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This article will explain in detail what are the common methods of big data analysis, 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.

Trend analysis is generally used for long-term tracking of core indicators. Comparative analysis, horizontal and own comparison, vertical and others (such as competitors). In quadrant analysis, each comparison subject is divided into four quadrants according to the difference of data. Cross-analysis, cross-display of the data from multiple dimensions, multi-angle combined analysis.

The update of science and technology and the rapid development of the Internet are promoting the advent of the big data era. Every day, various industries are producing an unpredictable number of data fragments. Only in a reasonable time to capture, manage, process and organize these huge databases, can we help enterprises to get the data they want, so as to better put forward management countermeasures.

Common methods of data analysis

1. Trend analysis

When there is a lot of data, and we want to find the data information faster and more convenient from the data, we need to use the power of graphics. The so-called power of graphics is to draw him with the help of EXCEl or other drawing tools.

Trend analysis is generally used for long-term tracking of core indicators, such as click-through rate, GMV, number of active users. Generally make a simple data trend chart, but just making a data trend chart is not an analysis, it must be like the above, the data has those trend changes, there is no periodicity, there is no inflection point, and analyze the reasons behind it. whether it's internal or external. The best output of trend analysis is the ratio. There are month-on-month comparison, year-on-year, fixed base ratio. For example, how much GDP increased in April 2017 than in March, this is month-on-month, which reflects the recent trend, but has a seasonal impact. In order to eliminate the seasonal effects, the year-on-year is launched, for example, how much GDP increased in April 2017 compared with April 2016, which is the same period last year. It is easier to understand the fixed base ratio, which is to fix a certain basis point, such as using the January 2017 data as the base point, while the fixed base ratio compares the May 2017 data with the January 2017 data.

2. Quadrant analysis

According to the difference of data, each comparison subject is divided into four quadrants. If IQ and EQ are divided, they can be divided into two dimensions and four quadrants, and each person has his own quadrant. Generally speaking, IQ guarantees a person's lower limit, while EQ raises a person's upper limit.

An example of quadrant analysis that has been used in practical work before. Generally speaking, registered users of P2P products are drained by third-party channels. if they can be divided into four quadrants according to the quality and quantity of traffic sources, and then select a fixed time point to compare the performance-to-price ratio of each channel, the quality can be measured by the dimension of the total amount retained. For high-quality and high-quantity channels to continue to maintain, for high-quality and low-quantity channels to expand the introduction of quantity, low-quality and low-quantity pass, low-quality and high-quantity to try to put in the strategy and requirements, such a quadrant analysis allows us to get a very intuitive and fast result in comparative analysis.

3. Comparative analysis.

Horizontal comparison: horizontal comparison is compared with yourself. The most common data indicator is to compare with the target value to answer whether we have achieved our goal or not, and to answer how much we have increased around the north compared with last month.

Vertical comparison: to put it simply, it is a comparison with others. We have to compare with our competitors to answer our share and position in the market.

Many people may say, comparative analysis also sounds very simple. Let me give you an example. There is an e-commerce check-in page. Yesterday, its pv was 5000. How do you feel when you hear such data?

You won't feel anything. If the average PV of this check-in page is 10000, it means that there was a major problem yesterday. If the average pv of the check-in page is 2000, then there was a jump yesterday. Only by comparing the data can it be meaningful.

4. Cross analysis

Comparative analysis includes both horizontal and vertical comparisons. If you want to compare horizontally and longitudinally, there is a cross analysis method. Cross-analysis is a cross-display of data from multiple dimensions and a multi-angle combined analysis.

When analyzing app data, it is usually divided into ios and Android.

The main function of cross-analysis is to subdivide the data from multiple dimensions to find the most relevant dimensions to explore the causes of data changes.

Description:

Trends, comparisons, quadrants, and crossover contain the most basic parts of data analysis. Whether it is data verification or data analysis, finding trends, making comparisons, drawing quadrants, and subdividing data can play its due role.

This is the end of the common methods of big data's analysis. I hope the above content can be helpful to you and learn more knowledge. If you think the article is good, you can share it for more people to see.

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