Big data in China

Chapter 33 Appendix
Open the door to big data

☆Points

●How to understand big data
●Big data treasure hunt map

●Preparation for thinking and acting

Big data has gained fame worldwide since 2011, and 2013 is the first year of big data in China.The Chinese quickly accepted the baptism of big data thinking. From the government to the private level, they began to promote big data to make it more valuable.

If you already know what big data is and what it can do through many similar books, then the appendix of this book focuses more on dispelling confusion and providing practical guidance for you: what should we do in the face of big data?
What exactly is "data"?What is the difference between big data and business intelligence?How big is the big data market?
What should we focus on to develop in order to achieve transcendence and counterattack?Where are our strengths and weaknesses?

In the hustle and bustle of experts, commentators and participants from all walks of life, we present this book to you. Get rid of the fatigue of receiving noisy information from the big data special committee, big data major, big data laboratory or various big data summits that emerge one after another, grasp the key points, grasp the key points, take a look, think about it, and find a place for yourself Clear and clear direction.

☆Basic concept--remember 4 V
Volume - large volume; Velocity - fast; Variety - miscellaneous types; Value - large value.

☆How "big" is big data?
A company called IDG has done a calculation on the volume of information created and copied every year: in 2011, it was about 1.8ZB;

In 2012, it reached 2.8ZB.

According to its calculations, when the time comes to 2020, this number will be about 40ZB.Of course, there are other companies that disagree with IDG's figures. They predict that by 2016, the total amount of data will only reach 1.3ZB.However, Google's statistics may be even more shocking - from the beginning of human civilization to 2003, humans have generated a total of 10 exabytes of data in a few 5 years, but by 2010, the speed of data generation has reached every two The extent of the day 5EB.

This shows that in today's world, data is not only very "big", but also very "fast", which is far beyond the imagination of the ancients or even ten years ago.

Seeing this, those who are interested in working in the big data industry or investing in data storage may be more confident.However, for ordinary people, all we can know is another key question: what is our personal data like?Obviously, we are making a huge contribution to the total amount of data, and we are also enjoying the benefits brought about by the qualitative change of this total amount.

However, no matter how the amount and speed of data changes, we must set a quantitative standard for it.After setting the quantitative standard, we can have a simple and clear value (whether it is accurate or not) to guide ourselves or the company's judgment on big data.This is both a necessary step and a wise one.

☆Treasure Hunting Map——If you are a big data entrepreneur, please look here!

As entrepreneurs and technicians, if you already have a deep understanding of big data, you need to know which industries will have big data, that is, which part of our energy to invest in, so that we can have the spring of big data.In the industrial chain, big data is usually divided into four categories:

scientific research big data

Scientific research data is relatively old, and actually existed before the generation of big data.They exist in certain equipment, research materials, or certain closed systems, and those with scientific research data are traditional scientific research institutions.They are the typical "tall, rich and handsome" that tend to ignore the mass market.Of course, the entry threshold for scientific research big data is also very high, often dominated by the state or large companies, and it is difficult for individuals to enter.

Internet big data
Internet big data is definitely the current mainstream, especially big data related to social media, which is considered to be the breaking point of the big data industry.Almost all big data technologies originated from Internet companies.Of course we also know their famous names, such as Baidu, Google, Facebook, Yahoo, Amazon and Alibaba.The driving force of this industry is based on two points: one is that the value of Internet companies is proportional to the square of the number of users, which is "Metcalfe's Law"; the other is the information sharing theory once quoted by Facebook founder Zuckerberg , that is, the amount of information a person shares doubles every one to two years.

In the industry chain of Internet big data, large enterprises occupy an absolute dominant position. They not only collect and own a large amount of data, but also play a leading role in platforms, such as Alibaba's data exchange platform, 360's large data center, Baidu's big data laboratory.Therefore, medium-sized enterprises can only survive if they start the service model, and invest their main energy in peripheral development, optimization and operation, and at the same time develop their own characteristics, such as Douban's "recommendation".

Small companies belong to a lower-level model. They have a special situation. Although they have a certain amount of data, they do not have big data capabilities. This has given rise to some opportunities for big data technologies and services.For example, they can make personalized recommendations and marketing analysis for e-commerce websites.This is also the case for various advertising alliances, mobile application service platforms, and companies that provide statistical analysis and marketing services.

Enterprise big data

Compared with more than a decade ago, enterprise data has not improved by an order of magnitude, but unstructured data content has been added on a traditional basis.Moreover, some aspects of enterprise big data and perception big data overlap. For example, enterprises will deploy the Internet of Things to collect perception data.

Sensing big data

Enterprise data is generated by people, and perception data is generated by machines such as objects, sensors, and signs.In contrast, the volume of perception data is much larger.A company predicted to us that the total amount of sensory data will surpass social media in 2015, and will reach 10 to 20 times the latter.

The reason why we can combine enterprise big data and perception big data into an overlapping industrial chain with the same nature is because both of them involve traditional industries and are much larger than the Internet industry in terms of economic aggregate.And importantly, traditional industries have limited big data capabilities, so this is also the main target market for big data technology and service companies, and an important opportunity for small and medium investors.

From a specific industry point of view, the huge demand for big data is mainly concentrated in public management and services, telecommunications, finance, medical care, retail, etc.However, in the case of fierce market competition, the more demanding customers are, the harder it is to provide you with golden opportunities to easily enter, even if your big data strength is quite excellent.Because no matter where you go, you will find those giants.

☆Preparation for Thinking and Action - Decision Maker's Section
As a business decision maker, what kind of big data view should you have?Facing such a huge volume of data, what preparations should you make in terms of thinking and action?
In the era of big data, we need a new worldview.Big data has opened up a brand new world for us technically, so we must take the initiative to seek changes, reflect a newer understanding of the world in terms of thinking and actions, and efficiently transform them into results.

For decision makers, big data is actually a kind of thinking, and it is also a strategic thing.Decision makers should see users and applications, not just a technology.However, it is obvious that the decision makers of many enterprises are absent in this regard. They are obsessed with the evolution of technology and lack macro thinking and layout.

Old perception - data is a scarce resource.This cognition directly leads to the small-scale peasant mentality of decision-makers, who do not pay attention to data measurement and massive data collection, but always imagine that they can squeeze the most information from the least data.New cognition - the key to big data lies in "big".

Decision makers themselves must have the courage to participate in the big data game and win, establish the concept of "big" for themselves, and collect all data, instead of getting used to sampling processing and analysis in the past.Decision-making must be based on all data, analyze all factors comprehensively and objectively, and take this as one's own responsibility and belief.The big data concept that decision makers need to have is also very simple: for us, data is not a burden, but wealth; whether the data has been used or not, it must be preserved, so as to gradually convert "cost" into "profit".Moreover, one must minimize one's own subjectivity.

1. Let data collection tools decide what information to collect and where to collect it.

2. If our analysis process has natural subjectivity, such as public opinion surveys or street interviews, etc., then before making a decision on data collection, you have the responsibility to design a more objective premise for it, such as setting many questions to reduce subjective error.

3. You should integrate data collection and storage into a shared platform as much as possible, that is, establish a basic framework, instead of doing a different collection and storage solution for a business.Moreover, you also need to introduce an incentive mechanism in the process of data collection to make the most adequate preparations for decision-making and collect the most abundant information.

(End of this chapter)