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Data visualization is the technique of representing information in graphical or pictorial form to communicate the correlation between the data. With the help of visual representation, information is explicated in schematic form to provide quick glimpse of the subject matter. The concise description of the data by means of graphs, charts, and points enable decision makers to get a summarizing view of the data analysis and identify new patterns or trends that might have gone unnoticed while observing the text based data. It acts like a natural extension to the thinking process so that subject matter experts can explore hidden capacities.

With the help of illustrations, complex ideas and insights can be astutely signified. Today’s extensive reference to data visualization for business planning has gone beyond the general usage of spreadsheets, histogram, pie charts, and heat maps. Data visualization is rapidly evolving with newer indicators and interactive facilities to grasp the evolving corporate landscape.

Importance of Data Visualization

Human brain can process an image faster than a paragraph portraying the same description. Since humans are visually cognitive, pieces of information using charts and graphs help business enablers to decipher complex information swifter. Since data visualization are symbolizations and not text-intensive, it becomes an easy to convey the information universally.

In the last couple of years, nearly 90 percent of all the data in the world has been mined and a research shows that nearly 2.5 billion gigabytes of new data are generated each day. Amidst the data explosion, it becomes imperative to get access to easily understandable graphs to make meaning of the otherwise unrelated information. While data analysis helps in finding the accurate information, data visualization is the architecture behind constructing that story in a visually impressive manner.

Data visualization helps in:

  • Locating precise points that needs careful attention
  • Building association between seemingly unrelated data to understand customer behavior
  • Predicting future trends in sales or business
  • Guiding users to make more informed business decisions

What are the different usages of data visualization?

There can be six ways of defining data with the help of charts, and each quantitative correlation of data responds to a unique message.

  • Depicting time-period: With the help of line charts, one can arrange data as they were recorded over a period of time. For instance, if a company wants to find the trend of car sales in Brooklyn, they can refer to a line chart that can demonstrate per year sales.

  • Grading: Comparing and positioning the data in orders of the rank, for example in terms of performance or sales is another broad practice of using data visualization.

  • Understanding ratio: To comprehend the ratio of a segment with respect to its totality, pie charts or bar charts are used to display market share of the variable.

  • Digression: Apart from understanding market ratio, a bar chart can also be used to evaluate the validity of data alongside another reference. For example, one can get a pictorial view of “planned versus achieved” with the help of charts.

  • Frequency division: Typically, a histogram is used to record the number of observations of a variable over a time-period. These graphical forms can be used to analyze ascend or descend of the variable over time to make informed decisions.

  • Geospatial distribution: Using a cartogram, one can compare a variable across the geographic limit to analyze a particular trend that lay over a city, state, or country. For instance, finding out the literacy rate of people by their states in a country.

Overcoming big data challenges through visual analytics

With the advent of big data, companies are poured with petabytes of raw data that requires processing to turn into invaluable insights for the business. However, the traditional infrastructure available for the process is not enough to meet the surging demands. As the demand for data-driven business strategies grows, IT teams are found laden with requests for ad hoc analyses. Delay in decision-making equals to loss of business. To counter the problem, more and more numbers of self-service data visualization software are becoming popular. Using automated data visualization tools, one can turn millions of rows of data into an insightful graph quickly.

By pairing the concepts of visual analytics and big data together, business leaders can utilize all the data they receive in building a logical relationship through illustrative methods. Automation in the process would also free the analysis from any human error in the using the insights.

With increasing acceptance of data visualization as a vital component of analysis in the age of big data, there is also a notable increase in associated technology such as in-memory technology and high-performance analytics to offer better results faster.






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