After reading Big Data Times, 1 is not interested in bestsellers, hot topics and fashion technology. Books and periodicals, like to have a certain year. Topic, the point of view of retreat. Novel products are beyond my power. I am used to using mature technical products. Neither lofty nor indifferent, just keep a certain distance from reality and leave some room for thinking. This habit has been broken recently. As a result of my work, the emerging concept of "big data" began to enter my field of vision frequently. I can't control my curiosity. I bought "Big Data Times" online and couldn't put it down. After reading it for three days, it was quite rewarding. This book has the following characteristics.
First of all, the author stands at the commanding heights of theory, and clearly expounds the innovations brought by big data to human work, life and thinking, three typical business models in the era of big data, and the challenges brought by the era of big data to personal privacy protection and public safety. Secondly, the examples in this paper are close to real life and times, which makes readers deeply impressed and empathetic. Besides, the author didn't use many technical terms to pretend to be a professional face. The whole book, words and sentences are easy to understand.
The author believes that there are three remarkable characteristics in the era of big data.
First, when people study and analyze a phenomenon, they will use all the data instead of sampling data.
Second, in the era of big data, we should not blindly pursue the accuracy of data, but should adapt to the diversity and richness of data and even accept the wrong data.
Third, understanding the correlation between data is better than exploring causality. What is more important than why.
The author points out that with the development of technology, the cost of data storage and processing has been significantly reduced, and people are now able to extract insights from fragmented and seemingly unrelated data residues. In the era of big data, three types of companies will become the darling of the times. First, companies and organizations with big data. Such as the government, banks, telecommunications companies, global Internet companies (Alibaba, Taobao). Second, professional companies with data analysis and processing technology, such as Amazon and Google. Third, companies with innovative thinking may have neither big data nor professional technology, but they are good at using big data to find their ideal world from big data.
How will individuals cope with the coming era of big data? This is a serious problem.
Reflections on the "era of big data" 2 Now when it comes to new media and the Internet, we must mention big data. It seems that if you don't say this, you will be out. What's more, there are still many people who follow suit, and many empty talkers haven't even read the classic work in this field-The Age of Big Data by Schon Dove. Who is Victor Mayer Schoenberg? He is currently a professor of governance and supervision at the Internet Institute of the Network College of Oxford University, and was the head of the information supervision research project at Kennedy College of Harvard University. His consulting clients include global companies such as Microsoft, Hewlett-Packard and IBM. He is the real maker and participant behind the official Internet policy of the European Union, and he has also served as a think tank of senior governments in many countries. This Oxford professor, who is known as the "prophet" in the era of big data, is awesome! So, the master said the golden rule? Not necessarily. You have to do some homework and understand the master's works before you can have an ideological dialogue with him.
Schoenberg discusses big data in three parts, namely thinking change, business change and management change.
In the first part "Thinking Change in the Age of Big Data", Schoenberg clearly stated his three views:
First, more: not random samples, but all data.
Second, more miscellaneous: not accurate, but miscellaneous.
Third, better: not causality, but correlation. I disagree with the first point.
On the one hand, it is very difficult to process all the data in terms of technology and equipment. On the other hand, is it necessary for everyone? Is it necessary to collect all the data for data analysis to judge simple facts?
I have discussed this issue with Professor Jonathan Zhu from the City University of Hong Kong. Professor Zhu is an expert in communication research methods and data analysis. He thinks that we can find a method of mathematical statistics for analysis, and we don't necessarily need all the data. In connection with the relevance mentioned in Schoenberg's second point of view, I understand that the total data he said refers not to quantity but to range, that is, the random sample of big data is not limited to the target data, but also includes all data outside the target. I don't think big data analysis can rule out random sampling, but the method and scope of sampling should be expanded.
I agree with Schoenberg's second point. I think it is a good supplement to his first point of view, and it is also a reflection on precision communication and precision marketing. The simple algorithm of big data is more effective than the complex algorithm of small data. "More macro vision and oriental philosophical thinking. I can't fully agree with Schoenberg's third point of view. " Not causality, but correlation. You don't need to know why, just what it is. Communication is data, and data is relationship. In the era of small data, people only care about causality, but they don't know enough about correlation. In the era of big data, relevance plays an important role, which cannot be overemphasized, but it should not be completely ruled out. Where does big data come from? What is it used for? If we completely ignore causality and don't know the cause and effect of big data, it will also dispel the humanistic value of big data. Nowadays, in order to elaborate and spread their views, many scholars often make surprising remarks and completely deny old ideas.
