The General Electric is a multinational conglomerate corporation based in New York and headquartered in Boston, Massachusetts, United States (Ge.com., 2018). The conglomerate corporation is defined as a large corporation formed by merging of different and distinct parts or items. The report focuses on the case study of General Electric that has proposed a project Industrial Internet. There are diagrams also which demonstrates the scenario of the case study. The purpose of the report is to explain General Electric case study on Industrial Internet project with reference to Enterprise Information Architecture and diagrams. The cloud options and suitability are also discussed.
The structure of this report is a brief explanation of General Electric and details of the proposed project through Architecture Overview Diagram and Component Relationship Diagram. The other sections of this report are strategies for integration of information management and cloud infrastructure strategies.
The General Electric Corporation is a big player and holds a reputable position in market. The Industrial Internet is proposed By General Electric to have powerful operations that can immediately identify system failures and downtime (MIT Sloan Management Review., 2018). The project can also withstand high risk situations such as life-threatening situations and provide safety. The following sections will discuss the General Electric Corporation details and the overall scenario of the Industrial Internet project with reference to Enterprise Information Architecture templates. The cloud infrastructure strategy and strategies for services and applications integration are also discussed in details to analyze the project.
Enterprise Information Architecture Reference Architecture (EIA RA)
The below two diagrams are Enterprise Information Architecture models which explain the General Electric case study on Industrial Internet.
Architecture Overview Diagram
The General Electric project Industrial Internet is presented through building blocks that help to build and develop the project. The common functional capabilities are described through Architecture Overview Diagram for future business prospective. The Architecture Overview Diagram is designed according the business needs for General Electric project Industrial Internet. The Architecture Overview Diagram shows the capabilities that are required to achieve the objective and the required goal of Industrial Internet project in General Electric (Chang, 2016). The goals which are required to be achieved are described briefly. The first goal is real-time monitoring and acquisition of data through the Industrial Internet project to analyze the environment outside and inside of General Electric. The second goal is real-time analytics of data that are necessary to build and develop Industrial Internet project within General Electric. The third goal is to integrate predictive business analysis and purposes for future prospective and effective operation of Industrial Internet for the General Electric business growth. The fourth goal is to properly present Industrial Internet project to the General Electric management department for authentication and authorization. The fifth goal is to finally present to the customer to assess the impacts of Industrial Internet on the business growth of General Electric.
Figure 1: Architecture Overview Diagram
(Abstracted from week 2 lecture slides)
The diagram above illustrates the goals and objectives of Industrial Internet that is to be presented to the General Electric Management department.
There are three major sections and they are finance department, Human Resource department and Data warehouse. The finance department is responsible for financial planning and upgrading the components of Industrial Internet project (Ahmad and Brosio, 2015). Cost Maintenance and assessment of profit are also looked by this department from the beginning to end of the project. The Human Resource department is responsible for acquisition of appropriate workforce and evaluating them for different departments (Whetzel and Wheaton, 2016). The overall scenario is briefly explained to the appropriate workforce by the Human Resource Department. The Data Warehouse is responsible for managing transaction including data history maintenance, data quality improvement and adding values to business growth (Stein and Morrison, 2014).
Component Relationship Diagram
The component relationship diagram describes the relationships among the components presented in the diagram. The below diagram illustrates the relationships among the various components of Industrial Internet project. The system developer can easily identify the functionality and operation of the information system through this diagram for Industrial Internet. The diagram is derived by architecture overview diagram, in other words the component relationship diagram is the next phase of architecture overview diagram (Galliers and Leidner, 2014). The component relationship diagram depicts the development of Industrial Internet project with reference to connections between the components. The diagram has all the components and connections between the components that are required to deliver functionality and operation to General Electric. It consists of components implementation, components name, composition interoperability, and finally the evolution, customization and deployment of the components through the diagram.
Information Management and Integration
The adoption of data analytics are used for decision making purposes for every organization. General Electric needs to have insights from data in areas such as sales team, finance team, marketing team, procurement team and other teams relevant to the Corporation. These departments have its own targets that are separated and individual to each team. The targets of individual team results in success and analysis of data structure to ensure security and privacy. Each department has concern for success of its department and the information is secured from other departments also. This helps in integrity and security. The working and function of each department can be described by following examples such as marketing team has concern related to number of leads generated (Shmueli et al., 2015). The next example is sales team which has concern related to meeting the targets of the business operations. The other example is finance team which has concern related to financial condition of business and cost maintenance. Another example is information technology that has concern related to proper and effective use of data analytics for the project. The data sources for General Electric are such as web data, marketing automation data and CRM data which are generated. The other data sources are such as media, cloud, Internet of Things and databases which are generated within the company (Talia, 2013). The media sources include Google, Facebook, Twitter, YouTube and Instagram. The cloud storage includes structured and unstructured data from various types of cloud options. The web data source includes data from websites that are free and that are paid. The Internet of Things sources include machine generated data (Jaseena and David, 2014). The databases data sources are structured data generated form company’s databases.
The data integration comes under data strategy components. This is explained as the five components compose data strategy and they are identification, storage, provision, integration and governing. The integration strategy is explained as integration of data after going through identification, storage of relevant data, provisioning of data among workforce of company (Baesens, 2014). The integration is costly as it includes moving and combining of data across the information system, and extraction and loading of data. The data integration is seen as challenging however it needs to be deployed effectively.
