There is one industry – the data analytics industry. In foreign data analysis has long been applied in various fields, many countries have set up corresponding industry organizations and management institutions, and have professional data analysis personnel. In China, the rise of the data analysis industry has been more than ten years, and this industry has both joys and worries, or often reports good news but not worries.
What everyone understands is that the data itself does not lie, it indicates a state, but the person who controls the data will lie, the lie will be generated, and the truth will be covered up. Real data helps us to recognize the world; and false data blinds our eyes, and the harm is self-evident.
Therefore, the hidden problems in the data are more worthy of our attention, even if it is real data, you need to think with your head, coincidentally, real data may also convey false information.
The source and collection method of data is no wonder that investigation, statistics, analysis, etc., and then come to a "conclusion"; and then use this "conclusion" to derive another "conclusion"; in this process, we often ignore a problem called relevance and logic.
The data analytics industry often confuses us in several ways:
First, the use of vertical start and end point statistics, such as: span ten years, the first three years is a chaotic stage, the data is abnormal; the last seven years is the normal development stage, the data is the norm, is the valuable data.
Second, the use of percentages, such as: the network frequent family income class and the percentage of the population, after reading it may find their own class is quite embarrassing; other example: manufacturing capacity, the percentage of technological development, often produce a wonderful feeling, of course, this is an illusion, because it does not highlight the percentage of core technology.
Third, using horizontal comparisons, such as: how much the average income rises, many people begin to explode foul mouth, X, and are averaged; such data actually does not have much meaning.
Fourth, size change, stealing the concept of change, such as: The Chinese people have to buy 800 million shirts to change a Boeing plane, if you change the term, a ship of shirts can change an aircraft composed of hundreds of thousands of parts; the two statements give people a very different feeling; the former statement often causes negative bad emotions and can not solve any practical problems.
Therefore, in the era of information flooding, when a set of data comes into our eyes and attracts our attention, we need to doubt and verify its authenticity, do deeper excavation, insight into its essence, and then use it as evidence for strategic decision-making, so as not to go astray.