Where does big data entrepreneurial data come from?

Big Data Entrepreneurship: Where does the data come from? How many obstacles do you need to cross?

This article has been considered for a long time without writing. On the one hand, I want to write some dry goods, on the other hand, I want to write something fascinating. Finally, I decided to write from the perspective of a neutral user and try my best to make it popular.

20 13 May 13, in the 10th anniversary party of Taobao-Ma Yun's retirement speech, Ma Yun said: This is a changing era. Some people still don't understand PC, and the mobile Internet is coming. I haven't figured out the mobile internet yet, and big data is coming. The era of change is the era of young people.

What Ma Yun said is crucial. He not only mentioned big data, but also explained in one sentence that the Internet evolved from the PC era to the mobile Internet era, and then advanced from the mobile Internet era to the big data era. There are several key points that are very important: in the PC era, a large number of Internet listed companies emerged around the world, including Google, Amazon, Sina, Sohu, New Oriental and so on;

In the era of mobile Internet, the entrepreneurial boom in China is booming. Not only have a large number of mobile internet (including mobile games) enterprises listed in the United States, but also countless entrepreneurial miracles have been born. The mobile Internet has not only brought convenience to our life, but also pushed the entrepreneurial craze to the highest peak in history.

Now the problem is coming. In the era of big data, should the entrepreneurial boom be more lively than the era of mobile Internet? How to start a business in the era of big data? What are the thresholds for big data entrepreneurship?

Answer the first question first: In the era of big data, should the entrepreneurial craze be more lively than the era of mobile Internet?

Not that I know of. Walking on Zhongguancun Venture Street, 99 of the 100 financing BP you can get may be APP and O2O projects, but more than 90% of these 99 companies will attach importance to big data.

So how to start a business in the era of big data? Please understand the entrepreneurial threshold of big data first.

Threshold 1: data big data big data, how to play without data? So where does the data come from?

BAT companies like Baidu, Tencent and Alibaba have accumulated a lot of data themselves, so playing big data is mostly "making a fortune". Of course, you can also say that several examples of BAT companies playing big data, such as Baidu Migration, Baidu Actuarial, Baidu Public Opinion and Baidu Big Data Prediction Engine, are all big data product applications of Baidu; In Alibaba's words, Alibaba Cloud, Alipay-Bai Hua, Alipay-Borrow, Sesame Credit, Ant Financial and so on should all have big data technology. As for Tencent, "Tencent Broadcom" and "Tencent Cloud Analysis", WeChat also cited big data technology.

How to play diaosi without data?

First, you can purchase data through a third party. For example, the data museum has a lot of data to sell and share.

Secondly, you can use a crawler to crawl back some data to store;

Furthermore, by authorizing enterprises, developers, webmasters, etc. to use big data tools to accumulate data. Start-ups in this area include Talkingdata, Youmeng, DataEye and so on.

Finally, use free government, enterprises and institutions to open data. For example, the API interface of Gaud data, the API interface of Weibo commercial data and so on.

In general, solving data sources is a necessary threshold for big data entrepreneurship. The key depends on what project you start.

Threshold 2: Hardware is in Beijing. I once visited a big data startup company, and they didn't get financing at that time. I went to their office area and found a particularly sad scene. Their employees work in a small room, while two larger rooms are used to install the expanded data storage server. The storage capacity of big data is amazing, which also poses new challenges to the computer room and hardware equipment.

This is not the same as the mobile Internet. You make an APP, develop it with a computer, use a cloud server as a server, and buy it on demand. But big data can't do it. You can't store your data on someone else's cloud server. On the one hand, there are security factors, on the other hand, there are also property rights factors.

Hardware is also one of the thresholds for big data entrepreneurship, but it is not the biggest threshold. By the way, the big data startup I visited has completed a million-dollar series A financing, and now their office area is particularly spacious. Congratulations on the map data.