Application and Data Storage Infrastructure Design
There is a need to have cloud storage as it is an asset for every organization. The cloud storage models are based on different models that are service models and deployment models. The service models include IaaS, PaaS and SaaS and deployment model include private, public and hybrid cloud (Rittinghouse and Ransome, 2016). The private, public and hybrid cloud are defined as the clouds suitable for different purposes of organization’s operations. The private cloud provides services that are managed by the company whereas public cloud provides services that can be access by the company over the internet. The hybrid cloud has functions of both the services of private and public cloud. IaaS is used for services on rent that are provided by external providers for business purposes. SaaS is used for accessing on the internet using browser for business operations. PaaS is used by combining the IaaS and SaaS service models.
The cloud storage models suitable for company’s function and operation are based on the provided service by the three models. SaaS is used as installing, managing and updating without human effort and thus cost is reduced. PaaS is used as an environment where the services can change when required. IaaS is used to control applications when there is a need of scaling up or down related to traffic networks in the system.
General Electric adopted cloud storage due to some reasons and they are speed-oriented services, security and privacy, lower costs, scaling ability, global visibility and failure of isolation. The company introduced Industrial Internet and developed its own software, Predix, for deploying the project (Predix: The Industrial Platform Advantage., 2016). The cloud infrastructure strategy requires some points. The first is cloud storage has security and legacy system issues in spite of advanced technology, thus it should be looked upon before deployment. The second is high agility, scalability and support is required to have greater business operations (Mishra et al., 2013). The third is to manage the overall operation of business working such as recruitment and retaining of customers. The fourth is management of cost for adoption of cloud storage. The fifth is evaluating the risks of cloud storage within the company for future purposes.
Application and Service Integration
The strategies for integration of application and services are described in the following discussions. The application integration strategies are automating business process and composite application for business improvements (Bussler, 2013). The automating business process or in other terms business to business integration where two companies deal on specific purposes. This process provides common gateway for communicating and working on suitable purposes. The benefits of automating business processes are fast, cheap, accurate and visible within the organization. The composite application has benefits of cross-application functions to do jobs efficiently and effectively. The service integration has strategies and they are addressing systematic and effective procedural barriers for business collaboration process (Charter and Tischner, 2017). The service integration provided by service provider is necessary to meet the client’s requirements. The agreement of each and every person of the company is needed to establish service integration. The final strategy is to develop a model for service and application integration planning to be presented to higher authority.
The above strategies for service and application integration are done to meet the requirements of General Electric Company to develop Industrial Internet.
Conclusion
The above discussions conclude that there is need of implementing the above strategies in order to get a useful insight of Industrial Internet project internally and externally in General Electric. The implementation of the Enterprise Information Architecture models as explained in the discussion through diagram, necessary to gain the knowledge and understanding of the information system. The report also focuses on various factors of implementation and integration strategies of applications and services in information system. The proposed project is valuable and effective if above strategies are implemented properly. The project can achieve security, speed and real-time monitoring and analysis for long term use in future. The cloud options are also discussed which concludes that there is a need of deploying cloud infrastructure with proper strategies. Therefore, it can be concluded that the project discussed in this report need to be properly and efficiently deployed to gain understanding and knowledge of the project
References
Ahmad, E. and Brosio, G. eds., 2015. Handbook of Multilevel Finance. Edward Elgar Publishing.
Baesens, B., 2014. Analytics in a big data world: The essential guide to data science and its applications. John Wiley & Sons.
Bussler, C., 2013. B2B integration: Concepts and architecture. Springer Science & Business Media.
Chang, J.F., 2016. Business process management systems: strategy and implementation. CRC Press.
Charter, M. and Tischner, U. eds., 2017. Sustainable solutions: developing products and services for the future. Routledge.
Davenport, T.H. and Dyché, J., 2013. Big data in big companies. International Institute for Analytics, 3.
Gal, A., 2015, August. Big data integration. In Keynote speech at international conference on open and big data (OBD 2015).
Ge.com., 2018. Our Company. [online] Available at: https://www.ge.com/in/about-us/building [Accessed 6 Jan. 2018].
Jaseena, K.U. and David, J.M., 2014. Issues, challenges, and solutions: big data mining. NeTCoM, CSIT, GRAPH-HOC, SPTM–2014, pp.131-140.
MIT Sloan Management Review., 2018. GE’s Big Bet on Data and Analytics. [online] Available at: https://sloanreview.mit.edu/case-study/ge-big-bet-on-data-and-analytics/ [Accessed 5 Jan. 2018].
Predix: The Industrial Platform Advantage., 2016. [ebook] General Electric. Available at: https://www.ge.com/digital/sites/default/files/Predix-Platform-Advantage-Infographic.pdf [Accessed 6 Jan. 2018].
Rittinghouse, J.W. and Ransome, J.F., 2016. Cloud computing: implementation, management, and security. CRC press
Shmueli, G., Bruce, P.C., Patel, N.R., Yahav, I. and Lichtendahl Jr, K.C., 2017. Data Mining for Business Analytics: Concepts, Techniques, and Applications in R. John Wiley & Sons.
Talia, D., 2013. Clouds for scalable big data analytics. Computer, 46(5), pp.98-101.
Whetzel, D.L. and Wheaton, G.R. eds., 2016. Applied measurement: industrial psychology in human resources management. Routledge.
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