Nine ways to get value from big data in the age of big data, there are now many cases of using big data to get business value, we can refer to these cases and use them as a starting point, we can also dig out more gold mines from big data. Last year's TDWI survey on managing big data showed that 89% of respondents saw big data as an opportunity, compared to 70% in a 2011 survey on big data analytics. Respondents in both surveys generally agreed that advanced analytics are needed to capitalize on the Big Data opportunity and derive business value from it. In addition, other ways to derive business value from big data include data exploration, capturing big data as it flows in real time, and integrating new sources of big data with existing enterprise data. While many people have come to the realization that Big Data will present us with a new business opportunity. However, there are only a few companies that can actually derive more business value from Big Data. Below are nine big data use cases that we can refer to when conducting big data analytics projects to better get the value we want from big data.1. Explore big data to discover new business opportunities. A lot of big data comes from some new source, which represents a new channel for customer or partner interaction. As with any new source of data, big data is worth exploring. Through data exploration, you can learn some previously unknown business models and truths, such as new customer base segments, customer behaviors, forms of customer churn, and root causes of least cost, etc. 2. Derive business value from data analytics. Note that there are some advanced data analysis methods involved here, such as data mining, statistical analysis, natural language processing, and extreme SQL, to name a few.3. Analyze the big data that has been collected. Many companies have collected a large amount of data that they feel has business value, but do not know how to get out of these get out of value big data. Different industries have different data sets, for example, if you are in the Internet marketing industry, you may have a large number of Web site log data sets, which can be divided into data by session and analyzed to understand the behavior of site visitors and improve the site's access experience.4. Focus on analyzing the big data that is valuable to your industry. The type and content of big data varies by industry, and the value of each type of data is different for each industry. For example, call detail records (CDRs) in the telecom industry, RFID data in retail, manufacturing, or other industries centered on the mouth of production, and sensor data from robots in manufacturing (especially in automotive and consumer electronics), to name just a few, are all very important data in each industry.5. Use social media data to extend existing customer analytics. Customers' various behaviors such as reviewing brands, evaluating products, participating in marketing activities or expressing their preferences, etc., will interact with each other in the customer. Social big data can come from social media sites, as well as own channels where customers can express opinions and facts. We can use predictive analytics to discover patterns and predict problems with products or services. We can also use this data to assess market awareness, brand reputation, user sentiment changes and new customer segments.6. Understanding unstructured big data. Unstructured information mainly refers to human language expressed using text, which is very different from most relational data, and you need to use some new tools for natural language processing, search and text analysis. Visualize business processes based on textual content.7. Integrate customer input into big data. Through the use of big data (integrated with the original enterprise resources), we can achieve a 360-degree panoramic analysis of customers or other business entities (products, suppliers, partners), and the dimensional attributes of the analysis are expanded from hundreds to thousands. The added granular details bring more accurate customer segmentation, direct marketing strategies and customer analysis. 8. Analyze big data streams to operate the business in real time and improve business actions. Real-time monitoring and analysis of the program has been in business operations for many years, those who need to run 24/7 energy, communications networks or any system networks, services or facilities of the organization has long been using this type of program. More recently, from the surveillance industry (cybersecurity, situational awareness, fraud detection) to the logistics industry (road or rail transportation, mobile asset management, real-time inventory), more and more organizations are taking advantage of the application of big data streams.9. Integration of big data to improve the original analytics applications. For the original analytic applications, Big Data can expand and extend its data samples. Especially in the case of analytics that rely on large samples, such as statistics or data mining; and in the case of fraud detection, risk management, or precision computing also have to use large samples of data.