Table of Contents
This is an age of optimization. Consumers want their data to be easily accessible, available in real time, and on multiple devices. Enterprises want real-time data analytics to be a systemic part of their business models. In industries such as retail, information technology, marketing, healthcare and banking, real-time data analytics is essential for daily functioning of a business.
In the enterprise space, there are two key factors enabling this push towards data analytics – consumption patterns and big data. Enterprises that are consumer facing find that data analytics is necessary to predict consumption patterns and consequently dictate business decisions. Secondly, with the advent of big data, there are copious amounts of data that need to be analysed and shared across industries more efficiently. This is being looked at as an avenue for adding more value to their customers and to identify new streams of revenue. While data mining techniques are efficient, there are several limitations which can be overcome by providing analytics in the cloud.
This market insight evaluates how cloud computing can enhance the benefits of analytics.
Analytics in the Cloud
Cloud analytics is a service that provides data and predictive analytics through a public or a private cloud model. The intersection between analytics and cloud technologies will create opportunities for companies to process and use big data on a large, secure scale. These are provided through cloud models – Infrastructure as a Service (IaaS)-based data warehouses, Software as a Service (SaaS)-based intelligence tools, and other analytic products that are hosted on the cloud. While cloud analytics provides the same offerings as traditional analytics, it enhances the service by integrating the attributes of cloud computing.
- Rapid Deployment: Cloud computing brings rapid deployment to analytics. Through IaaS and SaaS solutions available in the market, enterprises can quickly choose and deploy the solution of choice and start using it in a matter of hours to minutes. Time taken to migrate data into the cloud and setting up the reports are the major bottlenecks in the pace of deployments. This also allows for rapid returns on investments.
- Scalable and Agile: As cloud services are highly scalable, analytics services can be scaled up or down depending on demand. This will enable enterprises to efficiently perform their activities as traditional providers require long term licenses, while companies may require options that allow for scaling dependent upon requirements (for example, big project versus small projects).
- Consolidation of Data in the Cloud: For companies that have a share of their data stored in the cloud, it is the obvious choice to have analytics in the cloud as well. This would eliminate the need for conversions of traditional data and would consolidate data.
- Collaboration with Users: One huge benefit of cloud analytics is the ability to collaborate with users by providing them with partial/specific server access to be able to view the project being collaborated on. This is easier than providing external users access to the company’s network, especially with firewall concerns.
- Mobility: Having data analytics on the cloud would provide users with an option to log on to view the data from their mobile devices with secure access. This is especially useful for users that need to access this data out of office and hence not on the companies’ firewall – especially those using portable devices (tablets and mobile phones).
- Speed: Data in the cloud can be analyzed at a much faster speed, not being contained by small office bandwidths. It will also be easier to disseminate findings to users through various devices as they would all be able to log onto the cloud simultaneously.
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