Final report

Running head: USE OF SAAS IN DATA ANALYTICS 1

USE OF SAAS IN DATA ANALYTICS 2

Use of SaaS in data analytics

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SaaS (Software as a Service) and data analytics

SaaS seems to be a new model of business that many companies in different industries are adopting so rapidly in the last few years. SaaS is one of the largest in the clouding market, and it is growing very fast Businesses are based on software that enhances the productivity and satisfies the need of the people. SaaS is used in data analytics to enhance the performance of the employees and create good relationships with the customers. It is utilized by Snowflake Company to manage the back office operation, hunt target and provide services needed by the customers. SaaS in the data analytics brings new innovative features as well as information that is optimized for external and internal use by the vendors, partners, and customers (Riggins & Wamba, 2015). The solution provided by SaaS enables the user to make updates of the new functionalities after the developers have released them. It makes it easy for businesses to grow. It utilizes website in the process of delivering applications that are under the management of the third-party vendor, and the clients have access to the interface. The applications of SaaS are based on the business intelligence and the predictive analytics that determine the steps which a business operation should engage for it to be successful. SaaS solutions to the Snowflake Company enable it horizontally and vertically look at the whole business activities and find suitable solutions to problems facing the business.

Additionally, SaaS is also used in data analytics to enhance collaboration where multiple users can work together under the same suite of software and at the same time. The collaboration of the users is very crucial because it promotes teamwork and enables the sharing of the resources. Saas allow Snowflake Company to intelligently save the data which minimizes the chances of losing or leaving some information out (Wang, Kung & Byrd, 2018). The collaboration between the business teams and Information technology team enables the proper coordination of all the activities in the company. The success and increased the productivity of a business is highly depended on the information, data or report from the information technology. The applications of SaaS can be deployed very quickly and does not require the maintenance and management of infrastructure. Teamwork is important for data analytics as it leads to problem-solving and coming up with innovative strategies.

Ways in which SaaS is significant to the data analytics

It reduces the cost of application because the model of business allows users to subscribe and provide solutions based on specific needs. In the business units, SaaS is very popular because of the less hardware cost and minimal starting investment which are needed. The complete functionalities of the business can be controlled because the users can be added or removed at any time. It is one of the clouding services which can be the responsibility of the business operations including the security of the business, performance, and scalability of the enterprise operations. It gives the entrepreneur space to focus on business activities, sales and good relationship with the customers. Similarly, SaaS is used by Snowflake Company because of its flexibility to the users and the business (Yen et al, 2017). In data analytics, flexibility enhances the business possibility of making changes according to the products or services which have more demands. It is an added advantage for a business to have strategies, business model and culture which are flexible. It would be easy to upgrade the system, add more users or add features as SaaS provides an opportunity for features customization, user addition and new business solution which are customized. The users do not have to modify the modules because the infrastructures are under the management of the service providers who ensure that all the activities run smoothly. The application of the SaaS scale without much problem with the user’s perspective and business as well. The design of the SaaS allows the integration of many users working together and adding of the solutions and new service can take place without forcing the software to work. It is easy to share information between different teams from any device and at any time. This promotes data analytics that enables the Snowflake Company to share information and work together as a time.

Expected Cloud Service Provider

The expected Cloud Service Provider is Apogaeis provide services to the customers at a global level. The company offers data security services to the business, resource optimization, exposures the customers to the latest software, handles the issues of customer relationship management services and data storage (Abbasi, Sarker & Chiang, 2016). It is one of the service providers who help the business to save their time and reduce the cost which could be made on investments. The cloud solutions provided by the Apogaeis are cost-effective, industry-specific and robust. The SaaS solution involve minimal risks, can be deployed quickly and the cost is usually low.

References

Abbasi, A., Sarker, S., & Chiang, R. H. (2016). Big data research in information systems: Toward an inclusive research agenda. Journal of the Association for Information Systems, 17(2).

Riggins, F. J., & Wamba, S. F. (2015, January). Research directions on the adoption, usage, and impact of the internet of things through the use of big data analytics. In System Sciences (HICSS), 2015 48th Hawaii International Conference on (pp. 1531-1540). IEEE.

Wang, Y., Kung, L., & Byrd, T. A. (2018). Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological Forecasting and Social Change, 126, 3-13.

Yen, I. L., Bastani, F., Huang, Y., Zhang, Y., & Yao, X. (2017, June). SaaS for Automated Job Performance Appraisals Using Service Technologies and Big Data Analytics. In Web Services (ICWS), 2017 IEEE International Conference on (pp. 412-419). IEEE.