Use Case for large data

The Big data platform is about all the businesses that want to create a big data strategy and match the data they have. Enterprises must understand how to use large data within an enterprise. To this end, we will be in this article for you to introduce enterprise large data use of the eight typical cases. Of course, if your business has more and better cases, also welcome through the article comments to communicate with us.

1, real-time understanding of your customers

In the past, we always tended to find out where our customers were by using groups and questionnaires. When the findings are summed up, the results are often outdated. With large data, this situation will not happen again. Big data can help companies fully outline their customers ' DNA. Fully understand the customer is effective with the customer to achieve business cooperation key. Of course, in this process, if not very good to protect the privacy of customers, it is easy to go to extremes.

But if companies can ensure that their customers ' privacy is not compromised, large data can provide a very personalized view of individual customers. Using interconnected social media data, mobile data, network analytics, and other data analysis, companies can fully understand every customer, know what they want in real time, and when they want it.

Truly understanding your customers means that you can combine the personalized features of your customers to give targeted advice or display ads. Amazon has already done this at its best, and the products they recommend for their customers are by no means a coincidence. Amazon's recommendation engine is based entirely on what customers have done in the past: the items they collect in their shopping carts, the products their customers like, and the items they browse or buy. Amazon uses the algorithm to customize the individual homepage for each customer. The strategy was that the company's sales grew 27% per cent during its third fiscal quarter, to $13.18 billion trillion, compared with 9.6 billion dollars in sales over the same period last year.

2, the enterprise creates together, the real time improvement and the innovation product

In the past, we had to meet with clients, discuss with them what they wanted, and show them our final product. If the customer doesn't like it, you'll be in trouble. And by using big data, it has become a history of the past.

Large data analysis can help companies better understand the products customers want. By collecting from social media and blogs how people evaluate a product, they can provide more information than the traditional questionnaire survey. Especially when the information is collected in real time, the enterprise can make an immediate and specific improvement to the possible problems. This will not only be a good assessment of your product, but also can collect different population groups or different places of people for a product evaluation.

Large data can also help businesses run thousands of real-time simulations simultaneously, testing new products or improving digital products. With scalable computer power, combined with simulation algorithms, you can run and test thousands of different changes at the same time. Each design needs a little adjustment, the emulator can combine all the small changes to show a product improvement.

Internet offline products, if you know how to operate and perform the responsibility, can also use large data to make improvements and innovations such as automobiles. Ford has opened a laboratory in Silicon Valley to improve the development of automotive products. To improve the quality of cars, reduce fuel consumption, improve safety and reduce emissions, Ford has collected data from more than 4 million vehicles, including car sensors and remote application management software. All data is real-time, allowing engineers to analyze problems in real time, and to understand how cars travel under different roads and weather conditions, as well as any other factors.

3, determine how much risk your business faces

Identifying the risks your business faces is an important aspect of today's business. To identify a potential customer or vendor risk, you need to classify the customer or vendor in a specific category, with each customer or vendor having its own level of risk. More often, if your customer or vendor is categorized into a wrong category, it will undoubtedly lead to the risk of error. A profile with too high a risk is not very harmful, except that your business loses revenue, but too low a risk can sometimes cause serious damage to your business. Large data can be used to identify risk categories for each customer or vendor's past and current real-time data.

Especially in the insurance industry, predictive analytics can be used to determine how much money a customer might spend in the future. It can help insurance companies to provide customers with the most appropriate insurance products at the most appropriate time and place at the right price to ensure that the cost of the claim is reduced and customer fraud is avoided. The use of large data technology, such as pattern recognition, regression analysis, text analysis, social data aggregation and emotional analysis (through natural language processing or monitoring social media) 360 degrees no corner of the investigation a potential customer. This overall analysis can significantly reduce risk. Such a 360-degree analysis can, of course, be used to identify potential risks for a new or existing supplier. This task is a priority for many financial institutions in the coming years.

(Responsible editor: The good of the Legacy)