General Electric Company (GE) is an international conglomerate in the digital industrial sector. The company was incorporated in New York in 1892 while its headquarters are in Boston. As of 2018, GE operated in power generation, aircraft engine, oil and gas production, healthcare, transportation, venture capital and finance, lighting, and additive manufacturing as its major segments (Bose, 2008). By Dec 2016, GE was operating in approximately 180 countries around the world (Denic et al., 2016). In 2018, Fortune 500 ranked General Electric among the top 20 largest firms in the U.S by gross revenue. Unfortunately, despite the huge success of the company, it has continued to face challenges during the last decade. Last year, the company reported a record drop in stock price falling more than 40 percent in the previous year. The problems faced by the company are as a result of bad management by the retired CEO Jeff Immelt. While the company started improving late last year, I believe the use of Business Intelligence tools would restore GE back to its former glory.
Benefits of Using Business Intelligence Tools
There are several benefits that GE could gain from using Business Intelligence tools. For instance, the company would be able to get valuable insight into its business operations (Kohtamaki & Farmer, 2017). This is particularly so because, unlike other American manufacturers such as General Motors, and Apple Inc., GE does not depend on a single product or industry. Using BI tools will help the company to generate reports to guide decisions regarding aspects such as expenses, staffing, operation processes from all its segment. Secondly, BI tools would indispensable in tracking, managing, and implementing performance goals for the whole organization as well as its various divisions. This would help identify the divisions that are pulling down the company profits (Massaro et al., 2019).
Datasets for GE BI Analysis
Because the company has a wide portfolio of investment, it would require various datasets for almost all the industries it conducts operations. For example, airport and airline data are crucial for evaluation of the airline segment, World Bank Open Data contains a huge number of economic and development indicators from across the globe, stock market data is essential including End of Day and Historical Stock data for the analysis of the performance of the company's stock over the years.
Business Intelligence Tool for General Electric
Tableau is the best fitted BI toll for GE because of its unique features. For instance, it is a pioneer of drag-and-drop analytics where users can evaluate different datasets using a simple-drag-and-drop mechanism across various dashboards. Currently, Tableau is considered the most innovative BI tool in the market. Additionally, the software allows dashboard-to-dashboard interactions are giving the users plenty of iteration and development possibilities. Finally, its Security Assertion Markup Language (SAML) feature makes it possible to create a single log-on experience. The feature is handy for the company given its many divisions and departments (Schmidt et al., 2018).
By using BI tools, General Electric can revive its operations to recover from its losses over the last few years. Because the company covers multiple industries, the analysis can be useful to provide services tied to the products of its manufactures. Real-time analytics will improve the efficiency of the company by enabling machines to adapt continually by minimizing downtime caused by failures. Finally, the company can expand its investment portfolio further by evaluating business opportunities from the industrial internet.
Bose, R. (2008). Competitive intelligence process and tools for intelligence analysis. Industrial management & data systems, 108(4), 510-528. https://doi.org/10.1108/02635570810868362
Denic, N., Vujovic, V., Filic, S., & Spasic, B. (2016). Analysis of key success factors for business intelligence systems implementation. Structure, 42, 30. Retrieved from https://pdfs.semanticscholar.org/3ef9/1a38f7b0b82553e2c6d612bc88f38ce5fbd9.pdf
Kohtamaki, M., & Farmer, D. (2017). Real-time Strategy and Business Intelligence.
Massaro, A., Vitti, V., Galiano, A., & Morelli, A. (2019). Business Intelligence Improved by Data Mining Algorithms and Big Data Systems: An Overview of Different Tools Applied in Industrial Research. Retrieved from http://www.hrpub.org/download/20181230/CSIT1-13512271.pdf
Schmidt, A., Sousa-Zomer, T. T., Andrietta, J. M., & Cauchick-Miguel, P. A. (2018). Deploying Six Sigma practices to General Electric subsidiaries in a developing economy: An empirical analysis. International Journal of Quality & Reliability Management, 35(2), 446-462. https://doi.org/10.1108/IJQRM-09-2016-0155
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