1641- ASM2- Business Intelligence, Assignments of Social Intelligence

Download 1641- ASM2- Business Intelligence and more Assignments Social Intelligence in PDF only on Docsity! sty UNIVERSITY vy a7 GREENWICH atin wih LAI eductn Higher Nationals in Computing Unit 14: Business Intelligence ASSIGNMENT 2 Name of member: ID: Class: Subject code: 1641 Assessor name: Le Tran Ngoc Tran Assignment due: Assignment submitted: Assessment Brief Student Name/ID Number TRẦN NGỌC GIA HOÀNG / GCS210157 PHẠM ANH TIẾN PHÁT / GCS210419 DƯƠNG TRỌNG QUÍ / GBS200716 Unit Number and Title 14: Business Intelligence Academic Year 2023 Unit Tutor Assignment Title Assignment 2: Apply BI tools & techniques and their impact Issue Date Submission Date IV Name & Date Submission Format Part I: Project submission. This should be a zip / rar folder of your project, including all necessary files to run your project. There should be a link to your Tableau work on Tableau Public cloud. Part II: The submission is in the form of a group written report. This should be written in a concise, formal business style using single spacing and font size 12. You are required to make use of headings, paragraphs and subsections as appropriate, and all work must be supported with research and referenced using the Harvard referencing system. Please also provide a bibliography using the Harvard referencing system. Part III: Team needs to present their point of view about how business intelligence tools can contribute to effective decision-making as well as the legal issues involved in exploiting user data for business intelligence. You may need to research for specific examples of organizations that use BI tools to enhance or improve their business and evaluate how they can use BI tools for extend their target audience and make them more competitive within the market. Unit Learning Outcomes LO3 Demonstrate the use of business intelligence tools and technologies Assignment Brief (Continued from previous scenario) Your next task is to demonstrate to the board of directors about the ability of applying business intelligence in the company's current business processes. To demonstrate BI, you need to prepare a presentation about BI and related tools & techniques and a demonstration on real company dataset. For the presentation, you need: - Explain general concept of what is BI - Introduction to some tools / techniques for BI and their application in general For the demonstration, you need: - A (some) data set(s) extracted from the company's business processes. Explain the dataset. - Show how you pre-process data for later analysis, explain each step and it purpose - Design dashboards to show your analysis on pre-processed data. Explain clearly purpose of dashboards and charts. Suggestions should be made after analysis During the demonstration, you need collect feed-back and comments from users to review how well your dashboards design meet user or business requirement and what customization needed for future use. Team needs to present their point of view about how business intelligence tools can contribute to effective decision-making as well as the legal issues involved in exploiting user data for business intelligence. You may need to research for specific examples of organizations that use BI tools to enhance or improve their business and evaluate how they can use BI tools for extend their target audience and make them more competitive within the market. To summary, you need to submit a report in PDF includes 4 parts: your presentation, result of demonstration and review of user feedback, point of view on BI contribution and legal issues. Learning Outcomes and Assessment Criteria Pass Merit Distinction LO3 Demonstrate the use of business intelligence tools and technologies D3 Provide a critical review of the design in terms of how it meets a specific user or business requirement and identify what customisation has been integrated into the design. P3 Determine, with examples, what business intelligence is and the tools and techniques associated with it. P4 Design a business intelligence tool, application or interface that can perform a specific task to support problem-solving or decision-making at an advanced level. M3 Customise the design to ensure that it is user friendly and has a functional interface. LO4 Discuss the impact of business intelligence tools and technologies for effective decision-making purposes and the legal/regulatory context in which they are used D4 Evaluate how organisations could use business intelligence to extend their target audience and make them more competitive within the market, taking security legislation into consideration P5 Discuss how business intelligence tools can contribute to effective decision- making. P6 Explore the legal issues involved in the secure exploitation of business intelligence tools M4 Conduct research to identify specific examples of organisations that have used business intelligence tools to enhance or improve operations. ASSIGNMENT 2 ANSWERS I. General about BI 1. What is Business Intelligence (BI) Figure 1: Business intelligence (BI) Business intelligence (BI) is a technology-driven method for data analysis and information delivery that aids managers, employees, and executives in making wise business decisions. In order to make the analytics results available to business users for operational decision-making and strategic planning, organizations collect data from internal IT systems and external sources, prepare it for analysis, run queries against the data, and create data visualizations, BI dashboards, and reports.[1] Better business decisions will enable enterprises to grow revenue, enhance operational effectiveness, and gain a competitive edge over rival companies. This is the aim of BI projects. In order to accomplish that, BI combines analytics, reporting, and data management technologies with a number of different data management and analysis approaches.