However, the new benefits of big data analysis are speed and efficiency. A few years ago, an enterprise could collect information, run analysis and dig out information that could be used for future decision-making. Now, enterprises can make decisions immediately based on visual data, and react faster to maintain agility, which provides enterprises with an unprecedented competitive advantage.
Why is big data analysis important?
Big data analysis can help enterprises use their data to seize new opportunities. Excellent data analysis will bring smarter business processes, more effective operations, higher profits and more accurate customers. So, what is the value of big data analysis? Let's see:
1. Reduce costs. Big data technologies such as Hadoop and cloud-based analysis have brought significant cost advantages in storing a large amount of data. In addition, they can also determine more efficient business development methods.
2. Make faster and better decisions. With the speed of Hadoop and memory analysis and the ability to analyze new data sources, enterprises can immediately analyze information and make decisions based on what they have learned.
3. New products and services. The ability to measure customer needs and satisfaction through analysis can provide customers with what they want. Davenport pointed out that with the help of big data analysis, more and more companies are developing new products to meet the needs of customers.
Working principle and key technologies
Big data analysis requires multiple technologies to work together to help you get the most value from your information. The following are the key technologies and related principles:
Machine learning. Machine learning is a specific subset of artificial intelligence, which trains machine learning methods. It can quickly and automatically generate models, which can analyze larger and more complex data and provide faster and more accurate results, even very large-scale models. By establishing accurate models, enterprises can better identify profitable opportunities or avoid unknown risks.
Data management. Before analyzing data reliably, it needs to be managed with high quality. With the continuous inflow and outflow of data from enterprises, it is very important to establish a repeatable process to establish and maintain data quality standards. Once the data is reliable, the enterprise should establish a master data management program to make the whole enterprise on the same page.
Data mining. Data mining technology can help you check a large amount of data to find patterns in the data-this information can be used for further analysis to help answer complex business questions. With the help of data mining software, you can filter out all the chaotic and repetitive noises in the data, find out the relevant contents, and use this information to evaluate the possible results, thus speeding up the pace of making wise decisions.
Hadoop. This open source software framework can store a large amount of data and run applications on commercial hardware clusters. Due to the increasing amount and variety of data, it has become a key technology to carry out business, and its distributed computing model can quickly process big data. Another advantage is that Hadoop's open source framework is free and uses commodity hardware to store a large amount of data.
Memory analysis. By analyzing the data in the system memory instead of the data on the hard disk, you can gain instant insights from the data and take quick actions. This technology can eliminate the waiting time of data preparation and analysis processing to test new scenarios and create models; This is not only a simple way for enterprises to keep agility and make better business decisions, but also enables them to run iterative and interactive analysis schemes.
Predictive analysis. Predictive analysis technology uses data, statistical algorithm and machine learning technology to determine the possibility of future results according to historical data. It provides the best assessment of what will happen in the future, so enterprises can feel more confident that they are making the best business decisions. Some of the most common applications of predictive analysis include fraud detection, risk, operation and marketing.
Text mining. With the help of text mining technology, you can analyze text data from the Web, comment fields, books and other text-based sources to discover insights that have never been discovered before. Text mining uses machine learning or natural language processing technology to organize documents, which helps you analyze a large amount of information and discover new topics and terminology relationships.