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Assignment Brief (RQF)
Higher National Diploma in Computing
Student Name/ID Number: Unit Number and Title: Assignment 01 Academic Year: 2021 Unit Assessor: Assignment Title: Business Interligence. Issue Date: Submission Date: Internal Verifier Name: Date: Submission Format: Students have to submit the WHOLE Assignment work as an Academic report. Soft copy is required in the day of submission. Student need to answer all questions. Referencing is mandatory and no word Limits. Any type of academic misconduct will result a “Fail”. Final grade - fail, pass, merit or distinction depend on the quality and number of questions answered. Unit Learning Outcomes: LO1 Discuss business processes and the mechanisms used to support business decision-making LO2 Compare the tools and technologies associated with business intelligence functionality LO3 Demonstrate the use of business intelligence tools and technologies. 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
Assignment: Answer all questions. Q1. a) Briefly explain the following terms with using suitable examples. i. Business Process and Supporting Processes ii. Structured, Unstructured and Semi-structured data iii. Data Warehouse and Data Mining (P1) b) Explain the difference between unstructured and semi-structured data within an organization. (M1) c) Critically evaluate the benefits and drawbacks of using application software as a mechanism for business processing. (D1) Q2. a) Briefly explain the three management levels of an organization. (P2) b) Explain the types of support available for business decision-making at varying levels within an organization. (P2) c) Explain the key features of business intelligence functionality with examples. (M2) d) Compare and contrast a range of information systems and technologies that can be used to support organizations at three management levels of an organization. (D2) Q3. a) Explain what business intelligence is with examples. (P3) b) Explain the tools and techniques associated with business intelligence (P3) c) 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. (P4) d) Use the file “DATA SET-Assignment”. It has data sheet named “data-Q3(d)”. Using that data sheet answer the following questions. a. Shows the revenue in Germany and the US throughout the years b. Analyze the data from Germany first and, then, do a slice on country.
intelligence tools. (P6) c) Conduct research to identify five examples of organizations that have used business intelligence tools to enhance or improve operations. (M4) d) Consider the following case study explain how to apply Business Intelligence for this analysis. What an enterprise firm can do to extend their target audience and make them more competitive within the market, taking security legislation into consideration. (D4) Case Study Suppose your are work as a data analyst. You are recorded data of a Shopping Salesroom of 3-6 months. Candles are one of the product sell in this shop. It has three kinds of Candles- Candle A, Candle B, Candle C. On studying of these data, it come to know that sale of Candle C was at peak out of these three classes. Now on afresh and deep study into these data, it got the outcome that the sale of this Candle C was maximum between the time intervals of 9 am to 11 am. On further deeper analysis, it concluded that this specific Candle is the one used in place of worship. Learning Outcomes and Assessment Criteria of the Module Pass Merit Distinction LO-1 - Discuss business processes and the mechanisms used to support business decision-making P1 Examine, using examples, the terms ‘Business Process’ and ‘Supporting Processes’. M1 Differentiate between unstructured and semistructured data within an organisation. D1 Evaluate the benefits and drawbacks of using application software as a mechanism for business processing. LO2 Compare the tools and technologies associated with business intelligence functionality P2 Compare the types of support available for business decision- making at varying levels within an organisation. M2 Justify, with specific examples, the key features of business intelligence functionality. D2 Compare and contrast a range of information systems and technologies that can be used to support organisations at operational, tactical and strategic levels.
LO3 Demonstrate the use of business intelligence tools and technologies 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 userfriendly and has a functional interface. 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 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 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. 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.
Task 04........................................................................................................... 59
a) Explain how business intelligence tools can contribute to effective decision-making.......... 59 .......................................................................................................................................... 59 b) Discover the legal issues involved in the secure exploitation of business intelligence tools. 62 c) Conduct research to identify five examples of organizations that have used business intelligence tools to enhance or improve operations. (M4)...................................................... 63 d) Consider the following case study explain how to apply Business Intelligence for this analysis. What an enterprise firm can do to extend their target audience and make them more competitive within the market, taking security legislation into consideration............................ 65 References......................................................................................................................... 68
List of Figures
- Figure 1:Business Intelligence.............................................................................................
- Figure 2: Structured, Semi Structured & Unstructured Data...............................................
- Figure 3: Example for Structural Data.................................................................................
- Figure 4: Example for Semi-Structured Data.......................................................................
- Figure 5: Examples for Unstructured Data..........................................................................
- Figure 6: Data warehouse Process.....................................................................................