[1] 2. Real examples of how to apply BI on business Example 1: Coca-Cola Company: BI improves enhanced operational efficiency.[3] About the company: With more than 500 brands and 3,900 product names, Coca-Cola is the largest beverage conglomerate in the world. Challenge: The team spent the majority of its time producing reports. Real-time sales and transaction data were only partially accessible due to these laborious reporting methods. Solution: By automating the manual reporting procedures with the support of the business intelligence platform, the team was able to save more than 260 hours annually or more than six 40-hour work weeks. The sales department obtains timely information through mobile dashboards as a result of the automation of reporting. Results: Coca-Cola after applying BI tools to forecast demand based on historical sales data. It allows them to predict future trends and adjust production schedules, increasing operational efficiency. Example 2: Lotte.com: BI Increases Company Revenue[2] About the company: With 13 million consumers, Lotte.com is the most popular online shopping destination in Korea. Challenge: With more than 1 million visitors each day to the website, business management sought to know why customers abandoned their shopping carts. Solution: Customer experience analysis, the first online behavioral analysis system used in Korea, was deployed by the marketing planning team's assistant general manager. Managers utilize the data to execute customized marketing, convert websites, and study consumer behavior. Results: After a year, the new BI analytics program raised sales by $10 million and improved customer loyalty. These adjustments result from locating the sources of cart abandonment—like protracted checkout procedures and unanticipated delivery times—and resolving them. II. BI tools and techniques 1. Techniques 1.1. Collection techniques: In business intelligence, collecting techniques refer to data collection procedures. This means that two crucial methods to guarantee data quality are cleansing and labeling.  Cleansing data is the process of identifying and correcting errors, inconsistencies, and inaccuracies in the data. Eliminating duplicate entries, dealing with missing data, standardizing data formats, and addressing inconsistencies are just a few of the tasks it requires. Data quality assurance is the goal since reliable analysis and decision-making depend on correct and clean data.[4]  Labeling data is the process of categorizing and tagging data to improve its organization and discoverability. This method involves assigning data points or records appropriate labels or tags based on preset criteria. Data labeling enhances the data's accessibility and organization, enabling efficient analysis and retrieval.[4] To guarantee that the data used is reliable and useful for corporate decision-making, both of these strategies are crucial in business intelligence. 1.2.Analysis technique Business intelligence uses analysis techniques to better understand operations and support data- driven decision-making for organizations. Reports, queries, and dashboards are common methods.  Reports: Reports on business intelligence (BI) provide an overview and analysis of the most crucial company data and success elements. In these reports, data is often presented in straightforward and aesthetically pleasing ways using charts, graphs, and tables. In order to satisfy specific information demands, BI reports can be generated on a daily, weekly, or monthly basis. They are used to monitor and assess the success of businesses, identify patterns, and make decisions based on the data.[6]  Queries: Queries are used to retrieve particular sets of data from databases or data storage. Businesses utilize structured query language (SQL) or query tools to discover data that satisfies certain criteria or regulations. Users may do calculations, dig deeper into the data, and discover interesting information by using queries. They enable firms to search for trends, discover particular answers to queries, and examine how data is connected.[6]  Dashboards: Dashboards for displaying crucial data concerning a company's activities. Typically, dashboards are made to show significant data like sales, profit, the number of clients, and other indicators. Dashboard creation features are frequently included in data analysis applications so that users may readily and aesthetically present information. With the use of dashboards, people can quickly and easily access crucial information, facilitating speedy decision-making.[6] 1.3. Analytic technique In business intelligence, regression and machine learning are two crucial data analysis methods.  Regression is a statistical data analysis technique used to identify correlations between variables. The value of one dependent variable is predicted using the values of other independent variables. To examine data on sales, pricing, product sales, and other business variables, regression can be employed. [6]  Machine learning is a technique for data analysis that uses computers to discover patterns in data and forecast results. Data may be categorized, outcomes predicted, and patterns in the data can be found using machine learning. It may be used to examine customer, product, and other business-related data.[6] Managers and data analysts may use both regression and machine learning to examine data and come to wise business judgments. They may be utilized to improve revenue generation, operational efficiency, and corporate strategy. 2. Tools 2.1 Programming tools Python is a popular computer programming language used to create software and websites, automate processes, and analyze data.