Big data analysis refers to the analysis of large-scale data. Big data can be summarized as 5 V, with a large volume, a Velocity, a Variety, a Value, and a Veracity. Big data is the hottest IT industry vocabulary, and the subsequent use of data warehousing, data security, data analysis, data mining, etc. around the commercial value of big data has gradually become the focus of profits that industry professionals are vying for. With the advent of the era of big data, big data analysis has emerged.
Tool introduction front end display
The front-end open source tools for presentation analysis are JasperSoft, Pentaho, Spagobi, Openi, Birt, and more.
Commercial analysis tools for presentation analysis include Style Intelligence, RapidMiner Radoop, Cognos, BO, Microsoft Power BI, Oracle, Microstrategy, QlikView, Tableau.
Domestic BDP, Guoyun Data (Big Data Mirror), Sematic, FineBI and so on.
database
There are Teradata AsterData, EMC GreenPlum, HP Vertica and more.
Data mart
There are QlikView, Tableau, Style Intelligence and more.
Analytical steps Six basic aspects of big data analysis
1. Analytic Visualizations
Whether it is for data analysis experts or ordinary users, data visualization is the most basic requirement of data analysis tools. Visualization can visualize the data, let the data speak for itself, and let the audience hear the results.
2. Data Mining Algorithms
Visualization is for people to see, and data mining is for the machine. Clustering, segmentation, outlier analysis, and other algorithms allow us to drill down into the data and mine value. These algorithms not only deal with the amount of big data, but also the speed of big data.
3. Predictive Analytic Capabilities
Data mining allows analysts to better understand the data, and predictive analysis allows analysts to make some predictive judgments based on the results of visual analysis and data mining.
4. Semantic Engines (Semantic Engine)
We know that because of the diversity of unstructured data that brings new challenges to data analysis, we need a range of tools to parse, extract, and analyze data. The Semantic Engine needs to be designed to intelligently extract information from the "documents."
5. Data Quality and Master Data Management (Data Quality and Data Management)
Data quality and data management are some of the best practices in management. Processing data through standardized processes and tools ensures a well-defined, high-quality analysis.
If big data is really the next major technological innovation, we'd better focus on the benefits that big data can bring us, not just challenges.
6. Data storage, data warehouse
The data warehouse is a relational database established to facilitate multidimensional analysis and multi-angle display of data stored in a specific mode. In the design of business intelligence systems, the construction of data warehouse is the key, is the foundation of business intelligence system, undertakes the task of data integration of business systems, provides data extraction, transformation and loading (ETL) for business intelligence systems, and Data is queried and accessed to provide a data platform for online data analysis and data mining.
Development status open source big data
1. Hadoop HDFS, Hadoop MapReduce, Hba se, Hive are gradually born, and the early Hadoop ecosystem has gradually formed.
2. Hypertable is an alternative. It exists outside the Hadoop ecosystem, but there have been some users.
One machine data warehouse
IBM PureData (Netezza), Oracle Exadata, SAP Hana, and more.
Application example Brazil World Cup relationship
Different from previous World Cups: Data Analysis [3] has become a wonderful highlight outside the World Cup in Brazil. With the players' competition in the arena, Big Data is also fully explaining the analysis story behind the World Cup. The German team, which has always been known for its rigor, has introduced a football solution that specializes in big data, analyzes the game data, optimizes the team configuration, and finds the "empowering" method of the game by analyzing the opponent data; Google, Microsoft, Opta, etc. pass big data. Analyze and predict the results... Big data not only became the "12th person" on the field, but also played the "predictor" of the World Cup to some extent.
Big data analysis 邂逅 World Cup is an inevitable occurrence of the big data era, and big data analysis will also change every aspect of our lives in the future.
Business outcome
1. Proactive & Predictive Demand: Corporate organizations are facing increasing competitive pressures. They not only need to acquire customers, but also understand customer needs in order to enhance the customer experience and develop long-lasting relationships. By sharing data, customers reduce the level of privacy of their data usage, and expect companies to understand them, form interactions, and provide a seamless experience across all touchpoints.
