Data science news

When is data visualization useful?

In order to know when data visualization should be used, it’s a good idea to start with why we even use it at all, and what makes it work.

Why visualize data The whole point of taking data and turning it into more understandable information is so that we can utilize it to make a decision or take action from what we learn. Data visualization is just another way of turning data that we can’t read or understand and turning it into something that we can see and use. In other words, creating information with visible insights.

In general there are three reasons why you might want to visualize data:

1.Education – Many visualizations are valuable because they educate or report on a specific topic. These can also provide insight into changes related to a topic over time, so that you are able to understand trends and learn from them.
2.Exploration – As more data sets become increasingly larger, it can be tough to easily spot relationships between them and create predictions. Visualization can make this easier to understand and manage.
3.Confirmation – If there are assumptions about a subject, and data has been collected, visualizing it can be a useful way to prove or disprove the assumption.

Visualization: The Simple Way to Simplify Big Data

Data visualization has an amazing ability to make the complex simple, and the latest tools can do much more than give everyone the same view of data. It’s only through visualization that we can take something as abstract as symbols and turn it into a physical image that has dimensions that our eyes can quickly see and our brains understand. We can grasp data’s meaning more quickly. Visualization tells us when trends are heading in the wrong direction and we need to intervene.

But even more, when we visualize the scope and scale of today’s enormous data sets, we pick up things with the naked eye that would otherwise be hidden. We can see data’s previously untold story. We can see where Pennsylvania’s Vote ID Law is disproportionately affecting minorities and students. Visualization of Twitter data can show us a revolution as it unfolds in Cairo. What was being reported in the media as mass crowds forming in Tahrir Square comes to life when the network data becomes visualized as it grows.

Big Data Analysis Vs. Government Spending

Federal IT professionals estimate that government agencies potentially can save 14% of their budgets, or nearly $500 billion across the government, from successfully analyzing big data. But while nearly one-fourth of federal IT managers in a new government poll report their agencies have launched at least one big-data project, only 31% believe their agency’s big-data strategy is sufficient to deliver on that potential.

The numbers come from a recent report, “Smarter Uncle Sam: The Big Data Forecast,” by government IT networking group MeriTalk. The report, sponsored by EMC Corporation, is based on a survey of 150 federal IT professionals.

How Financial Institutions Can Benefit from Predictive Analytics.

It’s an enduring business challenge: How do you raise customer satisfaction and increase profitability in a mature, saturated, and highly regulated industry? Traditional solutions focus on incremental efforts around the edges—pricing offers, product tweaks, and payroll cuts. These have often led to poor customer service and unprofitable discounting.

A different approach, based on proven analytic technologies, supports more positive and sustainable outcomes. Customer service, with millions of interactions each day, is the logical place to implement this approach.

Advances in predictive analytics give financial institutions (FIs) a much deeper understanding of their customers’ needs, sentiments, and behaviors. Equipped with better insights, FIs can implement highly accurate modeling capabilities to improve the customer experience at every contact. Furthermore, integrating analytics and modeling into business operations sustains profitability and customer satisfaction over the long term.


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