Management Information Systems: Managing the Digital Firm PDF

Management Information Systems: Managing the Digital Firm Sixteenth Edition Chapter 6 Foundations of Business Intelligence: Databases and I...

Management Information Systems: Managing the Digital Firm Sixteenth Edition Chapter 6 Foundations of Business Intelligence: Databases and Information Management Copyright © 2020, 2018, 2016 Pearson Education, Inc. All Rights Reserved Learning Objectives 6.1 What are the problems of managing data resources in a traditional file environment? 6.2 What are the major capabilities of database management systems (DBMS), and why is a relational DBMS so powerful? 6.3 What are the principal tools and technologies for accessing information from databases to improve business performance and decision making? 6.4 Why are information policy, data administration, and data quality assurance essential for managing the firm’s data resources? Copyright © 2020, 2018, 2016 Pearson Education, Inc. All Rights Reserved File Organization Terms and Concepts Database: Group of related files File: Group of records of same type Record: Group of related fields Field: Group of characters as word(s) or number(s) Entity: Person, place, thing on which we store information Attribute: Each characteristic, or quality, describing entity Copyright © 2020, 2018, 2016 Pearson Education, Inc. All Rights Reserved Figure 6.1 The Data Hierarchy Copyright © 2020, 2018, 2016 Pearson Education, Inc. All Rights Reserved Problems with the Traditional File Environment Files maintained separately by different departments Data redundancy Data inconsistency Program-data dependence Lack of flexibility Poor security Lack of data sharing and availability Copyright © 2020, 2018, 2016 Pearson Education, Inc. All Rights Reserved Figure 6.2 Traditional File Processing Copyright © 2020, 2018, 2016 Pearson Education, Inc. All Rights Reserved Database Management Systems Database – Serves many applications by centralizing data and controlling redundant data Database management system (DBMS) – Interfaces between applications and physical data files – Separates logical and physical views of data – Solves problems of traditional file environment  Controls redundancy  Eliminates inconsistency  Uncouples programs and data  Enables organization to centrally manage data and data security Copyright © 2020, 2018, 2016 Pearson Education, Inc. All Rights Reserved Figure 6.3 Human Resources Database with Multiple Views Copyright © 2020, 2018, 2016 Pearson Education, Inc. All Rights Reserved Relational DBMS Represent data as two-dimensional tables Each table contains data on entity and attributes Table: grid of columns and rows – Rows (tuples): Records for different entities – Fields (columns): Represents attribute for entity – Key field: Field used to uniquely identify each record – Primary key: Field in table used for key fields – Foreign key: Primary key used in second table as look- up field to identify records from original table Copyright © 2020, 2018, 2016 Pearson Education, Inc. All Rights Reserved Figure 6.4 Relational Database Tables Copyright © 2020, 2018, 2016 Pearson Education, Inc. All Rights Reserved Operations of a Relational DBMS Three basic operations used to develop useful sets of data – SELECT  Creates subset of data of all records that meet stated criteria – JOIN  Combines relational tables to provide user with more information than available in individual tables – PROJECT  Creates subset of columns in table, creating tables with only the information specified Copyright © 2020, 2018, 2016 Pearson Education, Inc. All Rights Reserved Figure 6.5 The Three Basic Operations of a Relational DBMS Copyright © 2020, 2018, 2016 Pearson Education, Inc. All Rights Reserved Capabilities of Database Management Systems Data definition capability Data dictionary Querying and reporting – Data manipulation language  Structured Query Language (SQL) Many DBMS have report generation capabilities for creating polished reports (Microsoft Access) Copyright © 2020, 2018, 2016 Pearson Education, Inc. All Rights Reserved Figure 6.6 Access Data Dictionary Features Copyright © 2020, 2018, 2016 Pearson Education, Inc. All Rights Reserved Figure 6.7 Example of an SQ L Query Copyright © 2020, 2018, 2016 Pearson Education, Inc. All Rights Reserved Figure 6.8 An Access Query Copyright © 2020, 2018, 2016 Pearson Education, Inc. All Rights Reserved Designing Databases Conceptual design vs. physical design Normalization – Streamlining complex groupings of data to minimize redundant data elements and awkward many-to-many relationships Referential integrity – Rules used by RDBMS to ensure relationships between tables remain consistent Entity-relationship diagram A correct data model is essential for a system serving the business well Copyright © 2020, 2018, 2016 Pearson Education, Inc. All Rights Reserved Figure 6.9 An Unnormalized Relation for Order Copyright © 2020, 2018, 2016 Pearson Education, Inc. All Rights Reserved Figure 6.10 Normalized Tables Created from Order Copyright © 2020, 2018, 2016 Pearson Education, Inc. All Rights Reserved Figure 6.11 An Entity-Relationship Diagram Copyright © 2020, 2018, 2016 Pearson Education, Inc. All Rights Reserved Non-Relational Databases and Databases in the Cloud Non-relational databases: “No SQL” – More flexible data model – Data sets stored across distributed machines – Easier to scale – Handle large volumes of unstructured and structured data Databases in the cloud – Appeal to start-ups, smaller businesses – Amazon Relational Database Service, Microsoft SQL Azure – Private clouds Copyright © 2020, 2018, 2016 Pearson Education, Inc. All Rights Reserved Blockchain Distributed ledgers in a peer-to-peer distributed database Maintains a growing list of records and transactions shared by all Encryption used to identify participants and transactions Used for