Significance of BI tools in the Era of Big Data

By July 30, 2020 Uncategorized

Written by Anjali Sharma, Software Engineer at Powerupcloud Technologies

The Demand of business intelligence tool in the Big data world has become the BOOM these days. Today, after Big Data, one of the most used buzzword in the business world is nothing but Business Intelligence. Then how do they both relate to each other? The ascendance of Business Intelligence to the highest priority of most companies has meant that BI Analysts are highly sought after. Business Intelligence (BI) tools have enabled organizations to get revealing insights into their operations and processes and use them to improve productivity, boost revenue, cut costs, etc.

BI refers to the business strategy and technological tools used for analysing business information, including analysis of historical data, analysis of current data as well as future predictions. Hence, BI is a business discipline, much as it is also a technological discipline. As the technological part of BI, companies use various databases and data analytics tool, which comprise their enterprise BI infrastructure. BI tools have been around for decades. However, in recent years, the advent of Big data and artificial intelligence technologies have increased the number and broadened the functionalities of BI technologies.

Gone are the days when businesses were assumed to be like gambling. In those days, there were no other options than making ‘the perfect guess.’ But now, as you know, when it comes to a company’s future, this is no longer an appropriate method to arrive at a strategy. With the help of Business Intelligence software, one can have accurate data, real time updates, and means for forecasting and even to predict conditions.

Assortments- BI tool can have several visages according to the business demand or technical requirements:

  • Data Visual representation tool
  • Data Mining tool
  • Reporting tool
  • Querying tool
  • Analysis tool
  • Geolocation analysis tool etc.

How Tableau becomes the most Powerful BI tool

Now let’s understand among all BI tools how Tableau becomes the most powerful & user friendly-

Tableau offers powerful and sophisticated data collection, analysis and visualizations. One of the claims on Tableau’s website is “Tableau helps people see and understand their data” Tableau allows users to drill deep into data, create powerful visualizations to analyse the information, and automatically produce valuable business insights.

Several Data Source Connections

One of the main strengths of Tableau is that it can automatically connect with hundreds of data sources without any programming needed, including big data providers.

Tableau is one of the leading BI tools for Big Data Hadoop which you can use. It provides the connectivity to various Hadoop tools for the data source like Hive, Cloudera, HortonWorks, etc. Also, not only with Hadoop, Tableau provides the option to connect the data source from over 50 different sources including AWS and SAP.

Drag & Drop facility

Tableau’s drag & drop facility makes it really easy and user friendly. Tableau is designed with most integration taking place through drag-and-drop icons. You can quickly create visuals from data by dragging the icon for the relevant data set into the visualisation area. In other words, you can access visualisations that reveal important insights within a few clicks.

Live and Extracted Data Connection

Tableau allows users to connect live data and extracted data both. User can instantly switch between live data connections and pre-extracted data. You can also schedule extract refreshes and get notifications when live data connections fail.

Security

Users can collaborate securely across networks or the cloud, using Tableau Server and Tableau Online. This allows rapid sharing of insights, meaning that people can take action more quickly to save costs or make more money for the business.

Above mentioned features of tableau make it different from other BI tools. Data is growing faster than ever. With the proliferation of the internet, we now generate even more information. According to IBM, 2.5 quintillion bytes of data are created every day! However, less than 0.5% of it is ever analysed and used. Therefore, the importance of data analysis tools has increased these days. From past 6 years Tableau has been the leader among all data analysis and visualisation tool. Specializing in beautiful visualizations, Tableau lets you perform complex tasks with simple drag-and-drop functionalities and numerous type of charts.

If you are beginner, for better understanding let’s do a hands-on on Tableau with some sample data. Here I am using skill registry dataset where we have created a Google form for the employees of our organisation, we have shared it among them where they can fill their name, email address skills, Total experience etc. After collecting the data, we have created a CEO dashboard.

Download & Install Tableau desktop 14 days’ trial version-  

https://www.tableau.com/en-gb/products/trial

Also you can try free Tableau Public version 2020.2.

Open tableau and connect the data source wherever you have your data as Tableau provides more than 100 data sources we can connect.

After connecting data source check if the data is in correct format, any data source filter needs to be applied or should we use the data interpreter etc. Connections can be Live or Extracted as per the requirements.

What is Live & Extract? (Refer the link given below)

https://www.tableau.com/about/blog/2016/4/tableau-online-tips-extracts-live-connections-cloud-data-53351

If the data is not sufficient in one table, you can take another table using joins.

Now go to the sheet. It would be the first step moving forward creating your very first dashboard.

Tableau divides its data in two types- Measures & Dimensions.

Now Dimensions are something which contain qualitative values like Name, Date, Country etc.

And Measures are those field that can be aggregated or can be used for mathematical operations. In short the numeric values of the dimensions are measures.

As I am using Employees data I can put their location in one sheet using Map chart.

For another view I have put Employees’ skills in two different sheets skill categories and skill-sub categories using name count in measures so that we can analyze how many resources we do have in each skill category.

In last view I have added resource information like their email address, service group, Resume also I have added using action filter.

Now go to the dashboard symbol put all the sheets together and create a visualize representation. You can apply filters according to the requirements also use format option for making you dashboard clean and colorful.

(Data security is the reason why I have hidden the counts and resource information)

For the practice you can download sample data from https://www.kaggle.com/datasets and create your own dashboard.

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