Introduction to Data Visualization

Data visualization is the graphical representation of information and data using different visual elements like charts, graphs, etc. Data visualization tools provide an accessible way to understand trends and patterns in data and also help in revealing insights.

Data visualization is both an art and a science. Data science requires modeling, testing of data, and mathematical validation of the facts. However, how one chooses to display this information requires attention to both aesthetic and technical details.

To correctly visualize the data, an excellent data visualization tool is mandatory nowadays. With the help of these tools, one can visualize the data beautifully and simply.

Data can be hugely misrepresented due to a lack of understanding or an oversimplified aesthetic treatment. Data visualization requires an understanding of the raw data and the skills to utilize graphical elements wisely and accurately to depict actionable insights.

I assume we must be conscious that statistical evaluation is an area of difficulty to our preferences; i.e. statistics may be manipulated to permit its writer to tease out preferred results.

Thus, we need to be vigilant in reading our statistics. We need to be deft authors of design, using our capability as artists and scientists to unearth the underlying goal truths in our statistical data sets.


Increasing Use of Data Visualization & the Problems Associated


Many companies have succeeded in implementing analytics to their Business Intelligence Systems. They are using it for accomplishing various purposes. Every aspect of data analysis and business intelligence, whether data warehousing, data mining, or data visualization, is helping businesses to increase their ROI.

The companies like Amazon, Netflix, etc., are rapidly adopting a data-driven culture. They have been associated with excellence and innovation-driven by facts and metrics programs. They are huge believers in the power of data.

The companies are using the strength of data to improve their products and services. Of course, many other companies have full-fledged analytics programs in place. They are continuing to mature their usage in more and more areas.

In some companies, however, there is more inertia to using analytics. They have potential users to improve their adoption of it. But, after assigning good talent to analytics, giving them the right environment, budgets, and tools, they find that somehow the efforts fail to go further in terms of open adoption of the results by the business.

There could be several reasons why even a good quality output does not derive business benefit. One reason could, of course, be the lack of adequate drive and interest in analytics from the top. But if that is in place, another reason must be that the results are not presented to the business interestingly and understandably.


Data Visualization as the Solution to Problems

The presentation of results is best done through the visualization of the data. Statistical formulas may hold fascinating meaning for the data sciences team. They are already intimately familiar with the details, but they are nothing more than just lifeless numbers to the lay business user.

Statistics need to be made more easily understandable through the right data visualization technique and brought to life with the right story to present them. But, unfortunately, knowing how to use them effectively is more an art than a science.


Data Visualization as a Science

Being a part of mathematical and statistical analysis, data visualization is undoubtedly a science. Therefore, data scientists and analysts apply all the scientific approaches to develop the best output presenting the data.

Features of Data as a Science

  • Intuitions for Insights
  • Curiosity for Correctness
  • Systematic Organization
  • Proper Procedures

Despite all these features and possessing a structured scientific approach, data visualization is more an art of presenting data interactively so that any lay business user can understand it. Might he not analyze or predict, but at least he can understand the statistics related to his business.


Data Visualization as an Art

Any presentation of ideas and data is ultimately an art of selling, no matter what the purpose is. And this is why it is important to present the facts in an interesting and relevant way. There are a variety of presentation formats to choose from. These are simple data plots, line diagrams, bar charts, and basic pie charts.

Tableau, Fusioncharts, Google Charts, Thinklytics dashboards, Chart.Js, and many more are the alternatives for more visually appealing charts and more developer flexibility. For higher data volumes, dygraphs is a popular alternative, available as a Javascript library.

Features of Data as an Art

  • Aesthetic Design
  • Colour Texture
  • Intuitive Understanding
  • Open for Innovations

As an art, Data Visualization comprises all the essentials of being an art right away, from colour selection to aesthetic design. As an art, data visualization is always open for innovations. The creativity in presenting the data is also a sign of art.

Consequently, even though the ability and aptitude of the analyst’s group might be first-rate as far as the nature of their productivity and discoveries, their capacity to pick the correct presentation technique and to utilize the correct devices to feature the central issues while screening out the commotion in the information may have a major effect as far as acquiring the consideration of the business.

Data visualization sometimes behaves like the art of storytelling. Data narrators who are the storytellers, in this case, are acceptable communicators and journalists; often, they are imaginative and visually oriented.

Organizations are making cross-breed information representation groups, consolidating individuals with insightful and imaginative abilities to make one unit that can dissect the information and present the hidden story.

They change petabytes of often obscure data into meaningful information for the business, making representations that the normal business chief can rapidly comprehend. The “data artist” is certainly not another thing.

One of the first was Charles Minard. He made a measurable representation of Napoleon’s walk on Moscow. He consolidated a few arrangements of information—number of troops, course, the volume of troops, temperature, and so on—to make what is to a great extent viewed as the primary perception of hard information.

Like a decent story needs chapters or checkpoints en route and should close with a comprehension of the big story and how every one of those turns out together for a net outcome. Furthermore, in particular, it needs a principle character: in this case, the data.

While data analytics are at the foundation of finding insights, it’s not just about transforming that information into graphs any longer; we currently expect to create a story that gives understanding or permits you to make a move.

The “artists” transforming the information into a story needs to have sufficient business experience to comprehend the business need, which gives the capacity to offer psychological discernment and noteworthy bits of knowledge.


Let’s Sum Up

The present data visualization is a three-legged stool: science, art and intellectual discernment. Without every one of the three of these, the information stalls out in the “information bucket”, conveying data on a page rather than a meaningful new insight.


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