Essentials of Management Information Systems Chapter 6 PDF

Essentials of Management Information Systems Fourteenth Edition, Global Edition Chapter 6 Foundations of Business Intelligence: Databases and Informati...

Essentials of Management Information Systems Fourteenth Edition, Global Edition Chapter 6 Foundations of Business Intelligence: Databases and Information Management Copyright © 2020 Pearson Education Ltd. What Is a Database? Database: – Collection of related files containing records on people, places, or things Entity: – Generalized category representing person, place, thing – E.g., SUPPLIER, PART Attributes: – Specific characteristics of each entity:  SUPPLIER name, address  PART description, unit price, supplier Copyright © 2020 Pearson Education Ltd. Databases and Transaction Processing Firms use the web to make information from their internal databases available to customers and partners. Middleware and other software make this possible – Web server – Application servers or CG I – Database server Web interfaces provide familiarity to users and savings over redesigning legacy systems. Copyright © 2020 Pearson Education Ltd. Figure 6.16 Linking Internal Databases to the Web Copyright © 2020 Pearson Education Ltd. Relational Databases Organize data into two-dimensional tables (relations) with columns and rows One table for each entity: – E.g., (CUSTOMER, SUPPLIER, PART, SALES) – Fields (columns) store data representing an attribute – Rows store data for separate records, or tuples Key field: uniquely identifies each record Primary key Copyright © 2020 Pearson Education Ltd. Establishing Relationships Entity-relationship diagram – Used to clarify table relationships in a relational database Referential integrity rules – Ensure that relationships between coupled tables remain consistent – Example of a one-to-many relationship Copyright © 2020 Pearson Education Ltd. Figure 6.2 A Relational Database Table Copyright © 2020 Pearson Education Ltd. Figure 6.3 The PART Table Copyright © 2020 Pearson Education Ltd. Figure 6.5 Sample Purchase Order Report Copyright © 2020 Pearson Education Ltd. Figure 6.6 The Final Database Design with Sample Records Copyright © 2020 Pearson Education Ltd. Figure 6.7 Entity-Relationship Diagram for the Database with Four Tables Copyright © 2020 Pearson Education Ltd. Database Management Systems (DBMS) Software for creating, storing, organizing, and accessing data from a database Separates the logical and physical views of the data – Logical view: how end users view data – Physical view: how data are actually structured and organized Examples: Microsoft Access, DB2, Oracle Database, Microsoft SQ L Server, MySQL Copyright © 2020 Pearson Education Ltd. Figure 6.8 Human Resources Database with Multiple Views Copyright © 2020 Pearson Education Ltd. Operations of a Relational DBMS Select: – Creates a subset of all records meeting stated criteria Join: – Combines relational tables to present the server with more information than is available from individual tables Project: – Creates a subset consisting of columns in a table – Permits user to create new tables containing only desired information Copyright © 2020 Pearson Education Ltd. Figure 6.9 The Three Basic Operations of a Relational DBMS Copyright © 2020 Pearson Education Ltd. Figure 6.10 Access Data Dictionary Features Copyright © 2020 Pearson Education Ltd. Figure 6.11 Example of an SQL Query SELECT PART.Part_Number, PART.Part_Name, SUPPLIER.Supplier_Number, SUPPLIER.Supplier_Name FROM PART, SUPPLIER WHERE PART.Suplier_Number = SUPPLIER.Supplier_Number AND Part_Number = 137 OR Part_Number = 150; Copyright © 2020 Pearson Education Ltd. Figure 6.12 An Access Query Copyright © 2020 Pearson Education Ltd. Big Data Massive quantities of unstructured and semi-structured data from Internet and more – 3Vs: Volume, variety, velocity – Petabytes and exabytes Big datasets offer more patterns and insights than smaller datasets, e.g. – Customer behavior – Weather patterns Requires new technologies and tools Copyright © 2020 Pearson Education Ltd. Non-Relational Databases “NoSQL” Handle large data sets of data that are not easily organized into tables, columns, and rows Use more flexible data model – Don’t require extensive structuring Can manage unstructured data, such as social media and graphics E.g. Amazon’s Simple D B, MetLife’s Mongo D B Copyright © 2020 Pearson Education Ltd. Cloud Databases and Distributed Databases Relational database engines provided by cloud computing services – Pricing based on usage – Appeal to small or medium-sized businesses Amazon Relational Database Service – Offers MySQL, Microsoft SQL Server, Oracle Database engines Distributed databases – Stored in multiple physical locations – Google’s Spanner cloud service Copyright © 2020 Pearson Education Ltd. Blockchain Distributed database of transactions Operates on a network without central authority Maintains a growing list of records called blocks Once recorded, blocks cannot be changed Reduces cost of processing transactions and enhances security Copyright © 2020 Pearson Education Ltd. Figure 6.13 How Blockchain Works Copyright © 2020 Pearson Education Ltd. Business Intelligence Infrastructure Array of tools for obtaining useful information from internal and external systems and big data – Data warehouses – Data marts – Hadoop – In-memory computing – Analytical platforms Copyright © 2020 Pearson Education Ltd. Data Warehouses Data warehouse: – Database that stores current and historical data that may be of interest to decision makers – Consolidates and standardizes data from many systems, operational and transactional databases – Data can be accessed but not altered Data mart: – Subset of data warehouses that is highly focused and isolated for a specific population of users Copyright © 2020 Pearson Education Ltd. Hadoop Open-source software framework for big data Breaks data task into sub-problems and distributes the processing to many inexpensive computer processing nodes Combines result into smaller data set that is easier to analyze Key services – Hadoop Distributed File System (HDFS) – MapReduce Copyright © 2020 Pearson Education Ltd. Figure 6.14 Business Intelligence Technology Infrastructure Copyright © 2020 Pearson Education Ltd. Analytical Tools: Relationships, Patterns, Trends Once data is gathered, tools are required for consolidating, analyzing, to use insights to improve decision making – Software for database querying and reporting – Multidimensional data analysis (O L AP ) – Data mining Copyright © 2020 Pearson Education Ltd. Online Analytical Processing (OLAP) Supports multidimensional data analysis, enabling users to view the same data in different ways using multiple dimensions – Each aspect of information—product, pricing, cost, region, or time period—represents a different dimension – E.g., comparing sales in East in June versus May and July Enables users to obtain online answers to ad hoc questions such as these in a fairly rapid amount of time Copyright © 2020 Pearson Education Ltd. Figure 6.15 Multidimensional Data Model Copyright © 2020 Pearson Education Ltd. Data Mining Finds hidden patterns and relationships in large databases and infers rules from them to predict future behavior Types of information obtainable from data mining – Associations: occurrences linked to single event ex: change in system because of Covid – Sequences: events linked over time – Classifications: patterns describing a group an item belongs to – Clustering: discovering as yet unclassified groupings – Forecasting: uses series of values to forecast future values Copyright © 2020 Pearson Education Ltd. Text Mining Unstructured data (mostly text files) accounts for 80 percent of an organization’s useful information. Text mining allows businesses to extract key elements from, discover patterns in, and summarize large unstructured data sets. Sentiment analysis – Mines online text comments online or in email to measure customer sentiment Copyright © 2020 Pearson Education Ltd. Web Mining Discovery and analysis of useful patterns and information from the web – E.g. to understand customer behavior, evaluate website, quantify success of marketing Content mining – mines content of websites Structure mining – mines website structural elements, such as links Usage mining – mines user interaction data gathered by web servers Copyright © 2020 Pearson Education Ltd.