Software as a Service
The shared responsibility model of cloud deployments identifies the level of responsibility that is shared between the provider and the consumer. Diagram 1 shows the far spectrum of the shared responsibility model from On Premise to Software as a Service (SaaS).
Diagram 1: Shared Responsibility Model
Under the on-premises option, all infrastructure pieces are “dedicated”, or managed by the company itself. On the other side of the model, the SaaS option is mostly shared, where the responsibility is under the chain of command of the cloud service provider. The SaaS model effectively places the entire responsibility on the vendor who is providing the solution (Knorr, 2018). The user interface, user identity management, underlying infrastructure, the entire experience - is managed by the vendor or service provider. Predictive Analytics Today, a research forum of analytic solutions, has a detailed evaluation of 32 SaaS products and gives each of the products two separate scores - one score for an editor rating and one score for an aggregated user rating (“32 Cloud Analytics Software in 2020”, 2020). The total sources include a wide variety of internal sources for both the editor and user ratings. Using these scores, they have ranked these providers and solutions from 1 to 32. Number one is Sisense, with Periscope Data, Microsoft Power BI, and IBM Cognos Analytics rounding out the top 4 solutions. This list of analytic solutions provides robust self-service capabilities for reporting and dashboard creation. However, a major reason why companies shy away from these solutions can be the difference in capabilities the SaaS solution actually offers compared to what the business needs to be optimally efficient (Harvey, 2017). Moreover, IT might not have audit capabilities into exactly which users are utilizing these SaaS offerings, which becomes a significant barrier to entry for some companies.
Brian Dye, Corporate Vice President at Intel, is a leader who is pushing the organization to provide SaaS solutions that are continually improving. At a 2016 security convention, Dye was quoted as saying he did not have “one customer meeting at [this] show where the cloud discussion was not addressed as a business priority” (Dunkel, 2016). The seminar further outlines the use of big data and cloud computing solutions as preventive tools against cybercrime and terrorism - the characteristics of long-term product innovation for security systems and the need for a network bandwidth for big data analytics.
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