When big data analytics and cloud technology double swords

The advent of big data made business intelligence really go into the 21st century. But the fact that the word "big data" represents is not a solution, but a kind of problem. What value does it hide in these petabytes of data? What can we get from it, and make it guide all aspects of the business deployment. But this huge amount of data actually does not have much to be useful. So in order to take advantage of its hidden value, enterprises need to collect, filter, and analyze it through affective analysis applications, positioning tools and other technologies to generate useful information, which will serve the future business development.

Cloud can be used as an enabling device for big data analysis

Forrester defines big data as "the technology and skills to acquire data in large-scale economics." "One of the most critical words here is the economy. If the cost of extracting, processing, and using 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, helping most businesses to exploit this data. Cloud technology, whether public, private, or hybrid, is an integral part of enabling businesses to extract potential ROI from Big data analytics.

Collect and worry about

There is little that is available in the huge amount of data that has been mentioned before, but there is still a lot of data in the BI product that needs to be over-correlated and stored for its usefulness later. There is little interest in investing in infrastructure, where temporary information is stored, because most of this temporary data will be discarded. In addition, the data from the outside of the corporate firewall to the internal network will not receive any valuable information, and processing it is also a headache for the use of IT managers.

This phase of big data filtering is a perfect public cloud platform application that provides on-demand scaling of compute and storage resources.

Analysis

Once the data is converted into usable form, it goes into the stage of analyzing the information generated. In the long run, the raw data provided to the analysis application is not necessary to retain, and effective storage is the result of analysis processing. Public and hybrid cloud technologies can be used in the analysis phase to introduce Hadoop or similar alternatives during the data set processing phase. In the case of public cloud users, the original analysis phase can be performed on the public cloud infrastructure and then used by private cloud components to get processed, usable information inside the company.

Virtualization, integration, and collaboration

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, transforming and dwelling into existing systems, such as enterprise resource planning and customer resource management applications. Software-as-a-service applications run in the cloud, leveraging data developed earlier to enhance integration and enable users to collaborate with each other.

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

When big data analytics and cloud technology double swords