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This article will explain in detail what are the ten starting points for realizing big data's visualization. The content of the article is of high quality, so the editor will share it with you for reference. I hope you will have a certain understanding of the relevant knowledge after reading this article.

To realize big data's visualization, we need to consider users, tell coherent stories, iterative design, personalize everything, start with analytical goals, consider management, empathy with viewers, understand business, connect visualization, and simplify as much as possible. in order to solve the hypothetical problem at hand.

First, consider users

Dan Gastineau, director of visual analysis practice at Aspirent, a management consultancy, says companies should use colors, shapes, sizes and layouts to show visual design and use.

Aspirent uses colors to highlight analytical aspects that you want users to focus on. Size can effectively indicate quantity, but too much use of different sizes to convey information can lead to confusion. Size should be used selectively here, that is, where members of the consulting team want to emphasize. In addition, the form determines the shape in which the analysis is rendered: for example, whether to use lines or bar charts to render certain types of information. According to Gastineau, the placement of objects is as important as the objects themselves and facilitates effective communication.

Second, tell a coherent story

Communicate with your audience and keep the design simple and focused. Details such as colors to the number of charts can help ensure that the dashboard tells a coherent story. "the dashboard is like a book," said Saurabh Abhyankar, senior vice president of product management at MicroStrategy. "it needs to consider the design elements of the reader, not just force the list of all accessible data." The design of the dashboard will be a factor driving deployment.

Third, iterative design

You should constantly get feedback from visual analysis users. Over time, data exploration leads to new ideas and problems, while increasing data relevance over time and deployment makes users smarter.

Solicit and get feedback from your audience can improve the experience. Nick Mihailovski, chief product manager of Google's Cloud data Studio, said that building concepts quickly, getting feedback quickly and iterating can lead to faster and better results. In addition, surveys and forms can be integrated into exquisite reports, which can also help ensure that big data's visual results do help the target audience.

Fourth, personalize everything

Ensure that the dashboard displays personalized information to the end user and that it is relevant. Also, you should ensure that visualization is designed to reflect the device on which it is located, and provide offline access to end users, which will make visualization go further. Mihailovski said that carefully designed interactive visualization to attract audiences and spread the data culture can make the analysis attractive and fun. Employees can also visually access, visualize, and share reports that contain real-time dynamic data.

5. start with the analysis of the goal.

You should ensure that the data type and analysis target reflect the selected visualization type. "people usually take the opposite approach," says Mihailovski. "they first see clean or fuzzy visualization types, and then try to match their data." For the visualization of big data projects, simple tables or bar charts may sometimes be the most effective.

VI. Consider management

It may take time and effort, but the important thing is that the end user trusts the data. Get all the help you need from a technical, process, and personnel perspective to ensure that the data is reviewed and accurate.

7. Empathy for the viewer

Different visualization methods are used in each case. For example, many data visualization experts explicitly prohibit the use of pie charts because the human eye and mind can more easily measure differences in length or position, and it is difficult to identify angle differences. After turning the pie chart into a bar chart, the differences between different parts become less obvious, and it is more difficult to identify some small parts. Here you can consider the double-layer circle chart, which is equivalent to removing the pie chart in the middle area, and can quickly display 75%, 20%, and 5% modes.

8. Understand the business

Take the time to communicate with business users about what they want to achieve from the visualization of big data's products and what data they need to provide the required insights. If necessary, tools or techniques can be purchased to analyze and transform the data. Naresh Agarwal, head of data and analysis at Brillio, a technology consultancy, said: "in big data's field, we are dealing with huge amounts of data, so it is very important for users to benefit from this scale of data.

At the same time, it is important to understand business trends to help users adopt the latest metrics and analyses to drive better business decisions. When conceiving different dashboards, you should always consider the end user. Management, analysts, IT, and business users will derive value from different types of visual analysis explorations.

IX. Connection Visualization

Make sure that the different visualizations of the dashboard are connected and can be linked quickly to show the full view. Pratik Jain, a technical architect at Kyvos Insights, a business intelligence software provider, says, for example, that if you are analyzing a summary of sales by location, you should also be able to analyze or compare the sales of different products year by year.

Should ensure that big data's visualization can be updated and queried in real time. Zachary Jarvinen, senior analyst product marketing manager at supply chain software provider OpenText, says static displays or displays without underlying data sources will not help companies analyze fast-changing large data streams.

Simplify as much as possible

Most of the leading big data visualization tools are very feature-rich, which usually leads analysts to build intensive and overly complex visualization, which can make it difficult to collect viable insights. Bajaj says good analysts should simplify visualization as much as possible to solve the hypothetical problems at hand. Then, analysts should communicate with stakeholders as soon as possible to ensure that the final product is not a cool-looking product that does not directly meet the needs of stakeholders.

Most people are still skeptical of big data, and only a few companies are willing to invest a lot of resources. Due to the emergence of the Internet and smartphones, it is possible to collect a large amount of user behavior data. This gives many Internet and online marketing companies a first-mover advantage. The emergence of in-depth study of big data and the wide application of video and language recognition have greatly promoted the application of big data technology in financial technology, health care, smart city and other fields. Data has become a key resource for companies and governments seeking innovation, but concerns about data security and customer privacy are beginning to surface.

Data governance becomes critical. At the same time, at this stage, the complexity of the data source makes people realize that in order to maintain the update and high quality of the data, the need for an appropriate management system and the negative impact of a large amount of investment in data middle-tier technology and artificial intelligence have become the focus of attention. Potential unemployment and unfair competition are some of the problems that may need to be addressed.

On the realization of big data visualization of the ten starting points are shared here, I hope that the above content can be of some help to you, can learn more knowledge. If you think the article is good, you can share it for more people to see.

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