Big data should enhance its competitive advantage and realize value redemption

If one day can predict the future, then what are your first things to do? Buy lottery? That second, third thing? Selling a first off, we talk about it later.

Big data is an industry, broadly speaking refers to the whole product chain of mass information generation, dissemination, collection, processing and value creation in the era of information overload. In the narrow sense, it refers to the related industries of big data storage and processing and data mining. The most common use of big data on the market today is in analysis and prediction.

According to my 10 years of experience in this industry, big data and the previous two years of cloud computing, grid computing forward and parallel computing are several links in the same industry chain, it is as a concept by the media and practitioners Hype up. However, it is undeniable that our society is in an era of information explosion and the amount of information in all industries is increasing geometrically. Effective utilization of these data can indeed bring about tremendous changes in work and life.

Point 1: The data itself does not produce value, how to analyze and make use of big data to help the actual business is the key

For example, it can help control the U.S. healthcare spending of 17.6% of GDP, bringing over $ 300 billion in cost savings and revenue to the pharmaceutical industry (McKinsey's report)

The Story of Netflix Producing "House of Cards" Many people have heard that this company uses big data to analyze users 'favorite episodes, favorite actors' compositions, favorite writers and so on, and combine them to create their own A television show eventually achieved great success.

Although many people have heard the story, but almost no one noticed that the company is at the top of the big data application chain. Unlike midstream companies in the underlying industry that provide analytics services, big data storage, and data cleansing, Netflix, in addition to generating its own data and analytics capabilities, has the key to having the ability to translate the results of analytics into products, The core of profit and competitiveness.

Point 2: Business engaged in big data should pay attention to input and output

Big data threshold is very low, with an Excel can start, but with further research, want to use big data analysis for profit, still need some budget and investment.

For example, professional teams such as big data analysts are required. This position existed 10 years ago and called BI (Business Intelligence). The job was to analyze a large amount of data and help to formulate strategies or make business decisions through modeling.

With an analyst, you need a team of engineers to dig out valuable things from the vast amounts of data.

Server: Big Data Another resource to consume is the server, which requires continuous input from storage to compute to bandwidth.

Therefore, before entering the industry commercial companies have to consider whether there is enough budget, but at the same time the above also gave birth to many new opportunities, such as amazon is the world's largest cloud computing infrastructure vendors, splunk and the previous listing of Tableau All of them provide data processing services to analysts, which is equivalent to replacing some expensive engineers.

Opinion 3: Big data is not a recent addition, data persists, and technology for data analysis has revolutionized in recent years

Handling massive data has been a topic in the technology world. Several revolutionary technologies have emerged in the last 10 years, laying the foundation for our current big data, including virtualization technology, Map-Reduce & Bigtable, NoSQL database, Deep Learning Wait.

Virtualization created today's Amazon cloud services infrastructure, and map reduce created the hadoop open-source software that helped us make high-speed cloud computing, which can be done in a few minutes for a few days. NoSQL database has been widely used in the website with a large amount of data and high traffic, the performance has improved a lot than the traditional database.

Point 4: Many people have been silently monetizing big data

Through long-term research, business companies find themselves in the best of the past when they discover the secrets of making money from big data. Because the analysis methods and data sources once open, competitors will inevitably follow up, will lead to the method of homogenization ultimately reflected in the reduced earnings.

Precision marketing is a well-known area where different types of marketing are done by categorizing each individual's information. For example, search engines, you search for some new long-term real estate information, search engine based on your search history to determine your most likely potential buyers, the United States target department store once because of the user's shopping record to determine a girl's pregnancy And to its home delivery pregnant women shopping manual and reputation. After searching the travel backpack in Taobao, you can see ads related to travel goods on Sina.

However, in fact, big data companies are not content with this kind of direct contact data in order to enhance their competitive advantages and have more ways to collect data. According to the author's knowledge of these years, under the premise of protecting the industry's secrets, share some dry goods that readers have not heard before.