The complexity and diversity of anything in the world are not simple either-or. Is Schoenberg also such a naive thinking of binary opposition? In fact, when reading, readers must see clearly what context they are saying, and don't fall into the misunderstanding of taking things out of context because of the shallowness of reading. For example, Schoenberg proposed "not causality, but correlation." In this paper, he also said in the book: "In most cases, once we have completed the correlation analysis of big data and are no longer satisfied with just knowing' what', we will continue to study causality at a deeper level and find out the' why' behind it." It can be seen that all the data and related relationships he said are in a specific context and are options in data mining.
One of the driving forces of big data research is commercialization. In the second part, Schoenberg discusses the business changes in the era of big data. Schoenberg believes that digitalization means that everything can be "quantified". The quantitative analysis of big data can effectively answer the question of "what", but it still can't completely answer the question of "why". So I think we can't rule out qualitative analysis and qualitative research. There is no doubt that data innovation can create value. When discussing the role positioning of big data, Schoenberg still put it in the commercial system of data application, but not in the whole social system, but he discussed this issue in the second part of Management Change in the Age of Big Data.
In a risky society, the problem of information security has become increasingly prominent. How to get rid of the dilemma of big data? Schoenberg tried to answer in the last section "Control", but it was basically a cliche. I think maybe kevin kelly's Out of Control can help us answer this question? At least it can provide more thinking dimensions. As Schoenberg said in his conclusion: "Big data is not a cold world full of algorithms and machines, and the role of human beings cannot be completely replaced. What big data provides us is not the final answer, but the reference answer. Help is temporary, and better methods and answers are still in the near future. " Thank you, Brother Xun! Let the big data discussion return from natural science to humanities and social sciences. It can be inferred that the "big data era" is not the final answer, nor the standard answer, but the reference answer.
In addition, before reading this book, you must have some basic knowledge and concepts of data science, such as what is data? What is big data? What's the difference between data analysis and data mining, and what's the difference between digitalization and dataization? Do some homework before reading, it will be easier to read.
After reading the book "The Age of Big Data", I realized that we are about to or are about to usher in another major change after the leap from written to electronic.
This book introduces the three major changes that followed the arrival of the era of big data-business change, management change and thinking change.
In fact, this change has already begun. The business sector has been revolutionized by the arrival of the era of big data. A few years ago, a company called Farecast made it no longer a dream to book a better air ticket price. The company uses the data of air ticket sales to predict the future trend of air ticket prices. Now, passengers who use this tool can save about $50 per ticket on average, which is the convenience brought by big data.
You should know that the flu H 1N 1 appeared in 2009. Take the United States as an example The CDC only conducts data statistics once a week, and patients usually go to the hospital because of unbearable pain, which also leads to the lag of information. However, for fast-spreading diseases, Google can make timely judgments and determine the location of influenza outbreaks. This is based on huge data resources, indicating that the era of big data has also had a major impact on public health!
In my opinion, if you want to swim in the era of big data, you must not only learn to analyze, but also make bold decisions.
In the United States, every year in July and August, when typhoons are raging, flood control materials will also be put on the shelves. Wal-Mart noticed that at this time, the sales of an egg tart increased significantly compared with other months. As a result, the merchant made a bold guess, which originated from the correlation between the two items, so he put the egg tart next to the flood control supplies. Such a move has greatly increased profits, which belongs to the big data mind of the world's first retailer!
The arrival of the era of big data can make our life more convenient. However, if big data dominates everything, there are certain risks.
Everyone should know that electronic maps can show people the way. But you should not know that it will silently accumulate people's travel data and infer where your home and work unit are through intelligent analysis. In this way, our privacy is collected without being known.
The arrival of the era of big data makes our life safer and more convenient, but at the same time, our privacy is no longer privacy, and data collection has become all-encompassing and pervasive. The world has taken a small step towards the era of big data, and a brand-new era is coming to us. Let's arm our brains with knowledge and prepare for the new era!
Reflections on the "big data era" 4 First of all, I want to talk about what is big data and what is the big data era. Big data is both a resource and a tool. It provides a new way of thinking for understanding today's information world. Why is it a new way of thinking? In the era of lack of information or simulation, we prefer the accurate way of thinking, just like "nail is nail, riveting is riveting", but under this traditional way of thinking, we get only one answer to the question.
In the era of big data, we broke this way of thinking, in other words, we accepted the uncertainty of the results. In short, I think big data is a forecasting model. In the era of big data, we are not concerned with cause and effect, that is, what this is, but more concerned with the connection of "what". In other words, under this new way of thinking, it is not feasible for us to explore the reasons behind the problem. What we do is to use big data as a tool and let the data speak for itself!
