Assignment 2 - Business Intelligence - Pass, Assignments of Computer Fundamentals

Download Assignment 2 - Business Intelligence - Pass and more Assignments Computer Fundamentals in PDF only on Docsity!

Huynh Minh Huy

GCD1001 | UNIVERSITY OF GREENWICH

Assignment 2

BUSINESS INTELLIGENCE

ASSIGNMENT 2 FRONT SHEET Qualification BTEC Level^5 HND Diploma in Business Unit number and title Unit 14: Business Intelligence Submission date 22 /04/2023 Date Received 1st submission Re-submission Date Date Received 2nd submission Student Name Huynh Minh Huy Student ID GCD Class GCD1001^ Assessor name Phyo Min Tun Student declaration I certify that the assignment submission is entirely my own work and I fully understand the consequences of plagiarism. I understand that making a false declaration is a form of malpractice. Student’s signature (^) Huy Grading grid P 3 P4 P5 P6 M3 M4 D3 D

Acknowledgement

First of all, I would like to thanks the curators of the University of Greenwich, who make these courses available to students. I would like to thanks all the authors and researchers who have researched about this data. In addition, I would like to thanks Dr. Phyo Min Tun for his very good and professional lectures and tutorials. Finally, I would like to thanks the teachers and responsible staff of our university for providing a good learning environment.

Table of Contents

CHAPTER 2: THE IMPACT OF BUSINESS INTELLIGENCE TOOLS AND TECHNOLOGIES FOR

CHAPTER 1: THE USE OF BUSINESS INTELLIGENCE TOOLS AND TECHNOLOGIES.

I. Determine, with examples, what business intelligence is and the tools and techniques associated with it.

1. General about Business Intelligence. a. What is Business Intelligence (BI)? Business intelligence (BI) is a process that leverages technology to analyze data and provide valuable insights to leaders, managers, and employees for making informed business decisions. This involves collecting data from various internal and external IT systems, preparing it for analysis, and generating visualizations, dashboards, and reports that help business users with their operational decision-making and strategic planning. The primary goal of BI is to enable businesses to improve revenue growth, streamline operations, and gain a competitive edge over their rivals. BI combines various technologies, including analytics, data management, and reporting, and utilizes different approaches to data analysis and management. Figure 1 : Business intelligence.

b. Why business intelligence is important. Ultimately, business intelligence's job is to improve an organization's business processes by utilizing relevant data. Businesses that use BI tools and approaches efficiently may transform their acquired data into valuable insights into their company operations and plans. These insights may then be utilized to create better business decisions that boost productivity and revenue, resulting in faster business growth and more profitability. Organizations cannot easily benefit from data-driven decision-making without BI. Instead, executives and employees must rely on other criteria to make crucial business decisions, such as collected knowledge, prior experiences, intuition, and gut emotions. While these strategies can provide solid conclusions, they are also rife with the possibility of mistakes and mishaps due to a lack of data. c. Real examples and how to apply BI on business.  Airlines: Airlines can use BI to analyze passenger data, such as ticket purchases and flight history, to predict future demand and adjust capacity accordingly. BI can also help airlines manage their pricing strategies and schedule crew members.  Healthcare: Healthcare companies can use BI to analyze patient data and identify trends and patterns that can help improve care and patient outcomes. For example, BI can help identify patients who are at high risk of developing certain conditions or who may require additional interventions.  Universities: Universities can use BI to track student performance metrics and identify students who may need additional support. For example, BI can help universities identify students who are struggling academically or who may be at risk of dropping out.  Financial services: Financial services companies can use BI to assess risk throughout the lending and policy approval processes. BI can also help these companies identify new products and services to offer to existing customers.  Manufacturers: Manufacturers can use BI to analyze historical and real-time data to optimize their operations. For example, BI can help manufacturers with production planning, procurement, and distribution.

Figure 3 : Kaggle. c. Data warehouse tool. A Data Warehousing (DW) process is the collection and management of data from many sources in order to give valuable business insights. A data warehouse is frequently used to integrate and analyze corporate data from disparate sources. The data warehouse is the heart of a BI system designed for data processing and reporting. Figure 4 : Data Warehouse. It is a collection of technology and components that facilitates the strategic use of data. It is the electronic storing of a huge volume of information by a firm for inquiry and analysis rather than

transaction processing. To make a difference, it is a process of changing data into information and making it available to consumers in a timely way. d. Data visualization tool. i. Tableau. The graphical display of information and data is known as data visualization. Data visualization tools, which include visual components like as charts, graphs, and maps, make it easy to view and comprehend trends, outliers, and patterns in data. Furthermore, it allows workers or business owners to deliver facts to non-technical audiences without causing misunderstanding. Figure 5 : Example for data visualization in Tableau.