1, the router, before the Internet is a small box in the data procurement market is extremely hot channel. The reason is that in recent years, with the development of mobile phone hardware (mobile devices such as routers and mobile phones use similar chips), especially the geometric growth of processor computing power, a lot of programs can be run on a small router, and these programs are used by users When the Internet silently analyze a variety of data, including your friends often contact information, Internet records.

2, network operators, operators like to insert some ads when users browse the web, I believe most people have encountered, and the router data analysis principle, operators are not satisfied with the stereotyped fixed display ads, but also with the time Advancing, the use of big data for accurate personalized advertising and marketing.

3, basic software, such as browser, input method. Not only the computer also includes mobile phones, you use the input method submitted in various software queries, in the browser mouse to a product but did not click and so on, these big data will be stored in the cloud for manufacturers analysis.

4, financial companies.

Mention the financial industry when it comes to revolutionary technology. The big data cost issue we mentioned earlier in this article is not a problem in the financial industry, as the benefits of new technologies in this industry are much higher than the cost, so we can see that every new technology is often The first time in the financial industry. Big data is no exception, in fact, many years ago, data mining of big data has been widely applied to the financial sector. Here we have to gamble industry also classified as financial industry.

Until this year the United States has only sporadic applications, "mall flow monitoring system" in fact, many years ago has been applied to Macau and Las Vegas casinos in the United States, a casino installed thousands of cameras, starting from the customer door It started to track the face recognition technology, combined with each person's transaction data mining, in addition to find the old and other unwelcome people, but also pick big clients, to encourage irrational gamblers to increase the stake and other ways to get more More profit.

The stock market is another big data market besides the gambling industry. Decades ago the stock market was a market that made use of asymmetric information. Nowadays big data analysis has become a new generation of effective tools. The stock market has massive transaction information every moment, big data analysis technology has been accompanied by the securities industry development and growth.

Many previous big data articles mentioned that Indiana University researchers found that the stock market's ups and downs could be accurately predicted by analyzing people's emotions in the twitter message, but hedge funds trading on twitter on Google search can only find London's DCM a. The reason As mentioned in the previous paragraph, commercial companies, especially hedge funds, will not easily expose their own operational logic. This is the same logic as the "dark forest" in the "three bodies."

But in fact, we can find through various clues twitter information has been widely used by hedge funds in the market. For example, as long as Hollywood actress Hathaway appeared in the headline, shares of A-share of Warren Buffett's company rose. The reason is simple, the actress Hathaway's name is Anne Hathaway, Buffett's company is called Berkshire Hathaway (Berkshire Hathaway), both include the word Hathaway, indicating that many hedge funds have used real-time Analysis of twitter and news big data technology.

Another example: a fake news release on twitter: Two bombings in the White House and the death of President Barack Obama in the wake of the incident led to a drop in the relevant markets including the stock market and the euro for the first time. The Dow fell by two minutes in two minutes. More points, the euro is also a strong downward, the US stock market briefly evaporate about 1,400 billion US dollars. An accidental hacker prank, in turn, has led many to use twitter data hedge funds have been exposed.

Cite another example that occurred around me, we all know to the brokerage account why fill in a detailed form? You will be asked to fill in your income, investment experience, etc., because in Wall Street, there are very mature model can be filled out by you The table predicts your future earnings, while analyzing a large number of transaction records for different types of users for marketing and services, the same goal is to increase profits.

Another big area of ​​the financial sector is the credit markets, which have also been unusually hot lately, with companies such as Alibaba already entering. In addition to the new P2P industry in the business model, efficiency, user sources and other traditional banks are different, one of the most crucial factor is the big data. Big data can help solve the core problem of bad debt in the credit industry. Alibaba makes it very easy for users to conduct credit evaluation through the complete structured data of users' transaction data and favorable ratings on its platform. However, other platforms lack the data advantage of Alibaba and need more data mining to reduce the bad debt ratio As a result, the largest foreign P2P companies such as Lending Club made its first profit-making after six years of continuous improvement. This shows the great challenges we face in this huge market.