Importance of Data Visualization

Data visualization has become part of our day to day life. For example when you rent a cab in Uber or Ola, you get a summary report of your travel, which utilizes a map. Postpaid users get an itemized bill which also analyzes the calling patterns and summarizes the proportions of local, STD, ISD calls etc., Recently, all fitness tracking apps show dashboards. For decades, most of us are used to the charts on stock prices.

But recently data visualization has gained more traction in almost all industries. Every organization has started building its own data science team, in which data visualization plays a vital role. In this article I have highlighted a few reasons why data visualization is important. Nevertheless, there exists multiple other reasons.

1. Consume Big Data

Data across the world is growing every micro second. It is a fact that more data has been created in the past few years than in the entire history of human kind. It is estimated that by 2020,  about 1.7 MB of new information will be created every second for every human being.  But on the other end, how are we going to make use of this big data? We need to increase our consumption rate drastically. Visualization is one of the important techniques to consume big data.

bigdata.jpg

2. Communicate faster and better

It is highly competitive atmosphere in any field you take. That too the number of startups which are coming up with novel ideas, disrupt the technologies, tools very easily and quickly. Unlike traditional approaches, organizations need to keep changing their market strategies as quickly as possible. You might have noticed that FMCG, network service providers come up with new offers every day. Hence there is a need to understand the real picture of market, financials, resources etc., as quickly as possible. Stake holders might not have time to sit and interpret numbers in the form of traditional tables. New visualizations are emerging to encode large amount of information in simpler form and also the development time for creating operational dashboards has drastically reduced. Stakeholders need not wait for reports from the business intelligence team. They can directly log in to a portal to understand the current state of affairs.

I-dont-have-time-.jpg

3. Telling compelling stories

From the below picture, almost everyone can easily identify the story. We easily remember the interesting stories learnt during our childhood but how many of us can recall the definition of “light year“?. Stories are easy to remember for a long time than boring numbers. Instead of saying “I have 32 Million dollars”, it is easy to remember “I have lots of money using which I can buy 100 cars“. It also stimulates the reader to visualize 100 cars parked together.

story-cinderalla.gif

3. Detecting patterns

It is a well known fact that human beings are pattern seeking animals. For example, most of us would have related the formations of cloud with human faces. We identify patterns easily from visuals than looking  at plain numbers. Detecting patterns is a crucial step for telling compelling stories from data.

4. Identifying outliers

Treating outliers is one of the essential steps while building any machine learning algorithm. Though it is always debatable, whether one should treat outliers or not, plots like box plots, histograms, scatter plots are commonly used to detect outliers in data.

5. Analyze trends

Before taking any approaches, it is always useful to learn from the past which will help up to spot trends. Among organizations it is always a dilemma whether to choose a commercial product like Tableau or an open sourced library like D3 for data visualization. The below chart shows the interest level for both D3 and Tableau in India for the past 5 years. The blue line represents D3 and the red one represents Tableau. One can clearly observe that before 5 years, D3 was very popular in India than Tableau. But the interest level has been gradually increasing for Tableau across the years, and currently both share almost the same average interest level. Spotting these kinds of trends is very easy using simple charts.

tableau_vs_d3

Source: Google trends (https://trends.google.co.in/trends/explore?geo=IN&q=d3,tableau)

6. Derive actionable insights

The ultimate goal in collecting data, interpreting and consuming it, is essentially to take some proper actions. Modern visualization techniques help business users to easily identify areas in which they need to focus and identify the root cause. That too with modern tools, it is easy to create interactivity either in the form of data drill down, knocking off certain elements and collaborating with team members.

Finally, “A picture is always worth than thousand numbers”. As mentioned earlier, these are not the only reasons why we need data visualization. In the near future, data visualization will change from optional to necessity.

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