To do this, companies need to identify multiple identifiers for customers (such as mobile phones, emails, and addresses) and consolidate them into a single customer ID. As customers increasingly use multiple channels to interact with the enterprise, traditional data sources and digital data sources need to be integrated to understand customer behavior. In addition, companies need to provide context-sensitive real-time experiences, which is what customers expect.
2. Buffer Risk & Reduce Fraud: Security and fraud analysis is designed to protect all physical, financial, and intellectual assets from internal and external threats. Efficient data and analytics will ensure optimal levels of fraud prevention and improve the security of the entire organization: Deterrence needs to establish effective mechanisms for companies to quickly detect and predict fraud while identifying and tracking the perpetrators.
Using the statistical, network, path, and big data methodology for predictive fraud-prone models that bring alerts will ensure that responses are triggered in a timely manner after being triggered by the real-time threat detection process, and automatically alert and respond accordingly. Data management and efficient and transparent fraud reporting mechanisms will help improve the fraud risk management process.
In addition, integrating and correlating data across the enterprise provides a unified view of fraud across different lines of business, products, and transactions. Multi-type analysis and data foundations can provide more accurate fraud trend analysis and forecasting, and predict future potential operations to identify vulnerabilities in fraud audits and investigations.
3. Providing related products: Products are the cornerstone of any enterprise's survival, and are usually the areas in which enterprises invest the most. The role of the product management team is to identify trends in driving innovation, new capabilities, and service strategy roadmaps.
Effectively collating third-party data sources of ideas and opinions published by individuals, and then analyzing them, can help companies stay competitive when demand changes or develop new technologies, and accelerate the forecast of market demand. Provide the appropriate product before production.
4. Personalization & Service: The company is still struggling to deal with structured data and needs to quickly respond to the instability caused by customer interaction through digital technology. To respond in real time and make customers feel valued, it can only be achieved through advanced analytical techniques. Big data brings opportunities to interact based on customer personality. This is achieved by understanding the customer's attitude and considering real-time location and other factors to bring personalized attention to the multi-channel service environment.
5. Optimizing & Improving Customer Experience Poor management can lead to countless major issues, including the risk of damaging the customer experience and ultimately reducing brand loyalty. By applying analytics in process design and control, as well as in business operations optimization in the production of goods or services, we can improve the effectiveness and efficiency of meeting customer expectations and achieve operational excellence.
By deploying advanced analytics technology, you can increase the productivity and efficiency of on-site operations and optimize organizational manpower arrangements based on business and customer needs. Optimized use of data and analytics can provide an end-to-end view and measure key operational metrics to ensure continuous improvement.
For example, for many companies, inventory is the largest item in the current asset class—too much or insufficient inventory can directly affect a company's direct costs and profitability. Through data and analysis, uninterrupted production, sales and/or customer service levels can be ensured at the lowest cost, improving inventory management. Data and analysis provide information on current and planned inventory conditions, as well as information on inventory height, composition and location, and can help determine inventory strategies and make decisions accordingly. Customers look forward to getting a seamless experience and letting companies know about their activities.
Asynchronous big data analysis
The big data analysis of asynchronous processing complies with the process of capture, storage and analysis. In the process, the data is acquired by sensors, web servers, sales terminals, mobile devices, etc., and then stored on the corresponding devices, and then analyzed. Since these types of analysis are performed by traditional relational database management systems (RDBMS), the form of the data needs to be transformed or transformed into a type of structure that the RDBMS can use, such as rows or columns, and needs to be compared with other data. continuous.
The process of processing is called extraction, transfer, loading or called ETL. The data is first extracted from the source system, the data is normalized and the data is sent to the corresponding data warehouse for further analysis. In a traditional database environment, this ETL step is relatively straightforward because the objects of analysis are often well-known financial reports, sales or market reports, enterprise resource planning, and so on. However, in a big data environment, ETL can become relatively complex, so the transformation process is different for different types of data sources.
When the analysis begins, the data is first extracted from the data warehousing and placed in the RDBMS to generate the required reports or to support the corresponding business intelligence application. In the big data analysis process, the bare data and the converted data are mostly saved, because it may need to be converted again later.
Tool introduction front end display
The front-end open source tools for presentation analysis are JasperSoft, Pentaho, Spagobi, Openi, Birt, and more.