[7] Here are the Python libraries that help in data analysis: - NumPy: NumPy (Numerical Python) is a package for processing arrays that can be used for many different things. It gives you tools for working with high-performance multidimensional objects called arrays. NumPy also helps solve the problem of slowness by giving you these multidimensional arrays and functions and operators that work well with them. - Pandas: Pandas have fast, flexible data structures like data frame CDs that make working with structured data very easy and straightforward. 1.2. Explain rows to be used in dataset The total number of rows in the dataset is 5043 rows. Each row represents information for a phone product. The rows show a great diversity of phone products, with many different brands, models, colors, configurations. Each product information includes: brand name, model name, color, original price, discount price, configuration (RAM, memory, CPU), camera, monitor, battery, reviews and reviews. Column values have appropriate data types, such as object for categorical attributes, float/int for metric values. There are almost no missing values in the rows. The data collected and entered is quite complete. The large number of rows allows analysis with better statistical significance, representing a wide product range. As such, the rows provide detailed, rich and clean data on the phone products, which is very convenient for analysis and modeling. 1.3. Explain unique values in dataset I used pandas to see unique values in the dataset's columns and got the following result: Figure 5: Unique values in dataset Columns like brand, model, colour, processor have a lot of unique values, reflecting the variety of products. Configuration columns (memory, storage, display_size, battery_capacity) are less unique. Review columns (ratings, rating_count, reviews) contain only numerical values, so the number of unique values is low. Some columns such as battery_type and memory have very few unique values, reflecting technological uniformity. 1.4. Explain null values ( N/A values ) in dataset In our dataset, there are some N/A values which need to be pre-processed. The figure below shows the correlation between null data and non-null data, and also shows the types of data that contain null values in our dataset file, which includes: colour, memory, storage, processor, front_camera, battery_capacity, battery_type Figure 6: Null and non-null values in dataset The existence of these N/A values is due to the fact that there are some smartphone attributes that are not declared by manufacturers for technology security. In total, there are about 0.2% N/A values in the dataset. 2. Explain pre-processing step 2.1. Fill N/A values Fill N/A values are based on international minimum configuration conventions that phone manufacturers must follow, respectively: Colour: Dark Memory: 4 Storage: 32 Processor: Kortex font_camera: 5MP battery_type: Lithium-ion battery_capacity: 0 ( unknows ) Before fill N/A data After fill N/A data 2.2. Feature engineering For more convenient analysis in the future, I used pandas to create an additional column showing the average discount percentage of carriers, which is shown as shown below: Figure 7: missing valune before Figure 8: missing valune before Figure 11: Number of models by brand This bar chart shows the popularity of each phone company based on the number of products of each company, the popularity is arranged in order from left to right, starting from the most popular. Figure 12: Average price by Brand This bar chart shows the average price trend after the price of brands has decreased, allowing prices to be compared between brands visually. We can see that in addition to Apple, the price trend of the other companies is relatively uniform. Figure 13: Top 10 Brand market share The pie chart shows the market share of phone companies, looking at which it is easy to see that the top 10 phone companies with the highest market share have accounted for 93.4% of the total market share. Figure 14: Frequency of appearance of each device in each price range Explain Figure 17: This chart shows the average price of each smartphone brand (this is the price when the discount is not applied). Based on the chart, customers can choose to buy products of brands that suit their wallets and needs. Figure 18: Average discount percentage of each brand Explain Figure 18: This chart shows the average discount percentage of each brand's products. Based on this chart, customers can choose the company to buy the product so that if the product needs to be sold later, the product will lose as little value as possible. Figure 19: Market share of phone brand Explain Figure 19: This chart shows the market share of each brand. This also reflects the brand's popularity in the market, and this means that the more popular brand's products will have more spare parts when the phone breaks down than the less popular company. This can make it easier for customers to make purchasing decisions. Figure 20: Frequency of appearance of each device and average rating score in each price range Explain Figure 20: This chart helps us visualize the frequency of occurrence as well as the average ratings of smartphones within each price range. Based on it, customers can see if there are many smartphone options within their budget and whether the phones in that price range receive high ratings. This enables them to make purchasing decisions more quickly. IV. Point of view 1. How BI contribute to effective decision making In order to make well-informed and efficient decisions within organizational settings, business intelligence (BI) tool use is generally of utmost necessity. In order to transform raw data into useful and insightful insights, these technologies make use of data analytics, reporting, and visualization capabilities. Business intelligence technologies may help people make better decisions through a variety of techniques, including the following:  Data aggregation and consolidation: Databases, spreadsheets, and other systems' data are combined and integrated into one repository using BI tools. Through the creation of a single source of truth, these technologies give decision- makers a thorough understanding of the data within the business.