The objective of this week's deliverable is to provide Robert M. Lopez with an executive summary of your findings. In a 2-3 page paper, summarize your findings. Give proof for your analysis. Within your paper, explain the process you did to come up with

Analytics Ecosystem

Lisa Garay

Rasmussen College

Authors Note

This paper is being submitted for Anastasia Rashtchian’s B288 Business Analytics Course.

This paper looks at the nine clusters of the ecosystem. Clustering refers to a system of grouping functions that are similar so as to set them out from others. It begins by highlighting them before proceeding to defining them. It then identifies clusters that represent technology developers and technology users. Peer reviewed materials are used in this endeavor.

They include executive sponsor cluster which contains information that concerns administrators for directing the system. Another one is end-user tools and dashboards cluster that is made of functions that facilitate ability of persons to ultimately engage the system. Data owners cluster is made up of programs that are related to persons who have data in the system. Business users’ cluster is made up of functions that are related to clients of the system. Business applications and systems cluster is made up programs related to features of a given system. Developers cluster is made of programs that are related to the development of programs in the system. Analyst cluster is made up of materials that are related to analysis of data in the system. SME cluster that is made up switches that run SME applications in the system. Lastly, operational data stores that are made up of programs that are concerned with storage of data in a system (Pitelis, 2012).

While developers cluster is made up of technology developers in the system, business users’ cluster is made up of technology users in the system. In conclusion, clustering serves to bring roles together as well as separating roles that are not related in a system (Cameron, Gelbach & Miller, 2012).

They can be represented as follows:-

References

Cameron, A. C., Gelbach, J. B., & Miller, D. L. (2012). Robust inference with multiway clustering. Journal of Business & Economic Statistics.

Pitelis, C. (2012). Clusters, entrepreneurial ecosystem co-creation, and appropriability: a conceptual framework. Industrial and Corporate Change, dts008.

Infrastructure

Executive Sponsor Cluster

End-user tools and dashboards cluster

operational data stores

Data Owners Cluster

Business users' cluster

Business systems and applications cluster

Developers Cluster

Analysts Cluster

SME cluster