Commercial analysis tools for presentation analysis include Style Intelligence, RapidMiner Radoop, Cognos, BO, Microsoft Power BI, Oracle, Microstrategy, QlikView, Tableau.
Domestic BDP, Guoyun Data (Big Data Mirror), Sematic, FineBI and so on.
database
There are Teradata AsterData, EMC GreenPlum, HP Vertica and more.
Data mart
There are QlikView, Tableau, Style Intelligence and more.
Analytical steps Six basic aspects of big data analysis
1. Analytic Visualizations
Whether it is for data analysis experts or ordinary users, data visualization is the most basic requirement of data analysis tools. Visualization can visualize the data, let the data speak for itself, and let the audience hear the results.
2. Data Mining Algorithms
Visualization is for people to see, and data mining is for the machine. Clustering, segmentation, outlier analysis, and other algorithms allow us to drill down into the data and mine value. These algorithms not only deal with the amount of big data, but also the speed of big data.
3. Predictive Analytic Capabilities
Data mining allows analysts to better understand the data, and predictive analysis allows analysts to make some predictive judgments based on the results of visual analysis and data mining.
4. Semantic Engines (Semantic Engine)
We know that because of the diversity of unstructured data that brings new challenges to data analysis, we need a range of tools to parse, extract, and analyze data. The Semantic Engine needs to be designed to intelligently extract information from the "documents."
5. Data Quality and Master Data Management (Data Quality and Data Management)
Data quality and data management are some of the best practices in management. Processing data through standardized processes and tools ensures a well-defined, high-quality analysis.
If big data is really the next major technological innovation, we'd better focus on the benefits that big data can bring us, not just challenges.
6. Data storage, data warehouse
The data warehouse is a relational database established to facilitate multidimensional analysis and multi-angle display of data stored in a specific mode. In the design of business intelligence systems, the construction of data warehouse is the key, is the foundation of business intelligence system, undertakes the task of data integration of business systems, provides data extraction, transformation and loading (ETL) for business intelligence systems, and Data is queried and accessed to provide a data platform for online data analysis and data mining.
Development status open source big data
1. Hadoop HDFS, Hadoop MapReduce, Hba se, Hive are gradually born, and the early Hadoop ecosystem has gradually formed.
2. Hypertable is an alternative. It exists outside the Hadoop ecosystem, but there have been some users.
One machine data warehouse
IBM PureData (Netezza), Oracle Exadata, SAP Hana, and more.
Application example Brazil World Cup relationship
Different from previous World Cups: Data Analysis [3] has become a wonderful highlight outside the World Cup in Brazil. With the players' competition in the arena, Big Data is also fully explaining the analysis story behind the World Cup. The German team, which has always been known for its rigor, has introduced a football solution that specializes in big data, analyzes the game data, optimizes the team configuration, and finds the "empowering" method of the game by analyzing the opponent data; Google, Microsoft, Opta, etc. pass big data. Analyze and predict the results... Big data not only became the "12th person" on the field, but also played the "predictor" of the World Cup to some extent.
Big data analysis 邂逅 World Cup is an inevitable occurrence of the big data era, and big data analysis will also change every aspect of our lives in the future.
Business outcome
1. Proactive & Predictive Demand: Corporate organizations are facing increasing competitive pressures. They not only need to acquire customers, but also understand customer needs in order to enhance the customer experience and develop long-lasting relationships. By sharing data, customers reduce the level of privacy of their data usage, and expect companies to understand them, form interactions, and provide a seamless experience across all touchpoints.
To do this, companies need to identify multiple identifiers for customers (such as mobile phones, emails, and addresses) and consolidate them into a single customer ID. As customers increasingly use multiple channels to interact with the enterprise, traditional data sources and digital data sources need to be integrated to understand customer behavior. In addition, companies need to provide context-sensitive real-time experiences, which is what customers expect.
2. Buffer Risk & Reduce Fraud: Security and fraud analysis is designed to protect all physical, financial, and intellectual assets from internal and external threats. Efficient data and analytics will ensure optimal levels of fraud prevention and improve the security of the entire organization: Deterrence needs to establish effective mechanisms for companies to quickly detect and predict fraud while identifying and tracking the perpetrators.
