data, all companies – significant multinationals to the minor young startups are stockpiling massive amounts of data and are looking for ways to analyse this data in the raw form and obtain processed information, that can make complete sense. Data Visualisations represent data in pictorial form for the marketing managers to understand complex data diggings.

According to a fact, 3.5 trillions of e-mails are sent every day for the promotion of many companies; companies prepare ads, stockpile enough resources to deliver them to as many users as they can. With a slight observation, a considerable portion of receivers can be cut-off, who have a meagre – conversion rate. Doing so will not only lower the wastage of their resources but will also help them concentrate more on the people belonging to a higher rate of conversion, thus increasing the chances of the product being sold. For doing this, the implementation of supreme data visualisation is necessary.

Data Visualisation can take everyone by surprise. It is here that a meaningless looking pile of data starts making sense and delivers a specific result as per the likes of the end user or developer. It takes shape with the combined effort of ones creativity, attention, knowledge, and thinking. Data Visualisation can be useful, as well as harmful. (Read: 5 common mistakes that lead to Bad Data Visualization)To help your cause by not misleading your visualisation, here are some of the best practices for making your visualisation clear, useful and productive.

A. Plan your Resources
Create a sequence of steps by obtaining your requirements, your raw data, and other factors that might affect the result. This requires knowledge and experience for a data scientist to choose which method to use to for visualising your data. Planning the resources can be very helpful, as it will lead to greater efficiency with the efficient workload.

B. Know your Audience
The most essential and unavoidable step in creating great visualisations is knowing what to deliver. Focus on the likes of the audience, their mindsets, their queries, their perceptions and beliefs; and then plan effectively. It is not necessary that all the viewers will receive the information in the same way. For example, a probability density graph has a different meaning for an HR manager and a chief sales executive. So, it’s very vital that you know your target audience and prepare visualisations according to their perspective.

C: Predict after-effects
Predicting what might be the effect on the end users can add up to your cause. There can be a no-action session where everything is going positive in your way, while a downfall in a particular field may require some immediate action.

D: Classify your Dashboard
There are three main types of dashboards – strategic, analytical and operational. Following the below steps would let you know which dashboard suits best.

  • Strategic Dashboard: It represents a top notch level view of the inquiry line answered in a daily specific routine and presents KPIs in a minimally interactive way.
  • Analytical Dashboard: It provides a range of investigative approaches to a central specific topic.
  • Operational dashboard: It provides a regularly updated answer to a line of enquiry based on response to events.

E: Identify the Type of data

  • Data is of three types: categorical, ordinal and quantitative. Different types of visualisation work better with different kinds of data. A single relation of a data works best with line plot, two pieces of data work better with a scatter plot. A brief description of the type of data is given below:
    • Quantitative: Defines the number of data
    • Ordinal: Data that belongs to the same sequence. Ex: Medals – Gold, Silver and Bronze.
    • Categorical: Data that is of one type. Ex: Gender – Male, female and Other.

F: Use of Visual Features

  • Having done the above, a perfect choice of colour, hue, saturation can glorify your visualisation. It is just a matter of the presence of mind that draws attention.
  • Using the wrong hue and saturation configurations can bring ruin to all your efforts. A good set of visual features gives a final touch up to your data visualisation.

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Concluding, modern technologies like machine learning and AI by itself will find no use for business corporates, if not for data visualisation. Data Visualisation has itself found its field of study and interests and finds its importance in every walk of analysing data.

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