ESG: Large Data and cloud

The core of "big data" analysis is to grab value from a lot of detailed data. The cloud computing infrastructure, combined with sophisticated analytical tools, enables organizations to gain the full value of the above, without being limited by extensibility. ESG 2011 surveyed 611 senior IT consumers about their IT consumption intentions for enterprises using public cloud computing services, especially software SaaS. In addition to measuring application levels, ESG investigates current and planned users in terms of the specific application delivery or expected delivery mode-that is, through the SaaS model. Although Business Analytics is a bit more in the IT consumer intent list, Figure 1 shows that the current and planned user share of these applications as managed Third-party Online services is fairly high in large enterprises.

Figure 1. An application that an enterprise has deployed or expects to deploy through the SaaS model, based on the size of the enterprise

A further examination of this research reveals that it is clear that enterprises managing massive amounts of data are more likely to invest in Business Analytics solutions as hosted online services. More importantly, 1/3 per cent of the respondents with at least 100TB of disk capacity are using or planning to use software as a service product to host their data analysis platforms, compared to 13% of companies with less than 25TB of data. (See Figure 2) The current analysis infrastructure of these enterprises may have some limitations. Externally deployed options help ease the burden of this data management from a system and administrator perspective.

Figure 2. The use or planning of business analysis based on SaaS, based on the total amount of disk storage

Cloud storage services can be said to be a cheaper option if they are purely from storage capacity, but in practice it is quite expensive to use the data stored in the cloud. On the other hand, the concept of "big data" cloud services should be more meaningful. Analysis of "copy" production data and transfer of these copies to the remote site, over time, produced a considerable amount of data. Keeping remote backups of data (and being optimized) in a bad economy can help IT operations focus on their primary initiatives, in other words, the interplay of production data. In addition, because the computational power required to execute these queries is short-lived, the Enterprise is able to lease CPU cycles when necessary to query remote data to meet the needs of analysis. It is also noteworthy that, in many cases, the analysis is often directed at business processes or departments, or in some short-term research projects, to provide the business platform services mentioned above, which means that it does not need resources to support systems that require different requirements because of different projects.

ESG research shows that while large data itself is not a compelling reason to analyze customers ' turn to cloud, there is a strong link between local storage capacity and interest in the SaaS Business Analytics platform. Vendors offering these solutions will also need to address issues related to infrastructure reliability, security, and network performance. Protecting sensitive data, whether it is transmitted on the network or stored on a system, is important for any cloud product, especially data that includes personal identity information such as credit card or social Security numbers. Similarly, products that can load large amounts of data and quickly analyze data are also a big selling point for any managed data analysis service.

(Responsible editor: The good of the Legacy)