financial transactions, supply chain, and medical records Foundation of Bitcoin, and other crypto currencies Copyright © 2020, 2018, 2016 Pearson Education, Inc. All Rights Reserved Figure 6.12 How Blockchain Works Copyright © 2020, 2018, 2016 Pearson Education, Inc. All Rights Reserved The Challenge of Big Data Big data – Massive sets of unstructured/semi-structured data from web traffic, social media, sensors, and so on Volumes too great for typical DBMS – Petabytes, exabytes of data Can reveal more patterns, relationships and anomalies Requires new tools and technologies to manage and analyze Copyright © 2020, 2018, 2016 Pearson Education, Inc. All Rights Reserved Business Intelligence Infrastructure (1 of 3) Array of tools for obtaining information from separate systems and from big data Data warehouse – Stores current and historical data from many core operational transaction systems – Consolidates and standardizes information for use across enterprise, but data cannot be altered – Provides analysis and reporting tools Copyright © 2020, 2018, 2016 Pearson Education, Inc. All Rights Reserved Business Intelligence Infrastructure (2 of 3) Data marts – Subset of data warehouse – Typically focus on single subject or line of business Hadoop – Enables distributed parallel processing of big data across inexpensive computers – Key services  Hadoop Distributed File System (HDFS): data storage  MapReduce: breaks data into clusters for work  Hbase: No SQL database – Used Yahoo, NextBio Copyright © 2020, 2018, 2016 Pearson Education, Inc. All Rights Reserved Business Intelligence Infrastructure (3 of 3) In-memory computing – Used in big data analysis – Uses computers main memory (RAM) for data storage to avoid delays in retrieving data from disk storage – Can reduce hours/days of processing to seconds – Requires optimized hardware Analytic platforms – High-speed platforms using both relational and non- relational tools optimized for large datasets Copyright © 2020, 2018, 2016 Pearson Education, Inc. All Rights Reserved Figure 6.13 Contemporary Business Intelligence Infrastructure Copyright © 2020, 2018, 2016 Pearson Education, Inc. All Rights Reserved Analytical Tools: Relationships, Patterns, Trends Tools for consolidating, analyzing, and providing access to vast amounts of data to help users make better business decisions – Multidimensional data analysis (OLAP) – Data mining – Text mining – Web mining Copyright © 2020, 2018, 2016 Pearson Education, Inc. All Rights Reserved Online Analytical Processing (O L A P) Supports multidimensional data analysis – Viewing data using multiple dimensions – Each aspect of information (product, pricing, cost, region, time period) is different dimension – Example: How many washers sold in the East in June compared with other regions? OL AP enables rapid, online answers to ad hoc queries Copyright © 2020, 2018, 2016 Pearson Education, Inc. All Rights Reserved Figure 6.14 Multidimensional Data Model Copyright © 2020, 2018, 2016 Pearson Education, Inc. All Rights Reserved Data Mining Finds hidden patterns, relationships in datasets – Example: customer buying patterns Infers rules to predict future behavior Types of information obtainable from data mining: – Associations – Sequences – Classification – Clustering – Forecasting Copyright © 2020, 2018, 2016 Pearson Education, Inc. All Rights Reserved Text Mining and Web Mining Text mining – Extracts key elements from large unstructured data sets – Sentiment analysis software Web mining – Discovery and analysis of useful patterns and information from web – Web content mining – Web structure mining – Web usage mining Copyright © 2020, 2018, 2016 Pearson Education, Inc. All Rights Reserved Databases and the Web Many companies use the web to make some internal databases available to customers or partners Typical configuration includes: – Web server – Application server/middleware/CGI scripts – Database server (hosting DBMS) Advantages of using the web for database access: – Ease of use of browser software – Web interface requires few or no changes to database – Inexpensive to add web interface to system Copyright © 2020, 2018, 2016 Pearson Education, Inc. All Rights Reserved Figure 6.15 Linking Internal Databases to the Web Copyright © 2020, 2018, 2016 Pearson Education, Inc. All Rights Reserved Establishing an Information Policy Firm’s rules, procedures, roles for sharing, managing, standardizing data Data administration – Establishes policies and procedures to manage data Data governance – Deals with policies and processes for managing availability, usability, integrity, and security of data, especially regarding government regulations Database administration – Creating and maintaining database Copyright © 2020, 2018, 2016 Pearson Education, Inc. All Rights Reserved Ensuring Data Quality More than 25 percent of critical data in Fortune 1000 company databases are inaccurate or incomplete Before new database is in place, a firm must: – Identify and correct faulty data – Establish better routines for editing data once database in operation Data quality audit Data cleansing Copyright © 2020, 2018, 2016 Pearson Education, Inc. All Rights Reserved Copyright This work is protected by United States copyright laws and is provided solely for the use of instructors in teaching their courses and assessing student learning. Dissemination or sale of any part of this work (including on the World Wide Web) will destroy the integrity of the work and is not permitted. The work and materials from it should never be made available to students except by instructors using the accompanying text in their classes. All recipients of this work are expected to abide by these restrictions and to honor the intended pedagogical purposes and the needs of other instructors who rely on these materials. Copyright © 2020, 2018, 2016 Pearson Education, Inc. All Rights Reserved