Using the statistical, network, path, and big data methodology for predictive fraud-prone models that bring alerts will ensure that responses are triggered in a timely manner after being triggered by the real-time threat detection process, and automatically alert and respond accordingly. Data management and efficient and transparent fraud reporting mechanisms will help improve the fraud risk management process.
In addition, integrating and correlating data across the enterprise provides a unified view of fraud across different lines of business, products, and transactions. Multi-type analysis and data foundations can provide more accurate fraud trend analysis and forecasting, and predict future potential operations to identify vulnerabilities in fraud audits and investigations.
3. Providing related products: Products are the cornerstone of any enterprise's survival, and are usually the areas in which enterprises invest the most. The role of the product management team is to identify trends in driving innovation, new capabilities, and service strategy roadmaps.
Effectively collating third-party data sources of ideas and opinions published by individuals, and then analyzing them, can help companies stay competitive when demand changes or develop new technologies, and accelerate the forecast of market demand. Provide the appropriate product before production.
4. Personalization & Service: The company is still struggling to deal with structured data and needs to quickly respond to the instability caused by customer interaction through digital technology. To respond in real time and make customers feel valued, it can only be achieved through advanced analytical techniques. Big data brings opportunities to interact based on customer personality. This is achieved by understanding the customer's attitude and considering real-time location and other factors to bring personalized attention to the multi-channel service environment.
5. Optimizing & Improving Customer Experience Poor management can lead to countless major issues, including the risk of damaging the customer experience and ultimately reducing brand loyalty. By applying analytics in process design and control, as well as in business operations optimization in the production of goods or services, we can improve the effectiveness and efficiency of meeting customer expectations and achieve operational excellence.
By deploying advanced analytics technology, you can increase the productivity and efficiency of on-site operations and optimize organizational manpower arrangements based on business and customer needs. Optimized use of data and analytics can provide an end-to-end view and measure key operational metrics to ensure continuous improvement.
For example, for many companies, inventory is the largest item in the current asset class—too much or insufficient inventory can directly affect a company's direct costs and profitability. Through data and analysis, uninterrupted production, sales and/or customer service levels can be ensured at the lowest cost, improving inventory management. Data and analysis provide information on current and planned inventory conditions, as well as information on inventory height, composition and location, and can help determine inventory strategies and make decisions accordingly. Customers look forward to getting a seamless experience and letting companies know about their activities.
Asynchronous big data analysis
The big data analysis of asynchronous processing complies with the process of capture, storage and analysis. In the process, the data is acquired by sensors, web servers, sales terminals, mobile devices, etc., and then stored on the corresponding devices, and then analyzed. Since these types of analysis are performed by traditional relational database management systems (RDBMS), the form of the data needs to be transformed or transformed into a type of structure that the RDBMS can use, such as rows or columns, and needs to be compared with other data. continuous.
The process of processing is called extraction, transfer, loading or called ETL. The data is first extracted from the source system, the data is normalized and the data is sent to the corresponding data warehouse for further analysis. In a traditional database environment, this ETL step is relatively straightforward because the objects of analysis are often well-known financial reports, sales or market reports, enterprise resource planning, and so on. However, in a big data environment, ETL can become relatively complex, so the transformation process is different for different types of data sources.
When the analysis begins, the data is first extracted from the data warehousing and placed in the RDBMS to generate the required reports or to support the corresponding business intelligence application. In the big data analysis process, the bare data and the converted data are mostly saved, because it may need to be converted again later.
Our banknote handling machines mainly include following
BC -30: basic piece counter with only one UV
BC-35: piece counter with UV MG
BC-40: value counter with one CIS, support 4 currencies, Auto recognition, multi-currency mix counting
BC-55: value counter with two CIS, support 40 currencies at the same time, serial number reading, Auto recognition, multi-currency mix counting, remote software upgrading via network
Those 4 models have similar structure, passage can be opened from reat side, easy for maintenance and cleaning
BCS-160: 1+1 pockets banknote sorter with two CIS, support 20 currencies at most, serial number reading
Money Counting Machine,Cash Counting Machine,Currency Counter,Multi Currency Banknote Counter
Suzhou Ribao Technology Co. Ltd. , https://www.ribaoeurope.com