Introduction to Data Visualization

What is Data Visualization? Data Visualization is a graphical representation of data. We can say that visualization is the storyteller of data.

Why is Data Visualization important? The human brain is more focused when it receives information in the form of graphs and charts. When we visualize a large amount of complex data, it is easy to identify hidden important information, patterns, and areas that need more attention.

How important is Data Visualization? Modern-day, a lot of companies are dealing with a huge number of data and companies need their growth concerning some of the aspects, they want their data to be in a more understandable way. Therefore we visualize the data and identify hidden pieces of knowledge. Visualization is something that gives more value to your raw data. Those values make critical business decisions.

How Data Visualization impacts your business success (benefits)

The business success in any company depends on how fast its decision-making process against its environmental and market influences. The faster the data reaches the decision-makers, the chances of speedy adoption and recovery increase, as well as it gives the ability to react to business changes fluidly.

The visual interpretation of insights presented strategically is crucial to any enterprise. More eye-catching, understanding, straightforward and real-time the visualization is, more empowerment to the strategic level is received to conceptualize their business situations and forthcomings.

To explain this let’s take a company that operates with a large amount of data, in a volatile market, like telecommunications. Such a company must have a real-time data updating process, as it’s required that those data reach the top level on an hourly or regular basis.

• Finding new market opportunities using data anomalies.
• Determining changes in business areas like network consumption, sales by region, and churn.
• Understanding customer behavior patterns in the market.
• Factors that influence customer satisfaction.
• Monitoring telco product performance.

Understanding such key performance indicators can enable businesses like telecommunications that invest billions for the establishment of various infrastructures for network coverage, facility services, and satellites. Primarily to improve the price performance of products and services across regions, that will help to create goals for sales and marketing activities and business continuity, ultimately leading to better customer experience.

Some of the professions that data visualization becomes critical are logistics, marketing, retail, finance, and insurances. All but requires immediate insights on their present market.

What is BI (Business Intelligence)?

Business intelligence is the process of transforming data into insights that can forge conclusive strategies to develop and improve businesses. This is a technology-based approach that combines business domain knowledge with engineering and scientific methods like the Data Extraction and Transformation Process and Data Visualization.

ETL (Extraction Transformation Loadings) Process

The ETL process is the process that we want to complete our BI goals.

Basic Charts in Visualization

In visualization, it is mandatory to use some of the basic charts to represent data in terms of graphical components. Mainly used visualization charts are given below,

Scatter Plot

This graph represents two data features on the X & Y-axis. We use this when we want to identify any pattern in between those two features.

Bar Chart

One of the simplest ways to represent data is a bar chart. Which represent categorical data as rectangle bars as heights concerning another feature.

Pie Chart

A pie chart is a circular chart that shows one categorical data feature in slices of a circle and is very commonly used to represent data.

Line Graph

Line graphs consist of two axes, the horizontal axis, and the vertical axis. This will be used to show timeline data concerning any other feature to see any trend over time.

Box Plot

Box plot is a summary statistical representation of any continuous feature. This ability gives us statistics such as minimum, first quartile, median (second quartile), thirst quartile, and maximum. Also, this will help us to detect outliers.

Spider Chart

This chart is a very good representation to use comparison visualization to few data features (multivariate data). We plot this group of values against the common variable.

Maps

When our data is in a geographical domain we use maps to represent those data. This helps to place data on a map area to detect locations.

Basic Visualization Tools and Libraries

Basic and simple data visualization libraries are in R and Python tools. R consists of a few packages that do help to plot data in any charts. But the most usable one is the ggplot (ggplot2 package). Also, plotly and lattice are there to make more visualizations. For mapping purposes, we have leaflets and gmap packages. Python also has wonderful visualization libraries such as matplotlib and seaborn.

Popular Industry Level Visualization tools and evaluation measures

What is a Data Visualization tool? In simple words, a data visualization tool is an application that takes data from any particular source and turns it into visual charts, graphs, tables, and dashboards. When we say “top”, we should follow some measurements. Which are visualization tools should be,

• User friendly
• Inexpensive in terms of price and license
• Powerful and fast
• More capabilities with insight tools
• Easy use-case scenario
• Capable enough to handle big data

Software-Based tools (Desktop Applications)

1. Tableau:

Tableau is one of the most used software to visualize data. Which helps you to make interactive visualizations of your data very quickly. Tableau is easy and capable enough to deal with big data and a very fast tool. Also efficient to work with relational databases and cloud-based databases. Tableau ecosystem includes desktop application, public application, reader, Vizable, mobile, prep builder, and CRM products. Tableau Public is free and open-source software to use for any user. This allows you to have 10GB of data storage for your data sources and able to connect SQL databases and Redshift as well. Also which has the Tableau Creator version, which costs $70 per month.

2. Microsoft Power Bi:

Power BI is a cloud-based Business Analytic and Visualization tool by Microsoft. This is very much capable enough to handle all the Data Mining tasks, such as Data Warehouse, Data Preprocessing, Data Discovery, and interactive dashboards. Power Bi’s user-friendly ecosystem contains various tools to work with different platforms such as desktop, mobile (android/ios), online services work with Office 365, API (Application Programming Interface), on-premises database, etc. Power Bi has an impressive compression ability so that helps to deal with big data easily.

