Recognize the reality of the pain points and difficulties of China's big data industry

Recognize the reality of the pain points and difficulties of China's big data industry

Big data, as a new industry, has been at the tip of the public opinion. Just like the concept of Internet +, Big Data has been mythologized and sent to the altar of "religion". Big data companies always have a fear that big data is too high, the future may be a very bad fall.

The heat of China's big data industry in 2015 began with the Guiyang Big Data Exchange, and peaked in September with the State Council's 2015 No. 50 "Outline of Action for the Promotion of Big Data Development," and I believe that big data will still be a big hot spot at the Internet conference in Wuzhen in October.

Data products and solutions were introduced a lot at the Big Data Forum. The specific value that data brings to enterprises, data application scenarios, and pain points of the big data industry are introduced very little. China's big data industry is experiencing a lot of pain, the prospect of big data industry is very good, but it is difficult for big data enterprises to become bigger and realize a qualitative leap. The pain points and difficulties of China's big data industry are as follows.

1 Big data enterprises are numerous and weak, and it is difficult to realize industrial advantages

China's big data enterprises are about 200, nearly 60% of which are concentrated in Beijing, and are mainly small and micro enterprises, with annual sales reaching one billion RMB almost none. The big data industry is in the early Spring and Autumn period, the various lords and lords, each occupying a small niche, it is difficult to grow, are facing fierce competition from peers, some areas such as public opinion monitoring has become a red sea.

The number of big data companies is mostly in the tens to hundreds, and there are few companies with more than a thousand employees. No big data company can dominate an industry, no company has a 10% share of a niche market, and no big data company has established industry standards and led the industry.

China's big data industry is in an extremely decentralized state, with excellent talents distributed in different enterprises, making it difficult to form a synergy of talents. The small scale of each enterprise makes it difficult to do deep and big work in the enterprise, and it is difficult to use big data to help enterprises realize business improvement. Most of the enterprise tools and data is difficult to meet the overall enterprise data requirements, China's data mining and analytics products are also difficult to compete with foreign products.

Big data industry must need a group of leading enterprises if it is to form industrial advantages. With reference to foreign big data industry, China needs to produce a number of benchmark enterprises in big data infrastructure, data products, data tools, data cleaning and data mining, data analysis, data talent. The size of each leading enterprise should be more than 1,000 people, sales should be more than 10 billion, otherwise it is difficult to form a technology and talent advantage, and it is difficult to use big data to help customers achieve business improvement.

The Guiyang Big Data Exchange's 2015 White Paper on Big Data Trading in China mentions that the size of China's big data market in 2014 was 76.7 billion yuan. This number looks good, and it is estimated that less than 20% is actually related to big data tools and big data products (business value enhancement). Most of the funding goes to big data infrastructure platforms (storage and computing), consulting, reporting, and other areas that are less relevant to business value enhancement. Most of China's big data market sales are concentrated in traditional IT companies such as IBM, Oracle, EMC, Intel, Huawei, Lenovo, etc. The market share of all the real big data companies may add up to about 10 billion yuan.

China's big data enterprise scale is too small, the lack of leading enterprises, the industry is too fragmented, these are the factors restricting the development of China's big data industry, but also a pain point for the industry to grow bigger.

2 External data is an island, low data value

Data is the basis for the development of the big data industry, and data with commercial value can help companies gain insight into customers, digital operations, risk management, precision marketing, forecasting and decision-making. Data with commercial value and business analytics can really help companies improve their business and create new value.

China's big data market is still immature, and the data that many big data companies embrace is fragmented, making it difficult to form complete, commercially valuable data. There is a big gap between the quality of data in the big data market and the data needs of enterprises. External data is mostly in an island state, with little flow and integration between data; isolated, immobile, and unintegrated data is difficult to help enterprises, and many enterprises in need of data have to purchase data from multiple big data enterprises, which is inefficient, with little value of the purchased data, greater difficulty in data integration, and high overall cost of data procurement.

Everyone sees the drawbacks of decentralized data, so many places have set up big data trading markets to help people trade data and procure data. Due to the lack of legal protection, many companies are less inclined to trade data in the trading market, and often still use one-on-one data transactions, which protects the interests of both parties to the transaction. Data with commercial value is still under development, and the big data trading market, lacks a large amount of data that can be traded. Big data trading market this business model, still need to use a long time to prove.

China's best-quality data are in the financial industry, BAT, and telecom operators, which are more cautious and difficult to export data to the outside world. The main business of these three industries themselves is also not in data, and their desire to produce and export data products is not strong. The government is gradually opening up its data, but there are many challenges in terms of data quality, concentration, and output methods. It will take at least 3 years for large-scale data opening in China to reach commercial application requirements.