3. Business Intelligence Techniques. a. Collection Technique. Collection techniques in business intelligence refer to the methods used to gather relevant data from various sources, including internal and external data sources. These techniques involve collecting, extracting, transforming, and loading (ETL) data into a centralized database or data warehouse for analysis. Once collected, the data goes through a process to highlight context, relevance, and purpose, transforming it into valuable information that can be used to drive business decisions. In today's highly competitive business environment, where customer needs and market conditions can change rapidly, companies must have the ability to quickly research and act on data to stay ahead of the competition. To make informed decisions, managers need access to accurate and up-to-date data that provides a clear picture of the business's operations and performance. This is why businesses amass a considerable amount of data through their daily operations, and why they increasingly rely on business intelligence tools to analyze and act on this data. In the global marketplace, decisions can be complex, and managers need to consider a variety of factors before making any decisions. Rushed decisions can lead to costly mistakes, which is why it's crucial for managers to have access to the right information at the right time. Effective collection techniques and business intelligence tools are essential for businesses to make informed decisions that help drive growth and stay competitive. b. Analytic Technique. Analytic techniques in business intelligence involve using various methods and tools to analyze data in order to identify valuable insights and patterns. This is an essential component of BI, as it allows businesses to gain a deeper understanding of the data they have collected and make informed decisions based on that data. Analytics can be applied in many different ways across various industries. For example, a marketing organization may use analytics to determine which customer segments are most likely to convert to new customers. By analyzing customer data and behavior, they can develop targeted marketing campaigns that are more likely to be successful.

c. Analysis Technique. Analysis Technique in Business Intelligence involves various techniques to process and analyze data to derive insights and make informed decisions. One of the key techniques is query analysis, where queries are written and run to investigate specific business problems. This technique enables quick generation of queries and regular running of them, with the analytics data being incorporated into dashboards and reports for better visualization. Another technique is Predictive Modeling, which utilizes statistical methods to create models for predicting probabilities and trends. With predictive modeling, different statistical models can be used to predict the value of a certain data item and its characteristics. Online Analytical Processing (OLAP) is also a technique used in Business Intelligence to handle multidimensional analytical problems. The multidimensionality feature of OLAP allows users to approach issues from various angles, making it useful for tasks such as budgeting, CRM data analysis, and financial forecasting. Finally, Model Visualization is a technique used to transform discovered facts into histograms, graphs, charts, and other visuals that help with the accurate interpretation of insights. This approach enhances the understanding of the data and allows for better communication of insights to stakeholders. II. 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.

1. Scenario and Application. Our team was tasked with developing a tool or application to help with complicated decision- making or problem-solving activities. After considering numerous possibilities, our team determined that the Tableau application would be the best solution for data visualization. Tableau is well-known for its user-friendly data visualization skills, but it offers more than simply visually appealing charts. The program contains live visual analytics, which allow users to easily detect data patterns by dragging and dropping buttons. It can connect to most databases and supports a broad range of data sources, including Microsoft Excel, Box, PDF files, and Google Analytics, among others.

connections, or it can be kept in memory by extracting data from a data source as needed. Tableau offers additional data connections capabilities like as automated extract refreshes, notification of a failed live connection, and so forth. d. Data Sources in Tableau. Tableau offers a number of data source choices for connecting to and retrieving data. Tableau supports on-premise files, spreadsheets, relational databases, non-relational databases, data warehouses, big data, and on-cloud data. A secure connection to any of Tableau's data sources is easily created, and that data, together with data from other sources, may be utilized to provide a combinatorial view of data in the form of visuals. Tableau also supports a variety of data interfaces, such as Presto, MemSQL, Google Analytics, Google Sheets, Cloudera, Hadoop, Amazon Athena, Salesforce, SQL Server, Dropbox, and many more. e. Advanced Visualizations (Chart Types). Tableau's large selection of visualizations is one of its most important and popular aspects. Tableau allows you to create visuals as simple as a:  Pie chart  Bar chart And as sophisticated as a:  Histogram  Gantt graph  Graph with bullets  Graph of movement  Treemap  Boxplot and many others. By selecting the visualization type from the Show Me menu, you can simply pick and create any sort of visualization.

f. Maps. Tableau's map is yet another important feature. Tableau includes a wealth of pre-installed map data, including cities, postal codes, administrative boundaries, and so on. This makes Tableau maps very detailed and informative. Depending on your demands, you may add different layers of geology to the map and utilize your data to make intriguing maps in Tableau. Tableau provides a wide range of maps, such as heat maps, flow maps, choropleth maps, and point distribution maps. g. Robust Security. Tableau places a premium on data and user security. It has a failsafe security system based on authentication and authorization procedures for data connections and user access. Tableau also supports additional security protocols like as Active Directory, Kerberos, and others. It's important to note that Tableau uses row-level filtering, which helps with data security. h. Mobile View. Tableau understands the importance of mobile phones in today's world and provides a mobile version of its app. Dashboards and reports may be developed in a mobile-friendly format. Tableau enables you to design mobile layouts for your dashboard that are specific to your mobile device. You may use the customization function to add new phone layouts, interactive offline previews, and so on. As a result, the mobile view gives Tableau users a lot of flexibility and ease when dealing with data while on the go. i. Ask Data. Tableau's Ask data tool has increased its popularity among users globally. This program makes data processing easier by allowing us to perform simple Google searches. Simply ask Tableau a natural language query about your data, and it will provide the most relevant results. The solutions are offered in both text and graphic form. For example, if what you were looking for is already in a bar graph, the Ask data option will search for and open the bar graph for you. Consumers benefit from such features since they can easily explore into data and uncover fresh insights and trends.

Figure 7 : Create graph in Tableau.

Here is the Dual-Axis Combo Chart for sales by average car price of manufacturer. This chart shows the trends of price in thousands and sales in thousands for manufacturer. Figure 8 : Sales by average car price of manufacturer.