Threshold 3: Talent I think the biggest threshold for big data entrepreneurship lies in talent. Unlike being an APP, big data entrepreneurship can't be played by one person, or even by several people. For a startup, you should start with a team of 10- 15 people. This team should include Hadoop engineers, algorithm engineer, data modeling engineers, architects, NoSQL engineers, BI engineers, etc. All of them have high technical requirements and high salary requirements.

How expensive are big data talents? In the United States, the salaries of professionals in R, NoSQL and MapReduce have reached about 1 15000 dollars per year, which is not much cheaper in China. Without an annual salary of 300,000, it is difficult to recruit a big data talent.

In other words, a big data talent with great technology has a wide range of choices, either having entered the BAT enterprise or getting a high salary in a good enterprise. You have to tap such talents, except money, stocks, options, benefits and so on. These are the prices that must be paid.

20 15 -20 16 is the most scarce two years for big data talents. There is a simple reason. Major universities have just opened big data disciplines, and students have not graduated yet. The demand for big data talents in the recruitment market is far in short supply. In addition to BAT, communication companies, power companies, financial banking, medical industry, industry, game industry and so on. Which industries are not all recruiting big data talents? For startups to find the right big data technology talents in such a severe talent environment, the threshold can be more than money.

Threshold 4: Technology talks about talents, which refers to technology. Big data technology is not enough for you to know C++ or R language. Big data has its own technical system, including statistics, programming, JAVA, database, Hadoop, Spark, NoSQL, machine learning, natural language processing, algorithms, data visualization and other technologies. Hadoop alone requires many technologies and programming languages.

Moreover, the big data tools on the market are different from household to household, and the technologies required to use open source software (such as Hadoop and Spark) or SAP(SAP HANA) are also different. High technical requirements and few talents who master the comprehensive technology of big data have become the biggest problems that restrict big data entrepreneurship.

Threshold 5: Money Actually, I don't want to write money, but I have to write money. There is no shortage of funds for entrepreneurship in the big data industry. As long as the business model of your entrepreneurial project is fine, your technical ability is strong and your team is reliable, you can enter the A round no matter in China or the United States, and the capital concern is very hot. But before you get financing, you need a lot of money to start it yourself. Talent, hardware and technology are costly.

Let's put it this way. If a few good friends can spend 500,000 yuan on an APP project within three months, if you want to start a business in the big data industry, please prepare 6-8 million yuan before playing.

Threshold 6: Business Model What is the most profitable industry on the Internet in China? I think it is e-commerce and online games. E-commerce and online games are also the fastest growing industries on the Internet. And big data, its liquidity is not as simple and direct as online games and e-commerce. In many enterprises I have been to, they have money, data, talents and technology, but they don't know what their data can do.

In other words, there is no clearest and most direct business model for big data. Big data can only generate value if it is combined with business scenarios.

Big data is like petroleum crude oil. You know where it is, you can mine it, but after mining, you still have to go through smelting, vacuum distillation, hydrofining, solvent refining, solvent dewaxing and other refining processes to become refined oil, and then transport it to various gas stations, so that cars can be filled with oil to generate electricity and realize the final value. The same is true for big data, which requires a complex process to realize business value.

Then you may ask, is big data trading a business model? Personally, I think it depends on what the transaction is. For the original unstructured data, it takes too many processes to clean up the data later, and the data storage is also very expensive, and the transaction cost is too high. I believe that both enterprise users and individual users are more inclined to buy big data sources that can be used as they are.

You said that JD.COM and Tencent completed the first big data transaction. I think this is a joke. Didn't the big data of JD.COM and Tencent have been integrated long ago? I can use WeChat to shop in JD.COM directly, and the data can be exchanged. Why should I trade?

Therefore, the most difficult thing for big data entrepreneurship lies in the thinking of business model. If you don't find a way to realize big data, then don't be busy pulling the team to start a business. Starting a business in the big data industry is not enough. It is the key to the whole business model.

The above is what Bian Xiao shared with you about the origin of big data entrepreneurship data. For more information, you can pay attention to Global Ivy and share more dry goods.