How to Design Successful and Valuable Data Visualizations

Taft says, "Graphical representations of data. It's actually more accurate and illuminating than traditional statistical analysis." For a wide range of editors, designers, operations analysts, big data researchers and so on need from different dimensions, different levels, different granularity of data processing statistics, with the help of charts and infographics for the user (only access to information), readers (consumption of information) and managers (use of information for management and decision-making) to present the results of the analysis of the different tabular format. Data visualization technology integrates the use of computer graphics, images, and human-computer interaction to map the collected, cleaned, converted, and processed data that meets the standards and specifications into recognizable graphics, images, animations, and even videos, and allows the user to interact with and analyze the data visualization. And any form of data visualization will be rich in content, attention-grabbing visual effects, fine production of three elements, summed up is novel and interesting, full and efficient, aesthetic and pleasing to the eye three characteristics.

[why]Why data visualization?

Regardless of the profession and application scenario, data visualization has the ****same purpose of conveying information and knowledge accurately and efficiently, concisely and comprehensively. Visualization can transform invisible data phenomena into visible graphical symbols, can be intricate and complex, seemingly impossible to explain and correlate the data, to establish connections and links, to discover the laws and characteristics, to obtain a more commercially viable insight and value. And the use of appropriate charts and graphs is straightforward, clear and intuitive expression, to achieve the purpose of data self-explanation, let the data speak. The human right brain remembers images 1 million times faster than the left brain remembers abstract words. Therefore, data visualization can deepen and strengthen the audience's understanding and memory of the data.

[how] How to achieve reliable data visualization

Data visualization includes a series of complex data processing, including data collection, analysis, governance, management, and mining, and then designers design a form of presentation, perhaps 2D charts, 3D stereo views, no matter what kind of infographics, and finally front-end engineers create corresponding visualization algorithms and front-end rendering and presentation implementation. Just being able to transform data into pretty charts, designing charts with fixed dimensions and different styles to explain your point of view doesn't make such an ending good enough. It's a simple start, just the germ of a good idea. More homework needs to be done if you are to successfully report results that effectively translate the metrics and data you are analyzing into commercially valuable insights that can support decisions made based on facts.

Color enhances the visual impact of information visualization. On the basis that information visualization clearly conveys information and narrative through stylistic elements, grasping the use of color in visual elements makes the graphics more vivid and interesting, and the information is expressed more accurately and intuitively. Color can help people to classify information in depth, emphasize and fade, vivid and interesting visualization works of expression, often to the audience to bring the enjoyment of visual effects. Of course, the visual effect should integrate the enterprise brand hue into it, and the enterprise brand culture to maintain a high degree of consistency, which is a basic common sense. For example, if the enterprise's brand color tone is more keen on red, you design the visual effect, you have to consciously lean towards this tone. However, it is not necessary to match, because red visualization effect, usually contain warning rhyme, so, red is suitable for warning, reminding and highlighting the function of information.

Typographic layout enhances the narrative of the information visualization. I have wine, do you have a story? Typographic layout of the four basic principles:

(1) contrast (Contrast): If two items are not exactly the same, they should be made different, and should be very different.

(2) Repetition (Repetition): some aspects of the design are repeated throughout the work.

(3) Alignment: No element should be placed randomly on the page. Each item should have some visual connection to something on the page.

(4) Proximity: related items are organized so that they are physically close to each other related items will be seen as a cohesive group.

Dynamically increase the visual experience of information visualization. In the visual representation of information visualization, various forms of information dissemination that are separated from each other are dynamically and organically fused together in a connected and rhythmic information processing, transmission and realization. The ultimate goal is to explain the relationships that drive and link data representations in order to realize the linkages between them. Through the movement of chart styles and colors, it satisfies the audience's visual perception, while conveying the information content to the reader in a more profound and streamlined manner, making the whole process of information conveyance easier and more convenient. For data visualization there are many tools, such as: ECharts, iCharts, D3js, Flot, Rapha?l, etc. are very powerful, but for non-professional visualization and often deal with charts of the workplace, a lightweight, easy to learn and practical visualization software is very important. For example, cognos, tebleue and so on. If you need to show the data structure is not particularly complex, but also to show the data colorful and interactive, then the crystal easy table is the right choice.

1. Who is your reader?

Whether you're working on a traditional report or a new infographic, first ask yourself who is seeing the report? How much do they know about what will be discussed? What do they need? And, how will they use the information and data you're presenting? And as I said in "What's the formula for a solid data analysis report? I talked about how important it is to have clear analytic goals and methods, because it's only when you have clear analytic goals that you can have a well-driven process. Whether goal-driven or analysis process-driven, the subsequent data analysis work and analysis report to be presented in the entire content matters are tightly focused on the target theme and services.

2. Planning a data visualization program

Data visualization program, must be able to solve user-specific problems. Since it is able to solve user-specific problems, then such a high level, is based on the basis of your in-depth understanding of the phenomena and nature of these data. Simply put, it means that your visualization solution not only understands and can well explain the conclusions, information and knowledge of data analysis. And managers can quickly find and discover ways to make decisions along your planned visualization path.

