With the rapid development of the Internet today, the rise of digitalization has accelerated the speed of data transmission; at the same time, the popularity of intelligent Internet of Things such as smart home appliances, wearable devices and other products has further expanded the channels for data acquisition. Not only are massive amounts of data generated in seconds on web pages, mobile phones, and computer applications, smart devices are also capturing massive amounts of information.
It can be said that data analytics among various cloud vendors has entered a new stage of development in terms of volume, transmission efficiency, elasticity and cost savings. As a valuable asset for companies, brands, and society, the value of cloud data analytics is self-evident. And data visualization greatly facilitates the visual presentation of the value of data. Today, XtraLeap will discuss with you the related concepts and some cases of cloud data analytics.
What is Cloud Data Lake Analytics
This is a composite concept, including two aspects of cloud data lake + data visualization. Cloud Data Analytics refers to a large-scale data collection that greatly exceeds the capabilities of traditional database software tools in terms of acquisition, storage, management, and analysis. It has a high degree of diversity (that is, the diversification of data types, sources, and forms). Volume and high speed (data growth is fast and time-sensitive, requiring high-standard processing and response speed) these three characteristics.
As a big data resource with great research value, if you want brand decision makers to absorb its essence quickly, accurately and ruthlessly, data visualization is also required for processing and precision, so as to provide good data endorsement for decision-making and improve decision-making efficiency and accuracy.
Data visualization is to express the original information and data in the form of graphs and charts. By using visual elements such as charts, graphs, and maps, data visualization can provide a convenient way to observe and understand the outliers, trends, laws, and even patterns inherent in the data. Therefore, in general, cloud data analytics is a process in which big data is acquired, cleaned, and analyzed, and the analysis results shown are displayed in the form of graphics, icons, etc.
The Value of Cloud Data Lake Analytics
Compared with cookie-cutter digital tables, human eyes and attention are more easily attracted by colors and patterns: for example, quickly recognize red from blue, and quickly recognize square from circles. Therefore, cloud data analytics can help us to interpret massive amounts of data more scientifically from a visual perspective, thereby triggering the viewer’s interest, and focusing the viewer’s attention on a certain point through different expressions and prominent methods, and at the same time make It obtains more valuable information that is easy to internalize and understand.
Cloud Data Lake Analytics Use Case
Cloud Data Analytics mainly requires two major steps: data analysis over massive amounts of data and the visualization of analysis results in sub-seconds to seconds response time. The primary data visualization effect may be a simple tree chart, radiation chart, histogram, fan chart, etc., to speak with data; however, what we want to see is that there is relaxation, lightness and speed. The visualization results of the, so that the viewer can grasp the desired data dimensions and key values at a glance, which requires the data to speak by choosing the appropriate performance techniques. Next, I will give you two simple examples to help you understand the data visualization effects of the two dimensions of "use data to speak" and "let data speak".
This is a simple example of how the same group of students think about science before and after participating in a project. Through the basic cleaning and analysis of the collected survey data, the percentage of students' different views on science before and after the project can be obtained. The simple method is to make the data of these two different time nodes into two pie charts: this step is actually just a basic display of the results of data analysis, and the two pie charts are to a certain extent splits the connection between the data before and after participating in the project, so when it is shown to the audience, it is necessary for the audience to think and process the data to be able to draw further conclusions.
The data before and after the implementation of the project, which can clearly allow people to observe the comparative effect of students' views on science in the two time periods. At the same time, proper text description can not only have an auxiliary effect on the graphic effect, but sometimes even can tell the key trends, outliers, rules, etc. in the data at a glance, so that the audience can get the information in the visualization data at a glance. The key point is to realize the real "let the data speak".
This is an example of the number of tickets sold in a cinema throughout the year. In fact, in the process of data visualization, the ticket sales results are displayed without even in-depth data analysis. Although there is a color classification and comparison for the two ticket statuses in different months, the audience still needs to conduct a subjective analysis and judgment on the chart, and in fact it is still using data to speak.
The more sophisticated big data visualization is to correlate and display the data in a targeted manner according to the value of the data and the purpose of analysis. It can quickly display the trend of the number of tickets sold in a year and 12 months, so that the audience can easily capture the law of the data; at the same time, the number of tickets sold and the number of employees are correlated in the data analysis process, and the trend changes abnormally Adding employee resignation data to the value position allows the entire visualization chart to explain to the audience. The influencing factors of this trend can also let the data speak, and further explore and demonstrate the value of the data.
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