1641- ASM2- Business Intelligence, Assignments of Social Intelligence

Download 1641- ASM2- Business Intelligence and more Assignments Social Intelligence in PDF only on Docsity!

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:

❒ Summative Feedback: ❒ Resubmission Feedback:

Grade: Assessor Signature: Date: Lecturer Signature:

Assessment Brief Student Name/ID Number TRẦN NGỌC GIA HOÀNG / GCS PHẠM ANH TIẾN PHÁT / GCS DƯƠNG TRỌNG QUÍ / GBS 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

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.

Table of Contents:

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

2 .2 Database / Data warehouse tools MySQL: MySQL is a relational database management system that aids in relational database administration. Querying and connecting to the MySQL server's database management system is possible. Additionally, it can operate on a wide range of systems and has a strong data structure. In demanding production applications, it is capable of reliably and swiftly handling massive datasets.[8] Microsoft SQL Server: A relational database management system with capabilities for creating and distributing interactive reports, such as SQL Server Reporting Services.[8] Oracle Database: An all-encompassing Relational Database Management System offering business intelligence capabilities like Oracle Business Intelligence Suite, enabling data analysis, reporting, and dashboards.[8] 2 .3 Data visualization tools: Tableau: Figure 2 : Tableau Tableau is a robust data visualization application that is used in the business intelligence and well-known data visualization industries to generate interactive dashboards, reports, and visualizations utilizing drag-and-drop capability. Data interpretation is a skill that Tableau supports for professionals at all organizational levels. It also enables anyone without much technological know-how to design their own dashboards.[5] Power BI: Figure 3 : Power BI

Microsoft's Power BI is a set of business analytics tools that enables users to connect to diverse data sources, produce interactive visualizations, and distribute insights within businesses.[5] III. Demonstration about BI

1. Explain dataset

Figure 4 : Dataset 1.1. Explain Column to be used in dataset There are 22 columns in total, of which: There are 4 columns of product identification: brand, model, color, processor 6 columns of configuration: memory, storage, rear_camera, front_camera, display_size, battery 3 columns on prices: original_price, discounted_price, battery_type 3 columns about reviews: ratings, rating_counts, reviews The remaining 6 columns are other attributes of the product Data types of columns: Number: original_price, discounted_price, memory, storage, display_size, battery_capacity, ratings, rating_count, reviews These columns contain measurable data such as battery capacity, product price, and so on. Besides, it is also used to express people's satisfaction with the product (rating). Text: brand, model, colour, processor, battery_type These columns contain data that is used to describe attributes or labels. Mixed data types: rear_camera, front_camera These columns contain measurable data and include units.

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 10 : Explore data type result In the dataset there are data types: Object: text format for columns such as brand, model, color, reviews Int64: integer form for price columns, rating count Float64: real numbers for price columns, ratings My dataset does not exist different data types that make it difficult to calculate so there is no need to handle different data types. 3.2. Count values for each column I proceeded to use pandas to visualize data to better understand the distribution of data types in the dataset.

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.