[China's intelligent intelligence network market analysis] In the various predictions of the results of this year's US presidential election, "Yiwu made the flag to predict the results of the US election" suddenly emerged, let people see the magical power of big data.
From the figures, "Nuggets" big data analysis or a good helper
Right now, there are all kinds of big data around our lives, but many people still have a little knowledge of big data. Some people even think that big data analysis is a technical course for students in computer science and statistics. If students study business big data, they are definitely not as good as they are. Is this really the case?
The data itself is neutral <br> <br> on hand if there is a flood of credit card data, can be used to do? The general idea is that the data can be used to rate the credit of the consumer and determine whether to increase their credit limit. But have you ever thought that you can use these data to make stocks to make money?
This happened to the First Capital Investment International Group: Two analysts used the company's credit card consumption data to analyze the sales of at least 170 listed retail companies and forecast their sales. Then, they buy call or put options before these listed companies publish quarterly financial statements, and then get huge profits. In three years, their investment returns on the stock market reached an astonishing 1800%. Of course, this practice is illegal and it is a transaction that uses internal information and is eventually caught by the US Securities and Exchange Commission.
Interestingly, the US Securities and Exchange Commission also uses this to identify illegal transactions. The analytical methods they use are: extraordinary income recognition, seeing whether the investor's return on investment is much higher than that of investors using similar investment strategies. Link analysis, find a social circle from the phone call record, see if the abnormal investment income is related to the flow of information in the social circle; you can also find a small circle from the transaction record, such as whether the sale is carried out in a small group, collusion Stir high stock prices. Correlation analysis, find out the insider trading conspirators or head accounts through the correlation of trading behavior. Behavior analysis, to see whether the investor's trading behavior has changed abnormally, or whether it is inconsistent with the investment experience, such as the account registered by the rookie, the operation behavior is very old, the stop-loss risk control knows everything, and so on.
These examples show that big data itself is neutral and the key is how to use it. If there is a lack of business insight, big data may be just a bunch of numbers.
Business insight is "art"
If big data analysis is art, then data analysis technology is "skill" and business insight is "art". Good business insight can help companies go beyond the technical limitations of big data analytics.
The insurance industry is an industry that heavily uses big data. But what if someone falsifies information and deliberately creates a car accident to swindle? It can be dealt with by adding data and building a fraud identification model, or it can be identified by manual investigation, but it costs a lot of money.
A startup in Germany solves this problem that is difficult to solve with big data analysis by means of business model innovation. In this business model called P2P Insurance, policyholders send invitations to friends and relatives to establish insurance mutual assistance relationships, pay premiums together and participate in the insurance mutual help network. If the insurance product does not expire when it expires, the consumer can receive up to 40% of the premium refund. In the case of a small amount of payment, the pool of funds paid by relatives and friends will be paid. The payment beyond this fund pool is borne by the company.
This business model solves the problem of fraud prevention that is difficult to solve with big data analysis. Because the mutual understanding between friends and relatives is not willing to tie themselves together with the scammers, so the scammer can't find friends and relatives to insure him. At the same time, insurance fraud is not easy to be discovered by insurance companies, but it is easy to be seen by relatives and friends, and deceiving relatives and friends is more ethical than defrauding insurance companies. In addition, the small loss of their own for the relatives and friends to undertake, usually more embarrassing than the insurance company to serve, so people will not report damage.
Two common mistakes <br> <br> result in data mining process, there are two common mistakes that require attention.
First, misunderstanding that correlation represents causality, in fact, the two cannot be equal. Managers should continue to dig into causality based on data mining results in order to find more valuable business insights. For example, it is found that the user conversion rate from the mobile terminal is significantly higher than that from the computer side, and it is not the only way to increase the advertising on the mobile side. Continue to dig deep into the causal relationship of this phenomenon, you may find that users from the mobile end are actually the company's old customers, originally like the company's products, so the conversion rate is high. The users from the computer side are basically new customers, so it may be better to advertise on the computer side. Therefore, using relevance to help business decisions needs to be checked from time to time.
Second, mistakenly believe that data-based predictions are stationary. Business development is often not continuous, especially in the case of disruptive innovation. For example, a traditional taxi company can predict passenger traffic under some steady trend assumptions and decide how many new cars should be purchased. However, when network vehicles such as Didi are entering the market, these previous models and predictions will fail. Therefore, the analysis of historical data cannot be relied too much, especially in strategic decision making, where business insight plays a greater role.
In short, in the era of easy access to massive data, big data analysis is an important tool to help companies make decisions, but the manager's business insight and wisdom are still indispensable factors.
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