- Figure 7: Data Mining Process............................................................................................
- Figure 8: Top Level Management........................................................................................
- Figure 9: Middle Level Management...................................................................................
- Figure 10: Lower Level Management..................................................................................
- Figure 11: Levels of Management with Decision making....................................................
- Figure 12: Levels of Management.......................................................................................
- Figure 13: Functional Areas.................................................................................................
- Figure 14: Business Intelligence (BI)...................................................................................
- Figure 15: SAP Business Option.........................................................................................
- Figure 16: Micro Strategy....................................................................................................
- Figure 17: SAS Business Intelligence.................................................................................
- Figure 18: Micro Power BI...................................................................................................
- Figure 19: Clear Analytics....................................................................................................
- Figure 20: Tableau...............................................................................................................
- Figure 21: Oracle BI.............................................................................................................
- Figure 22: Domo..................................................................................................................
- Figure 23: Interface of the data gathered............................................................................
- Figure 24: Interface 2...........................................................................................................
- Figure 25: BI Dashboard......................................................................................................
- Figure 26: Pivot tables and Charts......................................................................................
- Figure 27: Revenue in Germany & US................................................................................
- Figure 28: Revenue of Germany and Slice Country............................................................
- Figure 29:Revenue by drill down to the sales organization.................................................
- Figure 30: Values for the Data Cube drawing......................................................................
- Figure 31: Analysed Data for data cube..............................................................................
- Figure 32: Data Cube..........................................................................................................
- Figure 34: Boxplot Diagram................................................................................................. Figure 33: Equations for drawing Boxplot............................. Error! Bookmark not defined.
- Figure 35: Oracle BI.............................................................................................................
- Figure 36: Decision Process................................................................................................
- Figure 37: Data Analyse Candles........................................................................................
Introduction
Business Intelligence is a set of processes, architectures, and technologies that translate raw data into meaningful information. A group of software and services that translate data into actionable intelligence and knowledge. BI has a direct bearing on the strategic, strategic, and operational business decisions of the organization. BI assists in making fact-based decisions using historical data rather than hypotheses and gut feelings. BI Tools analyses data and creates reports, summaries, dashboards, maps, charts, and charts to give users a detailed understanding of the nature of the business. Figure 1 :Business Intelligence (Source:” https://images.app.goo.gl/YiKqD79evW2vMB9V6”)
Task 01
Q1.
a) Briefly explain the following terms with using suitable examples.
i. Business Process and Supporting Processes ii. Structured, Unstructured and Semi-structured data iii. Data Warehouse and Data Mining (i) Business Process and Supporting Processes Business Process A business process is a set of relevant, structured activities and steps performed by individuals or equipment in an organization to achieve core organizational goals such as profit maximization and customer satisfaction. Business processes can be repeated many times at every corporate level and are not visible to customers. A business process is often regarded as a flow chart or workflow of logical steps, with a number of basic concepts such as business process management, process optimization, process mapping, process simulation, and process automation. The importance of business processes for small, medium and large businesses cannot be overestimated as it helps to streamline and streamline your business operations, thereby facilitating business process management and improving the efficiency and productivity of the organization. Business processes have many more sub processes related to the whole business vision. Mainly it includes operation process, management processes, executing processes, and supporting processes. Operational processes, also known as core business processes, are activities that provide direct value to the company and its customers. Since these processes are essential for the company and are generating revenue and that is why their completeness is very important. These days most of the processes and many operations have been centralized to computer or machine based systems because it supports to manage all kind of data and operations efficiently and accurately. All of the
HR
It is common knowledge that a company's human resource operations are critical and critical to the company's existence, as these processes directly affect how the company attracts, retains, benefits, and develops skills. Similarly, financial processes - from governance activities to internal consulting - are equally crucial to the success of any organization. This also applies to other support processes such as procurement or real estate. IT IT supports a business in many ways. Many companies today work on computers. Information technology helps a business to keep purchasing records. those The company stores a large amount of data, including sales report and company annual report. Activates business processes through information management and information processing. (ii) Structured, Unstructured and Semi-structured data Data is a set of factors used to describe, observe, and make decisions. We can classify them as structured, structured or semi-structured data.
