Speaking of big data, it is estimated that everyone has only heard the concept, but there is no standard thing about what it is and how to define it, because in our impression it seems that many companies are called big data companies, and there are hundreds of business forms. This kind of feeling is not very easy to understand, so I suggest to understand big data literally. Four characteristics of big data are mentioned in the "Big Data Era" written by Victor Meyer-Schoenberger and Kenneth Cukje:
1. a lot
The characteristics of big data are first reflected in "big". From the pre-Map3 era, a small MB-level Map3 can meet the needs of many people. However, with the passage of time, the storage unit has changed from GB to TB in the past, and even The current PB, EB level. Only when the volume of data reaches the PB level can it be called big data. 1PB is equal to 1024TB, 1TB is equal to 1024G, then 1PB is equal to 1024*1024 G of data. With the rapid development of information technology, data has begun to grow explosively. Social networks (Weibo, Twitter, Facebook), mobile networks, various smart tools, service tools, etc., have all become sources of data. Taobao's nearly 400 million members generate about 20TB of commodity transaction data every day; about 1 billion Facebook users generate more than 300TB of log data every day. There is an urgent need for intelligent algorithms, powerful data processing platforms and new data processing technologies to count, analyze, predict and process such large-scale data in real time.
2. High speed
The logic of data processing is very fast through algorithms. The one-second law can quickly obtain high-value information from various types of data. This is also fundamentally different from traditional data mining techniques. The generation of big data is very rapid, and it is mainly transmitted through the Internet. Everyone in life is inseparable from the Internet, which means that individuals are providing large amounts of information to big data every day. And these data need to be processed in time, because it is very uneconomical to spend a lot of capital to store historical data with little effect. For a platform, the data may only be saved in the past few days or a month, and then farther away. The data must be cleaned up in time, otherwise the cost will be too great. Based on this situation, big data has very strict requirements for processing speed. A large number of resources in the server are used to process and calculate data, and many platforms need to perform real-time analysis. Data is generated all the time. Whoever is faster has an advantage.
3. Diversity
If there is only a single data, then these data have no value. For example, there is only a single personal data or a single user submits data. These data cannot be called big data. A wide range of data sources determines the diversity of big data forms. For example, among the current Internet users, the characteristics of each person are different in age, education, hobbies, personality, etc. This is the diversity of big data. Of course, if it is expanded to the whole country, the diversity of data will be stronger. There will be a variety of data diversity in each region and each time period. Any form of data can have an effect. At present, the most widely used recommendation systems, such as Taobao, NetEase Cloud Music, Toutiao, etc., will analyze the user's log data to further recommend the things users like. Log data is data with obvious structure, and some data is not structured obviously, such as pictures, audio, video, etc. These data have weak causality, so they need to be manually labeled.
4. Value
This is also the core feature of big data. Among the data generated in the real world, valuable data occupies a small proportion. Compared with traditional small data, the biggest value of big data lies in mining data that is valuable for future trend and pattern prediction and analysis from a large number of irrelevant types of data, and through machine learning methods and artificial intelligence methods. Or in-depth analysis of data mining methods to discover new laws and new knowledge. If you have more than 1PB of online data of all 20-35 young people in the country, then it will naturally have commercial value. For example, by analyzing these data, we can know the hobbies of these people, and then guide the development direction of the product, etc. . If you have data on millions of patients across the country, analysis based on these data can predict the occurrence of diseases. These are the value of big data. Big data is widely used, such as in agriculture, finance, medical and other fields, so as to finally achieve the effect of improving social governance, increasing production efficiency, and advancing scientific research.
Big data has become the rule of the game in most industries in the past few years. Industry leaders, academics and other well-known stakeholders agree on this. As big data continues to penetrate into our daily lives, the hype surrounding big data is growing Turn to real value in actual use.
Big data has become ubiquitous. Big data is used in various industries, including finance, automobiles, catering, telecommunications, energy, physical fitness and entertainment, and all walks of life in society have been integrated into the footprint of big data.
Communications, media and entertainment industry
As consumers expect multimedia needs in different formats and various devices, some of the major data challenges in the communications, media and entertainment industries include:
(1) Analyze and utilize consumer insights (2) Use mobile and social media content (3) Solve real-time and media content usage (4) Application of big data in the communications, media and entertainment industries
Companies in this industry analyze customer data and behavioral data at the same time to create detailed customer profiles that can be used to:
(1) Create content for different target audiences (2) Recommend content as needed (3) Measure content effectiveness
For example, Taobao will analyze according to the content you have searched and browsed. When you log in next time, it will recommend your favorite products on the homepage, what kind of sneakers do you like, and what kind of snacks do you like. Under the big data analysis, you can do what you like.
