Large data combined with cloud technology intelligent world

The advent of large data enabled business intelligence to truly enter the 21st century. But the fact that large data words represent not a solution, but a kind of problem. What values are hidden in these petabytes of data, and what we can get out of it, and to guide all aspects of business deployment. But this huge amount of data is actually not very useful. Therefore, in order to use its hidden value, enterprises need to collect, filter, and through the emotional analysis of the application, positioning tools and other technologies to analyze it to generate useful information, so as to serve the future business development.

The cloud can be used as an enabling device for large data analysis

Http://www.aliyun.com/zixun/aggregation/13534.html ">forrester defines large data as “ technology and skills for acquiring data in a large-scale economy. ” The key word here is economics. If the cost of extracting, processing, and utilizing data exceeds the value of the data itself, then this work is meaningless. Fortunately, as the volume of data continues to grow, technology is evolving to help most businesses use the data. Cloud technology, whether public, private, or mixed, is an integral part of getting businesses to extract potential ROI from large data analysis.

As mentioned earlier, there are very few available parts of the massive data, but there is still a large amount of data that needs to be overdone and then associated and stored for usefulness. There is little interest in investing heavily in infrastructure that stores temporary information, since most of this temporary data will be discarded. Also, data that moves from outside the corporate firewall to the internal network does not get valuable information, and handling it is also a headache for using IT managers.

This phase of the large data filtering is a perfect public cloud platform application, it can provide on-demand expansion of computing and storage resources.

Once the data has been converted into usable form, it enters the phase of analysis that generates information. In the long run, the raw data provided to the analysis application is not necessary for retention, and the need for effective storage is the result of the analysis process. Public and hybrid cloud technologies can be used in the analysis phase to introduce Hadoop or similar alternatives in the dataset processing phase. In the case of a public cloud user, the original analysis phase can be performed on the public cloud infrastructure, and then the private cloud component is used to bring processed and available information to the company.

At this stage, we actually have the information available that can be used to guide decisions. This is not the end, but also to make this information available to users, transform and shelter into existing systems, such as enterprise resource planning and customer resource management applications. Software, the service application, runs in the cloud, uses data developed earlier to enhance integration, and allows users to collaborate with each other.

With cloud computing technology, the value of large data can be better transformed. It has to be said that the cloud is a fairly perfect platform for converting data into business.