3 Most enterprise customers have low sensitivity to commercial applications of data

Most enterprises have a need for data, but their commercial sensitivity to data is very low. There is little understanding of data business application scenarios and data technologies. Even for banks with high data business sensitivity, it takes at least three times of communication before they can establish the concept of data value. Other industries, such as manufacturing, real estate, and retail, have even lower data business sensitivity. Even Vanke's Wang Shi shouted, do not talk about big data applications with the real estate industry, the real estate industry data is still incomplete, a lot of manual data. So a leading e-commerce began to help Vanke data planning and construction, research on the application of big data in the real estate industry.

Already in the business case of big data companies, most of them are big data companies take the initiative to find customers to talk about cooperation, to provide data products, data tools or data technology for the enterprise, the purpose is to help the enterprise to improve business. But this business model is very tired, the market is difficult to be triggered, passive data business applications, often weak and business combination, can not quickly help enterprises to use data to improve business, but also can not solve the business development bottlenecks.

Enterprise insiders y understand the business needs, what they lack is market data and consumer feedback, the missing data analysis methods and tools. Enterprise insiders should become the main force of big data business application, participate in some industry activities, and take the initiative to find data and solutions from the demand. In the era of mobile Internet, the business competition strategy is clear, one is fast, and the other is to use data for decision-making.

The development of the big data industry is not just a matter for big data companies themselves, but also for each enterprise itself. Enterprise customers should also be based on business needs, take the initiative to the market to find data and solutions, improve data business sensitivity, from the business scenario, to find valuable data.

4 Insufficient depth of combination of big data technologies and products with business

All big data enterprises and customers in the market are facing a problem, that is, the depth of combination of data solutions with customer business is insufficient, and the overall effect of data on business promotion is not as expected, which is also a pain point of the outbreak of the big data industry. Due to external data quality, enterprise user data sensitivity, enterprise management style, business data talent and other issues, it is difficult to combine big data solutions with business depth.

The core value of big data is to reveal the law of development of affairs and help enterprises utilize data for scientific decision-making. At present, the commercial application fields of big data are mainly concentrated in the fields of data collection, data storage, data computation, user profiling, and precision marketing. The most commercially valuable prediction and assisted decision-making functions of big data have not been fully utilized. Especially in terms of major strategic decisions, the role of big data is not obvious. Enterprise product development, market strategy, strategic decision-making still rely on the elite decision-making and empiricism in the past. There are only two types of enterprises in the future society, one is the enterprise that utilizes data to develop, and the other is the enterprise that does not pay attention to data and is eliminated.

If a big data company wants to grow and develop, and if it wants to become a leading company in the industry, it must give up short-term interests and go deep into the customer's operations to understand the customer's data, understand the customer's business, and understand the customer's business needs. At the same time, use the data to understand the customer, understand the market, understand the business scenarios. The core of the deep integration of data and business is to master the right data, the right methods, and the right tools. Business people should understand data, and technical people should understand business. Composite data talent is the key to the data business, business personnel to master the data technology threshold is high, but the threshold of technical personnel to understand the business is very low, composite talent tends to start from the technical personnel training.

Data talent within the enterprise and big data companies need to learn from each other, understand each other's environment and needs, and talk and communicate on the same platform. The data team needs to y understand the business scenarios and the laws behind them, start from the business, start from the scenarios, start from the data, combine the big data solutions with the depth of the business, use the data to promote the development of the business, and play the core value of the laws of big data prediction.

5 Professional data mining tools and lack of talent

Traditional data mining tools and BI systems have existed for a long time, through all kinds of reports to show that the management of the enterprise to understand the operation of the information, in the past did help companies to improve the level of management, to achieve the desired purpose.

In the era of big data, enterprises need real-time data, efficient tools, decision support and forecasting. The performance and flexibility of traditional data mining tools can no longer meet the needs of enterprises, in addition to the application of non-institutionalized data has also challenged the traditional data tools. the BI field of SAS, SPSS, TD and other data tools are increasingly marginalized, the R language is becoming the new darling of data statistics and visualization.

The time value of data is being emphasized, especially in financial enterprises, where all business departments expect to see the use of funds, customer transactions, and risk control in the shortest possible time. The sooner the enterprise understands the information, the sooner it will make decisions, time is Money. past data demand may be T + 5 or T + 30, now the data demand is often T + 1 or T + 0, data real-time, accuracy, relevance is mentioned as a very important position. The business needs are obvious, but the data tools and talent are a big challenge.

More than 200 big data companies in China have seen the dawn of the big data industry and its value, while also experiencing the pain of big data companies. The big data industry is developing rapidly and the market is gradually getting bigger, but its industrial advantages are not obvious, there are few advantageous enterprises, the commercialization of data is slow, the market is still immature, the commercial sensitivity of customer data is low, and there is a lack of high-quality data tools and talents. The inner feeling of all big data enterprises is that they are standing in the wind of the times and have chosen the right direction and industry, but it is still difficult to develop and grow. more than 200 big data enterprises are working hard to cultivate the big data industry, and it hurts and is happy.

The above is what I shared with you about recognizing the reality, the pain points and difficulties of China's big data industry, more information can be concerned about the Global Ivy to share more dry goods