(Source:(https://images.app.goo.gl/zWW8fG7h1mgA7Pwk8) Structured Data Structural data is data that can be addressed for effective cost analysis. It is organized into a formatted repository, which is usually a database. It takes care of all the data that can be stored in the SQL database in a table with rows and columns. They have connection keys and can easily map pre-designed areas. Today, that data is mostly processed for development and simple information management. Structural data is often classified as quantitative data, a type of data that many of us are accustomed to working with. Think of data that fits neatly in fixed fields and columns in online databases and spreadsheets. Structural data is highly organized and can be easily understood in machine language. Those who work in online databases can input, search and manipulate structured data relatively quickly using the Relative Database Management System (RDBMS). This is the most attractive feature of structured data. The programming language used for structured data management is called structured query language, also known as SQL. This language was developed by IBM in the early 1970s and is especially useful for managing database contacts. Examples Dates, addresses, credit card numbers, stock information, geolocation Databases (SQL Relational Databases) Figure 2 : Structured, Semi Structured & Unstructured Data
Emails by Inbox, Sent, Drafts HTML Folders Organized by Topics Web pages Unstructured Data Non-structured data is not a good fit for a mainstream contact database as it is not organized in a pre-defined way or has no pre-defined data format. Therefore, for non-structured data, there are alternative platforms for storage and management that are increasingly prevalent in IT systems and are used by organizations in various business intelligence and analytics applications. Example : Word, PDF, Text, Audio, Video, Online surveys Figure 4 : Example for Semi-Structured Data Figure 5 : Examples for Unstructured Data
(iii) Data Warehouse and Data Mining Data Warehouse A data warehouse is a centralized repository of information that can be analysed to make more informed decisions. Data flows into a data warehouse from transaction systems, relational databases, and other sources. Business analysts, data engineers, data scientists and decision makers access data through business intelligence tools, SQL clients, and other analytics applications. Data and analysis are essential to maintain business and competitiveness. Business users rely on reports, dashboards and analytics tools to extract insight from their data, monitor business performance and assist in decision making. Data warehouses deliver these records, dashboards, and analytics tools provide efficient data storage and query results to hundreds of thousands of users at once, minimizing data input and output. A data warehouse can contain multiple databases. The data in each database are organized into tables and columns. In each column, you can define a description of the data, such as integers, data field, or strings. Can be organized tables into strategies that can think of as folders. When data is entered, it is stored in the various tables described by the calibration. Query tools use the scheme to determine which data tables should be accessed and analysed. Figure 6 : Data warehouse Process (Source:” https://images.app.goo.gl/TVncFkskZBeH1d2P9”)
Domino's helps customers create the perfect pizza - the world's largest pizza company, collecting over 85,000 structured and unstructured data sources, including 26 outlets and supply chain outlets and across all channels, including SMS, social media and Amazon Echo. This insight enhances business performance while providing one-on-one purchasing experience across touch points
b) Explain the difference between unstructured and semi-structured data within an
organization.
In an organization, in the context of Big Data, we know that it deals with a large amount of data and its execution. So, in nutshell, we can say that Big data is something that deals with a large amount of data and as the amount of data is so large then broadly there are three categories that are defined on the basis of how data is organized which are named as Structured, Semi-Structured and Unstructured Data Structured data is data with a high degree of organization, typically stored in a spreadsheet-like manner. Semi-structured data is data with some degree of organization. And unstructured data is data with no predefined organizational form and no specific format, so essentially everything which is not structured or semi-structured data. Figure 7 : Data Mining Process
Table 1 : Difference between Unstructured & Semi-Structured data Properties Semi-structured data Unstructured data Technology Based on XML/RDF Based on character and binary data Version management Over tuples or graph is possible Versioned as a whole Transaction management The transaction is adapted from immature DBMS No transaction management and no concurrency Flexibility More flexible than structured data but less than unstructured data More flexible and there is absence of schema Scalability It is simpler to scale than structural data It is more scalable. Robustness New technology, not very spread No Query performance You can make inquiries about anonymous nodes Only textual queries are possible Examples HTML, JSON, XML Files Images, Videos, Audios, Text Files, Word Files, PDF Files Semi-structured data is partially organized, well its level of organization is lesser than that of structured data but higher than that of unstructured data. And then, unstructured data is not organized for any way. Semi-structured data is more capable of adapting and easy to understand. Unstructured data, on the other hand, lacks the schema that enables it to be the most flexible and scalable of the two. Because only a partial database is supported, semi-structured data, tuples, or graphs are possible and there is no database support in unstructured data, versioning is likely to be done as a whole data set. Although semi-structured data transactions are adapted from DBMS, data concurrency still isn't available. At last, there is no transaction management or data concurrency in unstructured data.