3. RStudio Shiny Dashboard:

Shiny is an R package that helps to build interactive dashboards and web apps. Which support real-time updates, automation, CSS themes, and HTML widgets. It is free and open-source. Considering data Shiny can deal with data in databases, MySQL and Redis.

4. Fusion Charts:

FusionCharts has products such as JavaScript charts, Maps, Widgets, and Dashboard. Which also supports connection real-time responsive charts, extensive reporting, API, and cross-browser support. Used by close to 1M users across 28,000 companies worldwide. Companies such as Apple, Google, Intel, Yahoo, IBM, etc. Its basic package cost around $499 per year.

Web-Based tools (Web Applications)

1. Qlik:

QlikView is a BI (Business Intelligence) tool, which helps to know hidden information (knowledge) from raw data. It is one of the rapidly developing BI and Visualization tools, which is very fast to develop and deploy. As a customer, it is very easy to learn. It is free for individual use, students, and small start-ups. It costs around $89 per month for 10 users for a small business. QlikView can handle cloud-based large data and have more valuable Data Analytics and Data Integration tools.

2.Kibana:

Kibana is an open-source data visualization and dashboard creation tool, integrated with ELK (ElasticSearch, Logstash, Kibana) stacks. This is used to specially handle logs and time-series data with all types of graphical methods. Which also supports auto-update and automated data and additional elastic cloud data. Very user-friendly to handle the tool and can quickly develop.

3. Grafana:

Grafana is open-source cross-platform analytics, visualization, and web application tool. We can connect the supported data source to it and get an interactive visualization dashboard as output. Grafana consists of products such as cloud dashboards, cloud logs, cloud metrics, and cloud traces.

4. AWS QuickSight:

Amazon QuickSight is a Machine Learning powered BI tool that allows you to comfortably create convertible dashboards that help to see the insight into data. This supports data from cloud and amazon storage services such as Amazon S3, and Amazon Redshift. QuickSight will charge you a fixed cost per month.

5. DataBox:

DataBox is a very customizable and easy dashboard designer that helps to track your business trends. This allows to connection data from cloud platforms and simply just clicks, drags, and drops will create a meaningful dashboard. 70+ data sources and can edit any dashboard templates.

6. Dundas BI:

Dundas BI is a BI and real-time data insight visualization tool. This supports creating dashboards, reports, and visual data analytics on insights of data. Dundas Bi is adaptable, rich, and fast with more flexible tools. You can connect any data source with any type (relation databases, web-based, cloud, ODBC, and OLAP).

7. Google Charts:

Google Charts is a simple but powerful and free toolkit to demo your live data in a visualized manner. Consists of a rich gallery, which has all the charts and you can find the best fitting according to your data. Connect your live dynamic data feed to control the dashboard automatically.

8. Google Analytics (Data Studio & Firebase):

Google Analytics is a web analytics service by Google, which tracks and reports about your website and makes dashboards based on your website’s data. Google Data Studio service gives access to free tools that will turn your data easy and simple, understandable, and educative dashboards. You just have to add your data source and simply build the dashboard by adding the necessary charts. If you are interested in mobile application development, Google Firebase helps you to develop and understand, improve the usage of app data, and how to grow your app.

9. infoGram:

Infogram is an easy and infographic chart maker that supports you to make digital charts, infographics, maps, and dashboards as visualizations. You are allowed to connect any kind of data source say CSV, GDrive, MySQL, JSON feed, MS SQL Server, AWS Redshift, and Postgre SQL. The basic plan is free forever having some of the important features.

10. Sisense:

Sisense is an AI-driven BI tool that can do data analytics and creating dashboards. This has some products such as BI dashboard solutions, Data connectors, Analytics software, Big Data Analytics, and Cloud BI. Additional features like developer playground are there for developers to work with SDK and APIs. Before going to buy standard plans for Sisense you can have a free demo trial to load data, data preprocessing, and creating visualizations.

Mistakes made when visualizing data

• Choosing the wrong chart type (wrong visualization method) to represent data.
• Choosing the wrong data type.
• Choosing the wrong tools.
• Not identifying the correct outcome of visualization.
• Use undeserving color combinations.
• Not using 3D graphical views.
• Including too many variables.

Understanding these mistakes and overcoming these problems comes with the practice and experience you have as a data science enthusiast.

Examples of the real-life scenario on Data Visualization

• The population of a country with age groups and gender – Funner Chart
• Daily number of Covid-19 patients (Worldwide Covid-19 dashboard) – Line Chart
• Areas of the highest number of Covid-19 positive patients – Map
• A daily routine of famous people – Full stacked bar chart
• Dashboard for the taxi-driving company – Dashboard
• Internet service providers signal tower sources – Map
• Public age with loan application – Funner chart

How Rhino-Partners does it

We are happy to announce that we are very much capable enough to work with these technologies and tools as the Rhino-Partners Data Science team. You will experience our widespread domain knowledge and exposure to business processes, consumer behavior, and product performance combined with our specialization in scientific approaches for unveiling hidden intelligence from data. Our Data Visualization discipline is more focused on producing interfaces that best suits the business nature, in terms of KPIs or the tool that offers the best advantage to the client.