Data and Visualization: the Crucial Connection

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It’s a big word with big implications. The process of mining data is among the most significant solicitations of data warehouses. However, once you obtain all this data, what do you do with it?

In his noteworthy TED talk, David McCandless, a data journalist and designer, illuminates the audience about why the visualization of data is progressively dynamic and fundamental to how we extract the necessary information from it.

“It feels like we’re all suffering from information overload or data glut. And the good news is there might be an easy solution to that, and that’s using our eyes more. So, visualizing information, so that we can see the patterns and connections that matter and then designing that information so it makes more sense, or it tells a story, or allows us to focus only on the information that’s important.”

This process of knowledge discovery analyzes data from distinctive perspectives and then encapsulates it into valuable information, which then can be used to increase profits, decrease costs, or both. Converting data into visualizations is the key to accomplishing this. It lets users examine data from several assorted aspects or angles, label it, and summarize the relationships identified. Once this is done, it is easier to find correlations or patterns among stacks of fields in large relational databases.

“…it feels like visualizations, infographics, data visualizations, they feel like flowers blooming from this medium. But if you look at it directly, it’s just a lot of numbers and disconnected facts. But if you start working with it and playing with it in a certain way, interesting things can appear and different patterns can be revealed.”

Essentially, you can discover configurations and associations hidden in your data. Because data is a living entity, locating these patterns is the indispensable component that advanced statistical analysis and modeling techniques are applied. It goes beyond numbers: you get to visualize ideas and concepts, and more importantly, it allows you to explore views. The development of information discovery is ultimately crucial for successful data mining because it defines the actions you must take to certify substantial outcomes.

Nowhere is this displayed better than in the recent purchase of LinkedIn by Microsoft. In this article written by Dries Buytaert, he writes, “By acquiring the world’s largest professional social network, Microsoft gets immediate access to data from more than 433 million LinkedIn members.”

How will Microsoft utilize this data? What kind of information can be transformed into knowledge about past patterns and future trends?

Microsoft’s analysis will allow them to solve complex problems and better understand where and what is happening in the world. It goes beyond data acquisition. Now, the corporation can study the characteristics of people and the relationships between them. This analysis offers perspective to decision-making.

Consequently, being able to better comprehend your data should assist you in making suitable decisions about your analysis—and you should be able to predict how your choices will affect the results.

As more companies realize the power that analyzing their data brings, they will see how data can solve problems, form certain perspectives, and change minds midstream. We find ourselves at a time when, for many instances, we are no longer just mining data and storing it. Instead, we are challenging ourselves to expand our understanding of the meaning behind the data.

I welcome you to reach out to me and connect via LinkedIn at

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