My 1999

"Lao Yang, please say a few words."

After Yang Tingkun smiled and nodded, his deep eyes quickly scanned the scrutinizing faces.

"Hongmeng Company is the world's top Internet technology company, and I am deeply honored to be able to join it. I hope to get along well with everyone in the future and jointly create a brilliant future for Hongmeng. Thank you."

Wow….

Xu Liang took the lead, and everyone applauded Mianzi.

After asking Yang Tingkun to take a seat.

"Everyone should be a little confused, why should I clear out Pangu's business and enter the field of database research and development? I also believe that some people will think that I have been stimulated by Larry Ellison and want to dismantle him in the database field. ?

Frankly speaking, an old guy who is almost sixty years old and has his neck buried in dirt is not worthy of my attention.

But what really deserves my attention is that Hongmeng wants to get the ticket to the mobile era and the artificial intelligence era. "

Xu Liang, who felt a little unable to let go while sitting, simply stood up.

"Why do you say that.

Let’s start from the beginning.

In the early days of the development of the Internet, it was still an era when various warlords were fighting each other and actual combat was king. There was no so-called regular army, and solving problems was the only way to go.

Of course, there were not so many problems at that time. After all, at that time, the Internet was still a new term, and people who could be called "netizens" were a rare species. Internet access was mostly a high-end luxury product that only rich families could afford. .

From a technical point of view, it was still in the early stages of the development of Web applications. The Internet technology architecture was still the most primitive monolithic architecture. The number of Internet users was very small, and one server was enough to bear the pressure of user access.

At that time, relational databases received widespread attention and application. The number of website visits was not high in concurrency, let alone the user experience. If you could afford it, it was already better than most people.

However, with the development of the Internet, the cost of accessing the Internet is getting lower and lower, and the population of Internet users continues to grow.

According to the current development speed of the domestic Internet, China's Internet population will exceed 100 million in at most three to four years.

Hundreds of millions of people search, shop, watch movies, play games and entertain online, leaving behind billions of data.

I call this huge amount of data ‘big data’.

Simply relying on "relational databases" can no longer meet the business needs of Internet companies. We need a "non-relational database" to handle extremely large amounts of data and contain a large amount of irregular data. "

As presidents of subsidiary companies and industry elites, everyone also has a certain understanding of databases.

The so-called 'relational database' means that the storage format can intuitively reflect the relationship between entities.

Relational databases are similar to common tables. There are many complex relationships between tables in relational databases.

But they don’t understand ‘non-relational databases’.

Xu Liang also saw the confusion in everyone's eyes, so he continued to explain.

“The so-called non-relational database is actually relative to the relational database. We all know that relational databases usually process some structured data, and these data usually have certain corresponding relationships.

Non-relational databases are usually used to store data whose types are not fixed and have no rules.

Enterprises generate a large amount of data every day. Non-relational databases are widely used and have many application scenarios, such as caching. "

After a pause, allowing everyone time to digest, Xu Liang continued.

“With databases to store big data, we also need ways to process big data.

Because our data is stored in data centers in various places, if we want to calculate this data, we need a computing method that can coordinate data from various places. This method is called 'distributed computing'.

Distributed computing is implemented through the Internet, which we can call the ‘cloud’.

Taken together, this method of processing big data can be called ‘cloud computing’. "

As for the remaining theories and methods such as utility computing, load balancing, parallel computing, network storage, hot backup redundancy and virtualization, Xu Liang did not say much.

Learn to walk first, then think about running.

Everything must be done step by step.

“Foreign companies currently researching non-relational databases have just started, and Hongmeng cannot fall behind.

There are only some half-baked research projects on big data and cloud computing abroad, and there is no mature business model. We at Hongmeng must produce results as soon as possible and seize the market before other companies can react. "

From Xu Liang’s words, everyone could tell that he attached great importance to big data and cloud computing.

"Mr. Xu, I have a question." Wu Xifan asked.

"you say."

“How can big data and cloud computing be profitable?”

Xu Liang nodded slightly, "Let's talk about big data first.

I have concluded that there are roughly four major profit models.

First, the solution.

The main model of big data solutions is: I will build a big data system for you, and then maintain and upgrade this system for you every year and every month.

The fee is charged as follows: the cost of building and deploying the big data system + the annual maintenance/upgrade service fee.

