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Leverage the Power of Data with Data Visualization

trantorindia | Updated: June 27, 2018

Data is power. But only when you know how to use it.

To understand and predict customer behavior, companies are gathering information from multiple sources – social media, transactions, human resources, Google Analytics, and so on. But without a well-defined strategy in place to make use of this information, it soon becomes a liability to manage this huge amount of data.

Sometimes just looking at numbers and statistics doesn’t give you the whole picture. It literally takes a picture (graphs and charts) to see the whole picture.

Data Visualization Is the Key

Data visualization brings life to what otherwise is just a huge pile of information. It helps employees to assimilate a vast amount of data in one go; thereby, enabling them to see key patterns and draw executable insights to strategize effective business solutions.

Visualization empowers us to see and interact with data in an entirely novel way, which allows us to recognize hidden patterns which are virtually impossible (or very difficult) to see if you are sifting through data manually or even via a basic analytics tool.

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Image: Data in tabular form Vs Data in visual form

Before moving forward, let’s quickly list down a few advantages of data visualization:

  • Easy assimilation of huge pile of information at once
  • Interpretation of complex data into easily understandable graphics
  • Improved recognition of hidden patterns (pertaining to customer behavior, business operations and decision-making activities)
  • Quick access to relevant business insights
  • Rapid identification of upcoming trends
  • Formulation effective business strategies in advance
  • Precise customer sentiment analysis, and many more

Today, there is no lack of data visualization tools – Tableau, QlikTech, FusionCharts, Datawrapper, Plotly, and more. Their applications are widespread –across industries, business sizes, and business operations (such as recruitment, HR, sales, finance, etc.). The data visualization tool market is also evolving quite rapidly. Besides creating charts, videos, infographics, etc., there are tools that allow you to create graphics and presentations even in AR/VR channels.

Suggested Read: How to Leverage Data Analytics in Your Marketing Strategy

Now, let us discuss a couple of examples to show how data visualization helps to read data in a quick & easy-to-understand way, gather valuable insights, and make informed & smarter decisions.

Business Banking: Asset Portfolio Spread – US

The first example shows spread of small business loans portfolio of various US banks from 2009 to 2017 across the US by multiple factors like status, state, amount, and industry.

The data we had was comprised of details such as which bank has lent how much money to how many entrepreneurs, which state the bank belongs to, in which state the business was set up, the industries of the businesses, loan status, and so on.

Clearly, it was a vast amount of data, which was quite difficult to understand, let alone be insightful. But as we began to put it into interactive charts and graphs and maps (with the help of Tableau Data Visualization Tool), it became possible to comprehend all the data and identify insightful areas in it within a couple of minutes.

Source: sba.gov

Click on the image to view interactive graph in a new window (for mobile, use the Tableau app for best experience)

Now, with all this data in visual format, it is possible for banks to draw insights like:

  • From which states they are getting the most business
  • How the Bank’s portfolio has changed in last 10 years – in which states the exposure increased, in which it decreased. This can be useful in decision making for other bank divisions (retail operations, customer service, etc.)
  • In which states and industries there are biggest opportunities
  • How has a particular industry performed in last three years? Is a given particular industry a factor enough to review the risk assessment model of bank?
  • Which bank (competitor) is most active in which region and industry, and so on

With such insights at tip of bank’s fingers, it certainly becomes easier for them to come up with strategies to explore new opportunities and manage risk more effectively.

Democratic Election: India (2014)

In this example, we have taken the data of democratic election in India (world’s biggest democracy). The data is comprised of details such as competing parties, states they are contesting from, state-wise number of votes they received, which candidate received how many votes, and so on.

Given the size of Indian Democracy (543 parliamentary constituencies and over 800 million voters), the amount of data was obviously enormous. With the help of Tableau tool, we put the data into interactive visual format and were able to draw insights that quite difficult to find with data in tables and sheets.

For instance, at many points the competition between the top 2 parties (BJP and INC) was much closer than what had been reported. In media, it was regarded as a big victory, while the numbers tell a slightly different story.

Source: data.gov.in
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Click on the image to view interactive graph in a new window (for mobile, use the Tableau app for best experience)

Seeing such semi-hidden details easily through visual data, parties can find many key insights to better prepare their campaigns. Insights such as:

  • Which state and voter type supported them in which area
  • Where they could have been better
  • In which states and constituencies the margins were close

Concluding Notes

Data in itself is like inert, raw material. Visualization tools enable us to present it in way that it can reflect hidden patterns and empower organizations with insights to respond to their business’s and customers’ needs more effectively. However, using visualization tools in itself is not the solution. You also need to brainstorm over its presentation to make the visuals as intuitive as possible, since only then the sole purpose of data visualization is fulfilled; which is – to make enormous amount of complex data easy and quick to read and comprehend.