Big data in China

Chapter 20 Big Data and Thinking Change (1)
Digitalization of thinking -- winning in the brain
"How to win?" is an ancient question with countless answers throughout thousands of years of civilization.Some people will say: You can win with strength; others say: You can win with wealth.But at least today, my answer is: Whoever has big data thinking can become the biggest winner in the future.

From the current point of view, when the era of big data comes, the competitiveness of any company can be divided into three types.The first is big data itself; the second is technologies related to big data; the third is big data thinking.Of course, these three kinds of competitiveness are irreplaceable and indispensable, but the most critical of them is the part that combines data and thinking.Data can be copied, technology can also be surpassed, only ideas cannot be stolen.Big data players with leading thinking are most qualified to launch a war with a high probability of winning, or occupy the largest market share, and form their own indestructible competitiveness.

At the same time, I also found that companies with big data thinking advantages are often those emerging entrepreneurial companies. The application value of big data in a specific business field, and put your ideals into practice in the first time.They completed their monopoly before anyone else entered.

This is the case in business, isn't it the case in life?It also applies to individuals.You work for others in the company, how to let the leaders discover your value?How to make your boss think you are stronger than your colleagues?All winners in the competition must know how to use data as a measurement standard and use data to look at things today.When you have this kind of thinking mode, you will find that the world has become completely different, and you will be more awake and rational than before.

The arrival of the era of big data is not only an update of technology, but also a change in the way we process information, an upgrade of our way of thinking about problems, a deepening of our thinking, and the evolution of our intelligence.Over time, big data will completely change the way people think about the world.

We will learn how to learn from a large amount of information that cannot be obtained from a smaller amount of information;

We will use more and more data to understand things and make decisions; we will discover that many things are random rather than deterministic; we will recognize the difference between correlation and causation between things.Of course, we are more strongly aware that today's world is already an era of intellectual games, and it is also a new century where thinking is king.With the improvement of big data processing capabilities and the penetration of big data industries into all aspects of work and life, human observation, memory, imagination, analysis and judgment, thinking, and adaptability will also be qualitatively improved.

You will gradually and surprisingly find that not only are you more rational, but the city is also becoming smarter.

The future world will completely become an arena where the brain is king, and money and power will become the slaves of the brain.The human brain creates data, and it will dominate data.

In fact, as early as five or six years ago, industry leaders predicted that the arrival of big data would trigger a new "smart revolution".We can discover knowledge from massive, complex, and real-time big data, improve intelligence, and create greater value for society.Therefore, despite the shortcomings of one kind or another, the era of big data must be a beautiful era, because digitization is making our lives better, our work more convenient, and our future clearer within a controllable range. It also allows human beings to see the hope of changing the overall structure of the world, so that it will gradually have the characteristics of "smartness", so that through the tool of data, the communication between man and nature can be realized, and the exchange of wisdom and rationality between each other can be realized.

Then, by this time, our study, work, life, entertainment, transportation, medical care, energy utilization, etc. will all change accordingly.We can change our minds and obtain necessary tools and skills from massive data; we can improve our wisdom, reshape our life strategies with big data thinking, and enhance our competitiveness.

China’s Big Data Logic: Causality > Correlation
In today's era, we need to change our minds and look at everything with a brand new mind and operational logic, in order to discover new things and release more productivity.Big data provides us with a new way to observe the world from a full perspective.

☆Important difference--from the analysis of causality to the analysis of correlation
Foreign big data thinking believes that—as European and American big data experts said in interviews, an important thinking transformation is from traditional causal analysis to correlation analysis. Only by focusing on correlation can we truly Reflect the thinking characteristics of big data.They believe that the emergence of big data is often overlooked, that is, it has quietly changed the causal thinking logic that people generally pursued in the past.

American big data expert Robert said: "Big data mainly starts with correlation rather than causation, which essentially changes the traditional data mining model."

For example, he cited that Google researchers published a paper in 2009 that successfully predicted the outbreak of seasonal flu, which caused a sensation in the medical community.The researchers conducted a very comprehensive analysis of the most frequently searched terms (as many as 2003 million) between 2008 and 5000, hoping to discover the characteristics of the geographical location of some search terms-whether it is related to the prevention of flu diseases in the United States. It is related to the data released by the control center.

Robert said: "The Centers for Disease Control and Prevention regularly tracks patients in hospitals and private clinics across the United States, and then aggregates and releases relevant information, but it often lags by a week or two. A certain period of time, but Google’s big data can discover real-time trends, and these entries are all real-time, with records of corresponding time and place.”

Finally, Google compared the predictions with the actual flu cases recorded by the Centers for Disease Control and Prevention in the past two years and found that the results of big data processing found a combination of 45 search terms. After calculation through a suitable data model, The conclusion drawn through the correlation prediction has an overlap of 97% with the official data, which shows that this type of prediction problem can be solved through correlation analysis.

There is an airline in Europe with millions of members.An important piece of information for members is the email address.In addition, the Twitter account application also requires an email address.Generally speaking, the same email address means that the member on the airline and the member on Twitter should be the same person.

So, the airline made a screening, from which [-] users were merged.What to do next?The airline invited the data department of a third-party company to come over, and the task was to see what these [-] users would do on the social platform, such as what they said, what they followed, and what topics they liked to participate in. Forward comments, or what kind of commercial media you like to follow.

The purpose of this airline is to study what kind of activities it needs to launch on social platforms, and what kind of gifts or discounts to give, so as to attract these [-] members to come and become VIP users of the company, and give the company Provide profit growth points.

Although the data involved in this story is related to [-] individuals, it is not a huge amount of data.But its essence actually reflects the value of relevance.Airlines seek correlation to determine where their new profit growth points are and who are the potential VIP users, and then make efficient decisions or take targeted marketing activities based on this.

☆China's big data philosophy-cause and effect first
Correlation is of course very important, and we have experienced its magic through the above examples.Through correlation, we discovered its great value for prediction, and behind it is the update of thinking and analysis methods.Correlation helps us move from understanding the past to predicting the future.

However, causation is still the logical basis for correlation.Because data is not just a cold symbol, data is only a representative of the connection between things, and each of us can include our own factors as an ordinary individual into this analysis system, and personal subjective things will greatly affect the system The direction and decision-making, this influence is even decisive.For example, various factors of human existence: risk, accident, love, cruelty, and even some mistakes can all be reflected in the changes of big data, which cannot be reflected by correlation and must be defined by causality.

"Then, here comes the important question, what is the relationship between causality and correlation?" Causality represents subjectivity and is a human factor; correlation represents objectivity and is an information factor.People and information are combined and inseparable, which means that causality and correlation are inseparable.Especially for the Chinese, when we see a correlation, we want to understand why and explore the reasons behind it, not just business or market opportunities, nor just a certain phenomenon.

When you start to give a hypothesis, build a model, and then verify the model, you will immediately bring in your own subjective factors, that is, the reason.Cause is causality, and it determines our direction.

This new way of thinking and prioritizing is very important.If you only focus on correlation, you will deviate from the original intention of the analysis because you lack the support of causality; if you only focus on causality, you will lose your grasp of massive data and key information during data collection because you ignore correlation.

simple is better than complex

(End of this chapter)