Data Visualization: The PATH of Visualizing DATA
A picture is worth than a thousands words…..!!
Have you ever wonder how a company manages all the data of its thousands of employees? With everyday changing data, how the success rate of a company is compared with its competitors ?
The answer to all these questions is “DATA VISUALIZATION”
What is data visualization?
Data Visualization can be defined as the representation of the data in the form of graphs,charts, and other pictorial forms.
Data Visualization enables users and decision makers to see analytics presented visually so that they can grasp difficult concepts easily. It is generally done to assist IT administrators in getting quick, visual and easy-to-understand insight into the performance of the underlying system. Most IT performance monitoring applications use data visualization techniques to provide statistical insight of performance of the monitored system.
The process of visualizing data can be easily understood from the following flowchart:
The above figure shows the process of data visualization in which heterogeneous data ( dissimilar type of data ) is converted into info-graphics (visual representation of data or information) for the ease of understanding.
But how can we convert the heterogeneous data into bar graphs, histogram dashboards or scorecards ?
The answer to this question is there are various data visualization tools that can help the analysts to convert the data into info-graphics easily.
Lets have a look at some of these tools !!
Data Visualization Tools :
- Tableau:
Tableau is often regarded as the grand master of data visualization software and for good reason. It is particularly well suited to handling the huge and very fast-changing datasets which are used in Big Data operations, including artificial intelligence and machine learning applications, thanks to integration with a large number of advanced database solutions including Hadoop, Amazon AWS, My SQL, SAP and Teradata. Extensive research and testing has gone into enabling Tableau to create graphics and visualizations as efficiently as possible, and to make them easy for humans to understand.
2 . Plotly
Make charts, presentations and dashboards with this flexible software. You can perform your analysis using JavaScript, Python, R, Matlab, Jupyter or Excel, and there are several options for importing data. The visualization library and online chart creation tool allow you to make great-looking graphics.
3. DataHero
DataHero enables to pull together data from cloud services and create charts and dashboards. No technical abilities are required, so this is a great tool for every person who is either technical or non-technical.
4. Chart.js
Although armed with only six chart types, open source library Chart.js is the perfect data visualization tool for hobbies and small projects. Using HTML 5 canvas elements to render charts, Chart.js creates responsive, flat designs, and is quickly becoming one of the most poplar open-source charting libraries.
5. Timeline
Timeline is a fantastic widget which renders a beautiful interactive timeline that responds to the user’s mouse, making it easy to create advanced timelines that convey a lot of information in a compressed space.
Each element can be clicked to reveal more in-depth information, making this a great way to give a big-picture view while still providing full detail.
Process of data visualization:
An American expert of data visualization , Ben Fry , described the seven stages of data visualization in his book “Visualizing data” .
These seven stages are also termed as the process of data visualization.
The following process forms the path to the answer :
- Acquire
Obtain the data, whether from a file on a disk or a source over a network.
2. Parse
Provide some structure for the data’s meaning, and order it into categories.
3. Filter
Remove all but the data of interest.
4. Mine
Apply methods from statistics or data mining as a way to discern patterns or place the data in mathematical context.
5. Represent
Choose a basic visual model, such as a bar graph, list, or tree.
6. Refine
Improve the basic representation to make it clearer and more visually engaging.
7. Interact
Add methods for manipulating the data or controlling what features are visible.
But still a question arises in our minds, WHY Data visualization is needed ?
Let’s have a look at the IMPORTANCE of data visualization…!!!
Why is data visualization important?
Because of the way the human brain processes information, using charts or graphs to visualize large amounts of complex data is easier than poring over spreadsheets or reports. Data visualization is a quick, easy way to convey concepts in a universal manner — and you can experiment with different scenarios by making slight adjustments.
Data visualization can also:
- Identify areas that need attention or improvement.
- Clarify which factors influence customer behavior.
- Help you understand which products to place where.
- Predict sales volumes.
Conclusion:
The greatest value of a picture is when it forces us to notice what we never expected to see.
— John Tukey
Data visualization is going to change the way our analysts work with data. They’re going to be expected to respond to issues more rapidly. And they’ll need to be able to dig for more insights — look at data differently, more imaginatively. Data visualization will promote that creative data exploration.
