Business Intelligence and Data Analytics of Fintech

Business Intelligence and Data Analytics
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Business Intelligence and Data Analytics
Executive Summary
This paper is a business analysis of Fintech, a semiconductor manufacturing company, which currently lacks a business intelligence tool to help with generating actionable insights from voluminous organizational data. Integrating big data with Artificial Intelligence (AI) and Machine Learning is an opportunity for Fintech to generate business value from its company data sets. The two emerging technologies will increase the company’s operational efficiency and profit margins, over and above, help it gain a more significant competitive advantage. The paper outlines the problem improvement strategy, including AI/ML implementation process, strategy assumptions, and execution constraints. It also highlights several implementations, ethical, and inclusivity considerations of the problem improvement strategy.
Problem Statement
Fintech is a semiconductor technology company that lacks an effective business intelligence tool to convert data sources into actionable business intelligence. Because of its inability to analyze the large amounts of data scattered across its traditional databases, the company has found itself unable to make decisions promptly or produce comprehensive business intelligence reports. Without any business intelligence tools to assist in synchronizing and generating meaningful insights from the fragmented and unstructured data sources, affected companies cannot effectively make informed decisions or gain an unbeatable competitive edge in terms of operational efficiency (MastersInDataScience, 2020).
Fintech does not have an analytical solution to guide its business decisions or develop high-performance reports that identify where to invest or enhance the performance of sales, manufacturing, and R&D divisions. Besides the inability to generate reliable, extensive, accurate, and timely business intelligence, the semiconductor device maker, is ineffective in creating predictive forecasts of future R&D bottlenecks, production delays, or industry developments. Consequently, the company has been having issues with developing stronger customer relationships, improving its profit margins, optimizing productivity and production cycles, and other critical metrics.
A recent survey report by McKinsey showed that only about 30 percent of semiconductor device makers are creating value in the form of actionable insights through AI/ML. Only a handful of companies in this sector have invested in AI/ML talent and achieved the necessary technology, data infrastructure, and other enablers to fully scale AI/ML-driven business intelligence (McKinsey Insights, 2016). The intersection of big data and AI/ML is an opportunity for improvement, especially for Fintech, which is currently inefficient in finding value in the information it is collecting. When applied to voluminous, fragmented, and unstructured data, AI/ML eliminates limited and inaccurate interpretations by promptly providing clear, actionable business intelligence from large data sets. The two technologies can process large data sets to extract, analyze, generate, and even predict business-essential insights.
Problem Improvement Strategy
The strategy for improving Fintech's ability to generate actionable business insights from its voluminous and fragmented data sites entails implementing AI/ML as a business intelligence tool. Now that more companies realize the value of AI/ML in collecting and analyzing business data to not only improve business intelligence but also develop predictive and prescriptive models, implementing an AI/ML system will help the semiconductor manufacturer increase operational efficiency and profit margins over and above, gain a more significant competitive advantage.
The first step in the problem improvement strategy is defining a use case where the company management team aligns AI/ML strategy with the business objectives of enhancing operational efficiency, increasing profit margins, and gaining a competitive advantage. Fitting the AI/ML strategy into the corporate strategy ensures that everyone involved understands the overall vision and the key performance indicators in evaluating the business value of the program include (Tandon, 2021). The next step of the problem improvement strategy is defining data requirements, staffing needs, and costs.
Data is perhaps the most critical element nec...