Bank Securities Industry
A study conducted a survey on 16 projects of 10 top investment and retail banks. The results showed that the industry’s challenges include: securities fraud early warning, UHF financial data analysis, credit card fraud detection, audit trail filing, corporate credit risk Reports, trade visibility, customer data conversion, social analysis of transactions, IT operation analysis and IT strategy compliance analysis, etc.
The Securities and Exchange Commission (SEC) is using big data to monitor financial market activity. They are currently using network analysis and natural language processors to capture illegal transactions in financial markets.
Retailers in the financial market, big banks, hedge funds and other so-called "big boys" use big data for high-frequency trading, pre-trade decision support analysis, sentiment measurement, predictive analysis and other aspects of transaction analysis.
The industry also relies heavily on big data for risk analysis, including anti-money laundering, enterprise risk management, "know your customer" and reduce fraud.
medical field
The health care department has obtained a large amount of data, but it has not been able to use the data to curb the rise in health care costs, increase health care benefits, and improve system efficiency. This is mainly due to insufficient or unavailability of electronic data. In addition, it is difficult to link healthcare databases that store health-related information with data on useful patterns in the medical field.
The computing power of big data analysis applications allows us to decode the entire DNA in a matter of minutes. And so that we can work out the latest treatment plan. At the same time, it can better understand and predict diseases. Just as people wear smart watches and other data that can be generated, big data can also help patients to better treat their conditions. In the medical field, the important role of the Internet of Things is manifested in big data. Big data technology has been used in hospitals to monitor the condition of premature babies and sick babies. By recording and analyzing the baby’s heartbeat, doctors can predict that the baby’s body may have symptoms of discomfort. This can help doctors better rescue the baby.
Some hospitals are using data collected from millions of patient mobile apps to allow doctors to use evidence-based medicine instead of performing multiple medical examinations on all patients who go to the hospital. The University of Florida used free public health data and Google Maps to create visual data for faster identification and effective analysis of medical information for tracking the spread of chronic diseases.
Manufacturing and Energy
The increasing demand for natural resources such as oil, agricultural products, minerals, natural gas, and metals has led to an increase in the amount and complexity of data. A large amount of manufacturing data has not yet been developed. Insufficient use of this information will hinder product quality, energy efficiency, reliability and higher profit margins.
The concept of energy big data is the comprehensive collection, processing, analysis and application of related technologies and ideas for data in the energy fields such as electricity, oil, gas and other fields such as population, geography, and meteorology. Energy big data is not only the in-depth application of big data technology in the energy field, but also the in-depth integration of energy production, consumption and related technological revolutions with big data concepts, which will accelerate the development of the energy industry and innovation of business models.
In the natural resources industry, through big data, geospatial data, graphic data, text and time data can be used to ingest and integrate a large amount of data to build predictive models to help make decisions. Application areas include: seismic interpretation and reservoir characterization.
Specific challenges in the insurance industry
The main challenges include the lack of personalized services, the lack of personalized pricing and the lack of targeted services for new market segments and specific market segments. In the survey conducted by Marketforce, the challenges identified by insurance industry professionals included the loss of profits caused by insufficient data and the desire for better insights.
The industry is already using big data to analyze and predict customer behavior through data obtained from social media, GPS-enabled devices and surveillance video, to provide customer insights for transparent and simple products. Big data can also protect companies and improve customer retention.
In terms of claims management, predictive analysis of big data has been used to provide faster services because a large amount of data can be specifically analyzed during the underwriting phase. Fraud detection has also been enhanced. Real-time monitoring of claims through digital channels and social media has been used to provide insights to insurance companies.
Traffic field
Recently, large amounts of data from location-based social networks and high-speed data from telecommunications have affected travel behavior. Regrettably, research to understand tourism behavior has not been so rapid. In most places, transportation demand patterns are still under-understood about the structure of social media.
As one of the sources of massive data in the information age, video surveillance has produced huge information data. The Internet of Things is ubiquitous in the security field. Especially in recent years, with the rapid development of industries such as safe cities and intelligent transportation, large integration, large networking, and cloud technologies have promoted the security industry to enter the era of big data. The existence of big data in the security industry has been well known by more and more people, especially the massive unstructured video data in the security industry, as well as the rapid growth of characteristic data, which has driven a series of problems in the application of big data.
The government uses big data: traffic control, route planning, intelligent transportation systems, congestion management (predicting traffic conditions)
The private sector uses big data in transportation: revenue management, technological improvement, logistics and competitive advantage (by consolidating shipments and optimizing freight)
Personal use of big data includes: route planning to save fuel and time, travel arrangements, etc.
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