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Insight as a Service is a cloud computing model, similar to Software as a Service (SaaS). Insight as a Service describes cloud computing-based, action-oriented, analytic-driven applications and solutions that operate on Big Data infrastructure. This differentiates Insight as a Service from other types of cloud computing-based solutions including Data as a Service (DaaS) and Platform as a Service (PaaS). Other "as a Service" (aaS) solutions enable users to generate reports or perform analytics. Insight as a Service strives to link the conclusions from these analyses to a specific action or set of actions. [1]

Types of Applications[edit]

As of May 2012, there are two types of IaaS applications. The first type operates on data that is primarily managed by SaaS, i.e. transactional applications including ERP, HCM, CRM. The second type operates on the data generated by SaaS applications.[1].

Example[edit]

Insight as a Service targets the layer of the cloud computing stack above Analytics as a Service (AaaS)[2]. An example is a cloud-based solution which establishes the attrition score (the potential for a customer to leave a company) of a customer. The solution then goes to the next level and automatically identifies the customers that are most favorable to focus on to help prevent them from leaving. It then recommends the attrition-prevention actions to apply on each target customer and determines the portion of the marketing budget that must be allocated to each set of related actions. To provide these insights into the combined customer data, IaaS applications incorporate a variety of data sources. These include proprietary corporate data, syndicated and open source data, and data generated by other SaaS applications.[1].

History[edit]

As of 2012, specialized personnel could only manually derive insights from large data sets. There is limited scalability to that model because of the small number of qualified personnel who can derive such insights. Oftentimes corporations used specialized personnel, called Connectors, who can translate a business problem into a data problem that can then be operated on by data scientists to uncover any patterns in the data. The Connectors can subsequently evaluate the patterns and develop insights into how to utilize the patterns and develop a plan of action. Finally, business analysts can review the Connectors' recommendations and select an appropriate action to take.[1] The Insight as a Service model aims to automate the "Connector" piece of this process. Reducing, filtering, and processing data streams to deliver relevant and useful information, or to define an appropriate action is crucial to a business that generates massive data sets as part of its business model.[3] The phrase "Insight as a Service" was coined in 2010[4], and the term is becoming accepted by the SaaS community[5].

Companies[edit]

Several companies have developed IaaS solutions including; Acteea, 9Lenses, JBara, Totango, Startup Genome Compass. 8thbridge, Dachis Group, and Host Analytics have created Insight as a Service offerings as a complement to their existing SaaS solutions.

Conclusions[edit]

As of 2012, Insight as a Service is becoming a new layer of analytic applications in the stack of cloud computing models (after Infrastructure as a Service, Platform as a Service, and Software as a Service). Insight as a Service is particularly appealing to organizations that don't have data scientist teams that are able to analyze the massive amounts of data they are generating. As more companies move into the "Analytics as a Service" space[6], and drive the need for insight into the results of "Big Data" analysis, it is possible that Insight-as-a-Service applications will become as essential as SaaS applications. [5]

References[edit]

  1. ^ a b c d Simoudis, Evangelos (8 May, 2012). "Big Data and Insight as a Service". SandHill Group. Retrieved 2012-05-08. 
  2. ^ Hardy, Quentin (1 January, 2012). "Will Amazon offer Analytics as a Service?".  Unknown parameter |Publisher= ignored (|publisher= suggested) (help);
  3. ^ Khosla, Vinod (19 February, 2012). "The "Unhyped" New Areas in Internet and Mobile". TechCrunch. Retrieved 10 May, 2012. 
  4. ^ Simoudis, Evangelos (4 October, 2010). "Insight as a Service". Enterprise Irregulars. 
  5. ^ a b Rosenberg, Dave (February 8, 2012). "Data as a Business Philosophy". CNET. 
  6. ^ Hardy, Quentin (1 May, 2012). "Google offers Big-Data Analytics". The New York Times. Retrieved 10 May, 2012.