Secondly, I want to talk about how to use big data to enhance the combat effectiveness of our army. Of course, big data analysis is not accurate prediction, and accurate prediction does not exist. Big data can only help us understand the present and predict the future.
As a soldier, I am concerned about how to make good use of big data tools to enhance the combat effectiveness of our army and win this information war. Undoubtedly, we are not fighting knife-to-knife, gun-to-gun wars, not to mention the simulation era, but the digital era, and we are fighting an information war!
With the victory of four wars, the US military's war form has changed from mechanization to informationization, and the corresponding battlefield winning time is getting shorter and shorter, which is the inevitable result of the era of big data. And our army is in the process of turning to informationization. In the process of this war form, we need more high-tech talents such as computational analysts, big data analysts and mathematicians to win this information war. This is the foundation we have to have in the era of big data. The improvement of our army's combat effectiveness is imminent!
Of course, big data is a double-edged sword, and it can only be won if it is used well. On the contrary, poor use will lead to incalculable losses.
After all, this is only a prediction model, and it can't get accurate prediction results. We should provide data to us and not be framed by a huge database. In order to adapt to the development of the times, in this world of survival of the fittest and law of the jungle, the brutal competition in the era of big data has sounded the alarm for us, and a silent information war has started!
Reflections on the era of big data 5 Last year's "cloud computing" was in full swing, and this year's "big data" was caught off guard. It seems that overnight, all manufacturers have changed their flags and pushed up "big data". As a result, CIOs of various enterprises have also turned their attention to "big data". There is a cartoon from Weibo, The Programmer, which is very vivid. I think this picture really reflects the status quo of cloud computing and big data for SMEs.
But then again, The Age of Big Data is a good book.
Of course, many IT celebrities also strongly recommended it and wrote many comments to express their love for this book. Before reading this book, I was basically confused about the concept of so-called big data. Although I have paid attention to BI, which is also very popular now, I feel similar. It may be more data, more detailed data analysis and data mining. After reading this book, I feel that the previous idea can only be considered as a small half-massive data, while the other one: focusing on data relevance, not data accuracy, may be the difference between big data and current BI, not only the method, but also the way of thinking. But frankly, it really takes time to test whether the data is more relevant or accurate. At least from the current data analysis methods, it is more inclined to the accuracy of the data.
After reading this book, I have some questions in my heart:
1. What is big data?
I checked Baidu Encyclopedia, and it is defined as follows: bigdata, or huge data, refers to the information that involves so much data that it can't be captured, managed, processed and sorted into more positive purposes by mainstream software tools at present to help enterprises make business decisions within a reasonable time. The 4V characteristics of big data: quantity, speed, diversity and accuracy, which seems to be the definition of IBM.
Personal opinion: Mass data and mass storage are the basic prototypes of big data.
2. What kind of enterprise is big data suitable for?
It is true that the premise of big data is massive data. Only with a huge amount of data resources can we find out the relevance of the data, and then make it produce value for the enterprise through professional processing. For telecom operators, large enterprises that use such massive user data on the Internet also have unique conditions on the road of applying big data, but what about small and medium-sized enterprises? Sales order data? If it is not a century-old shop, the estimated data is pitiful, and only consumer data can be used. It seems that most manufacturers, for example, analyze consumers' buying behavior the most.
Similarly, in the public sector, the role of big data may also play a very good role. On the contrary, I feel that most small and medium-sized enterprises seem to have a big problem in applying big data. The book says: Big data is the competitiveness of enterprises. It is true that data is the core intangible resource of an enterprise (if used well), but is it really appropriate for all data, or in other words: all enterprises have the competitiveness of big data? Will it appear that SMEs are making a mountain out of a molehill?
3. The impact of big data
When wave after wave of IT technology upsurge comes to our pavement, you have to start to meet the impact it brings to you before you are even ready. With the Internet of Things and cloud computing, big data began to appear. But what does it bring us?
1) Predicting the future Starting with the case that Google successfully predicted the possible future influenza, it shows that the application of big data can be used as a beacon for our lives. The essence is simple, technology changes the world.
2) Transforming the business opportunities brought by commercial big data will also give birth to a series of business opportunities and business models related to big data, and the potential value of data will continue to play a role. It is easy to imagine that there will be a data industry chain in the future, with specialized data collection, data analysis and data generation. Of course, IT companies will also be affected.
3) The Book of Easy Thinking says: Because there is a huge amount of data as the basis, we may pay more attention to the relevance of data rather than the fineness in the future. I still have reservations about this article.