For example, when a company is not performing up to its standards (and it is in the best interest of the company to do so), the visualization path should be designed so that it can be used as a tool to help you make decisions about the company's performance. The design path of the visualization program should be like this:

Step1, from the overall operation, to clarify what are the key factors will affect the transaction and performance.

For example: effective list, demo quality, customer service, product attributes, etc. Accordingly, we should look at the performance of the KPIs corresponding to these key factors, which will be the driving factors for the overall performance, and the KPIs corresponding to these factors will be the ones that will have a direct drive and impact on STV. The visualization of these drivers is the foundation and the starting and ending point for finding solutions. Because the performance of this data is the most direct view of operational success.

Step2, in-depth analysis of the key factors to determine what factors led to the performance did not achieve, to discover and dig into the root causes and problems that led to underperformance.

For example:

1. Comparative analysis, one by one to observe the performance of all the key factors corresponding to the KPIs for the month of 201601-2016December, comparing the month of the highest turnover performance and the month of the worst turnover performance of the key factors corresponding to the KPIs where the difference is, to be able to quickly locate what aspects and factors led to the performance of the non-attainment of the target. Then it can be targeted to drive and help business departments to improve.

2, tracking the transaction and performance to drive and improve the landing and implementation of the action program progress, there are what kind of problems, whether there is a lack of implementation of the action program to affect the performance of the target.

Step3, for these problem factors, targeted to do to improve and explore ways to improve performance.

Otherwise, the design of the commercial splendor of the visualization charts, if you can not quickly get the information and business decision-making recommendations and programs are meaningless. Visualization simply becomes a result of falsehood and deception, glamour without pragmatism. Based on the prepared answers to all these questions, start customizing your data visualization solution to meet the specific requirements of each decision maker. Data visualizations should always be customized for their audience, and such reports should include only the information that the audience needs to know, and should place that information in a context that is relevant and meaningful to them.

3. Give the data visualization a clear title.

When your report reads like a newspaper or magazine story. From that headline, it makes a strong impact on the reader. A clear headline is able to well explain the theme of the report and story, is the information that summarizes the whole report and story. Of course, operations analysts are not encouraged to be "headline geeks". A good headline is one that is neither ambiguous nor overstated, but simply explains the chart. This helps the audience to get straight to the point. This allows the reader to skim the document and get to the heart of it quickly. Try to make your title stand out.

4. Connect the data visualization to your strategy and program

If the purpose of the data visualization is to present data that solves specific, measurable, actionable, relevant, and time-sensitive problems, then add those problems in the opening. Later connect them to your strategy to clarify the positioning of the data, so readers immediately understand the relevance and value of the visualized data. Ultimately, they are better engaged and able to use the information more intelligently. Data visualization, ultimately, serves the business value of running a good business. It's hard to build infographics with linkage value if you're not focusing on the strategy and action programs of the business. For example, the action plan implemented by the enterprise is usually to reach and realize the strategic goals of the enterprise, through which lean management and lean operation can be realized. Therefore, the visualization solution should be able to achieve the driving effect of the action plan on the strategic objectives, the driving and influencing effect of individuals and teams on the overall departmental indicators and KPIs. Only by establishing a view of the information with a linkage, you will get valuable data visualization.

5. Choose your presentation charts wisely.

Regardless of the type of chart you use, bar charts, line charts, radar charts, etc., each has its own strengths and limitations. You can't find the perfect visualization chart. But you can try to mix the presentation to make the visualization a little more humane. All visualizations should convey the message as simply and precisely as possible. This means: no matter how trendy, good-looking, or flashy it is, that's not the point of designing data visualizations in the first place. It's true that we are in a constant and insatiable quest for the beauty of data. But the best balance is to use the right data visualization to illustrate the beauty of the value of the right information and knowledge.

? Use only graphics that are relevant, convey important information, and are needed by your audience.

? There's no need to fill in all the blanks on the page - too much clutter just interferes with the reception of important information and makes it too hard to remember and too easy to ignore.

? Use color appropriately to add depth of information. Also be aware that some colors have underlying meanings. For example, red is considered to be the color of warning or danger. It is suitable for warning amounts.

? Don't use too many different types of charts, tables and graphs. If you need to compare various charts and graphs, make sure that you elaborate on the data using the same kind of charts and graphs so that they can be easily compared with each other.

6. Note text where appropriate

Text helps explain the data in words and adds depth while contextualizing the chart. While numbers and tables may only provide a snapshot, captions allow one to learn more about key points, comment on them, and emphasize their meaning. Guide the viewer to think about the subject of the graphic, not the methodology, graphic design, graphic generation, or something else.

? Avoid distorting the original intent of the data.

? Make large data sets coherent and consistent.

? Engage the reader in comparing and contrasting different pieces of data, highlighting key points and strengths and weaknesses.

? Be fairly clear about the main idea: describe, mine, tabulate, and visualize self-interpretation.