So which companies need solutions from the big data industry?

One is government enterprises and institutions.

For example, tax bureau, public transportation system, health system, air defense system, public transportation system, etc.

The second is traditional industries.

Clothing, food, housing, transportation, medical care, education, retail, communications, aviation, industry, manufacturing, sports, entertainment, lottery, film and television, catering, tourism, real estate, etc.

These industries have three important characteristics.

First, they do not have big data technology capabilities.

Second, they don’t have big data talents.

Third, they hope to realize Internet+ through big data and transform the current situation of the industry through big data.

It is foreseeable that this will be the place where the big data industry will have the most lucrative jobs and the most "fat" jobs in the future. It will also be the place where the competition among big data companies will be fiercest in the future.

Second, infrastructure.

I include databases, data sources, data cleaning, data processing tools, big data engines, big data software and hardware integrated machines, etc., all into the infrastructure.

The main profit model of infrastructure is: I help you solve some problems in big data deployment.

This mode is a bit like the "build machine" mode of desktop computers. Use this company's CPU, use other companies' memory, and match your own keyboard and mouse, etc.

This model requires enterprises to have big data capabilities and talents.

You can freely combine big data infrastructure to build a big data system that is more suitable for your business.

Fee collection method: Charges are based on different facilities. You can buy out, or pay on demand, monthly, annually, or by volume, which is more convenient and flexible.

Third, data tools/product services.

Typical models include intelligence mining, public opinion analysis, sales tracking, precision marketing, personalized recommendations, visualization, website/APP analysis tools, etc.

Fee collection method: purchase on demand, some functional services are free, and some functional services are charged.

Fourth, industry application.

This module may conflict with the solution, but the industry applications mentioned here mainly talk about the new effects produced by traditional industries plus big data.

Big data can be applied to traditional industries such as medical care, education, retail, communications, aviation, industry, and manufacturing.

When big data collides with these industries, new businesses will be created.

Main mode: Use big data to gain industry insights and achieve more benefits.

For example, big data + medical treatment is a smart medical system, big data + manufacturing is equal to Industry 4.0, and big data + movies is equal to box office prediction.

Fee collection model: There is no direct realization, but greater value is generated through big data, which saves costs, optimizes the original industry, and derives new business models.

Typical examples of industry applications include: box office prediction, business district location selection, college entrance examination prediction, smart cities, drones, robots, driverless cars, etc. "

The more Xu Liang talked, the more excited he became. During this time, because of writing a book, he has been recalling the memories of his previous life and summarizing big data and cloud computing.

As a successful entrepreneur.

His exposure to industry information is far beyond that of ordinary people.

Especially after the big model of education came out, smart blackboards and smart teaching have become major trends in the industry, and he has learned a lot about them.

This will come in handy.

“Here, I specifically single out financial big data, because financial big data has the most promising prospects and is also sustainable.

The financial industry will continue to generate data, and data can be used repeatedly.

The application of big data in finance is mainly reflected in credit reporting, microfinance, P2P, electronic credit cards, quantitative investment, anti-fraud, Internet finance, etc.

Banking, insurance, securities and other industries currently rely on the insight capabilities of big data.

The financial industry needs data the most and can best monetize big data. "

“The above are the four major models of big data monetization that I have summarized.”

Xu Liang, who was talking endlessly, stopped and licked his lips. He felt that he had summarized it quite comprehensively.

Just as he was about to continue talking about cloud computing, he suddenly noticed a strange atmosphere in the conference room.

Take a glance at it subconsciously.

But when they saw everyone, except for Yang Tingkun who looked excited, Yu Jun and Xie Wen who were thoughtful, and Sun Mingzhen who was participating in the video conference with a pretty face full of admiration, everyone else frowned and thought hard.

Xu Liang understood instantly.

I said too much and was too ahead of my time.

Now I don’t even have an accurate concept of big data, and I have already explained the profit model clearly.

Before I even learned to walk, I had already started to fly.

As someone who is hearing about this new concept for the first time, of course I am confused.

I originally planned to talk about "cloud computing", but suddenly I lost interest.

Even if you say too much, they won’t understand. It’s just a waste of saliva.

Let’s do research and development first. Follow the progress of research and development and popularize it bit by bit. It will be easier for people to accept than talking about it dryly now.