[10]  Data analysis and visualization: Complex calculations, statistical modeling, and the detection of data trends and patterns are all made possible by BI technology. These tools utilize machine learning and potent algorithms to discover insights that are difficult to locate manually. Decision-makers can quickly grasp data thanks to charts, graphs, and dashboards.[9]  Real-time monitoring: Decision-making may track Key Performance Indicators and metrics in real time with the help of many BI systems' real-time monitoring and alert features. When predefined thresholds or conditions are met, decision-makers may get timely notifications and communications. This enables businesses to respond rapidly to significant events or alter business conditions.[11]  Real-Time Reporting: BI technologies can offer near-real-time or real-time reporting, giving decision-makers access to the most recent data about their company. Managers are better equipped to react quickly to changing market circumstances, new possibilities, and possible hazards when they have timely access to data. It lessens the need for manual data collection and makes it possible to make proactive decisions based on up-to-date, reliable information.[11]  Analytics that are predictive and prescriptive: Business intelligence technologies include analytics that are predictive and prescriptive. Using historical data and statistical modeling, predictive analytics predicts market conditions, customer behavior, and demand trends. To improve performance, prescriptive analytics suggests specific actions. With the use of advanced analytics, BI solutions allow data-driven, insight-based decision-making.[12]  Performance measurement and goal monitoring: 2.2. Control over Data Because data control data owners can be exploited and shared, data ownership can be competitive. Ownership of data resulting from analysis is also crucial. Security and Terms of Service rules for websites, online services, and mobile applications frequently assign data rights. Conventional written contracts can be utilized for business transactions. 2.3. Intellectual property protection A ruling by an allice court resulted in the patenting of some data analysis software. To patent the software data, the innovation stages will be required. Over time, these patents can become less valuable since the algorithm may advance beyond what was described in the patent and the required patent application. 2.4. Terms of service agreement A legal agreement known as the Terms of Service was created to restrict how websites, mobile applications, and online services may be used. There are clauses in the Terms of Service that lessen the possibility of complaints from users and other parties. Along with the extent of permissible use, limited activity, content disclaimer, remuneration, term and termination, regulatory law, jurisdiction, dispute resolution, and other concerns, the Terms of Service may also address copyright and other intellectual property rights. Example: Amazon - Data Privacy and Business Intelligence Background: Amazon, a prominent player in the e-commerce industry, collects vast amounts of user data to enhance their business intelligence efforts. They use this data to analyze customer behavior, preferences, and buying patterns to improve marketing strategies, optimize product offerings, and enhance overall customer experience. Legal Issue: User Data Privacy and Compliance with Regulations Challenge: Amazon faces a legal challenge regarding the privacy of user data. Some customers raise concerns about how their personal information is being utilized for business intelligence purposes. Additionally, new data protection regulations have been enacted in the region where Amazon operates, imposing strict guidelines on the collection, storage, and use of personal data. Legal Actions Taken: + Data Protection Compliance: Amazon engages legal experts to ensure their data practices comply with the newly enacted data protection regulations. They conduct thorough audits of their data collection methods, storage protocols, and analytical processes to align with the legal requirements. + Transparency and Consent: Amazon revises its privacy policy and terms of service to clearly inform users about how their data is collected, processed, and used for business intelligence purposes. They implement robust mechanisms to obtain explicit consent from users before utilizing their data for analytics. + Anonymization and Aggregation: To address privacy concerns, Amazon adopts advanced techniques such as data anonymization and aggregation. Personal identifiers are removed from datasets, and data is aggregated at a broader level to prevent individual user identification while still enabling meaningful analysis. + Internal Training and Awareness: Amazon conducts extensive training sessions for employees involved in data processing and analysis. Staff members are educated about the legal obligations, user privacy rights, and the importance of adhering to ethical practices when handling customer data. Outcome: By proactively addressing the legal challenges associated with user data privacy, Amazon not only ensures compliance with regulations but also builds trust among its customers. Transparent communication, data anonymization, and strict adherence to legal guidelines enable Amazon to continue leveraging user data for business intelligence responsibly and ethically. V. Evaluate REFERENCES 1. Stedman, C. 2023. What is business intelligence? [Online]. 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