What is Data Visualization?

Data visualization (data graphics) is the data presented in a visual status such as a graph or a scenic structure in order to be usable and easy for people to understand. It is mainly an instrument for reasoning about quantitative information. Some of the data that are not discovered in context can be simply recognized in visualization software. Due to the easy accessible of data analytics that is assorted and represented impactful visually and had a better efficiency in representing the information, decision makers can identify new patterns because without doubt, it is like taking a further step by examining the graph or chart for more details and information and engaging the message quickly. Since the 1800’s, people have been using graphs and maps to understand the data. However, computers and technology recently have reduced the time consumed in processing large amount of data we have.

What are the Tools Used in Data Visualization?

Other than the usual standard tools that are used in data visualization, such as pies, graphs, charts and spreadsheets in Excel, there are some tools that are more advanced like heat maps, fever charts, infographic's, dials, Sparklines and geographic maps. Although some techniques are more sophisticated, but dashboards and visualizations need to stay simple and digestible to make the overview information easy to follow, accessible and understandable. When using graphical presentations, a huge amount of data gets clear and it can draw a conclusion to address the issues before becoming bigger problems and answer questions in a short period of time (comprehend the information quickly). The main challenge in this step is choosing the right visual that illustrates the data in the best way and deciding which data is appropriate for each model. For example, to track the relationship between variables, we use Line Charts. To compare parts of data, we use Pie Charts. To know the joint variation of two or three data items, Scatter Plots, and Bubble Charts are used sequentially. Finally, to compare quantities of different categories, Bar Charts are the best. The data visualization technology from SAS is a great example of a program that delivers fast answers to complex questions regardless of the size of your data. Data visualization will lead analysts to look at the data differently and act towards it in a creative way after digging for more exploration and insight.

What is the Importance of Data Visualization?

When it comes to dealing with data, the human brain comprehends the processed input better if demonstrated and sorted out in graphs or charts to visualize the whole amount of data. In addition, data visualization can also resemble zones that need improvement, explain which factors make a change in the behavior of customers and influence them, assist in where to place the products, and predict the sales volumes. In the value of data visualization, there are two major types: (1) exploring, which is finding a story the data is telling you, and (2) explanation, which is telling the story of data to the audience. Storytelling with data visualization creates an impactful response from the user with the numbers to back it up. Some of the tools that are used in building data visualizations are Google Charts, Tableau, visual.ly, and Modest Maps (mapping tool). A major scope of data visualization these days is emerging the business world. This type of visualization adds a new dimension in helping to understand the issues at the present time.

          To conclude, Data visualization is a wise investment in your big data future. It makes the huge amount of information and data you collected makes sense and communicate better by correlating and comparing the information you have. It is necessary to simplify data by using graphs and charts for people, including employees and audience, to recognize the information given, act upon it, and make the important changes in order to get the best results in the organization. For that to happen, there are a various number of tools that can be generated to conclude the finest conclusion such as infographics and geographic maps.

https://www.gooddata.com/blog/5-data-visualization-best-practices

https://www.sas.com/en_us/insights/big-data/data-visualization.html#modal2