Management Information Systems 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 Videos Used in this Class Blockchain- https://www.youtube.com/watch?v=SSo_EIwHSd4 Big Data- https://www.youtube.com/watch?v=GEfsltXnCo4 Business Intelligence- https://www.youtube.com/watch?v=0aHtHl-jcAs Data Mining: https://www.youtube.com/watch?v=7rs0i-9nOjo Data Mining: https://www.youtube.com/watch?v=81bm2OsEzbg OLAP: https://www.youtube.com/watch?v=P7hf_emjsRI 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? 6.5 How will MIS help my career? Copyright © 2020, 2018, 2016 Pearson Education, Inc. All Rights Reserved Video Cases Case 1: Dubuque Uses Cloud Computing and Sensors to Build a Smarter City Case 2: Brooks Brothers Closes In on Omnichannel Retail Case 3: Maruti Suzuki Business Intelligence and Enterprise Databases Copyright © 2020, 2018, 2016 Pearson Education, Inc. All Rights Reserved Data Management Helps the Charlotte Hornets Learn More About Their Fans Problem – Large volumes of data in isolated databases – Outdated data management technology Solutions – SA P H AN A – Data warehouse – FanTracker Illustrates the importance of data management for better decision making and customer analysis 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-is the presence of duplicate data in multiple data files so the same data are stored in more than one place or location Data inconsistency-where the same attribute may have different values Program-data dependence- refers to the coupling of data stored in files and the specific programs required to update and maintain those files such that changes in programs require changes to the data. Lack of flexibility-A traditional file system can deliver routine scheduled reports after extensive programming efforts, but it cannot deliver ad hoc reports or respond to unanticipated information requirements in a timely fashion. Poor security-Because there is little control or management of data, access to and dissemination of information may be out of control. Management may have no way of knowing who is accessing or even making changes to the organization’s data. Lack of data sharing and availability- Information cannot flow freely across different functional areas or different parts of the organization. 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 (DBM S)- is software that permits an organization to centralize data, manage them efficiently, and provide access to the stored data by application programs – 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 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 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-to specify the structure of the content of the database. Data dictionary-is an automated or manual file that stores definitions of data elements and their characteristics. Querying and reporting – Data manipulation language-that is used to add, change, delete, and retrieve the data in the database  Structured Query Language (SQ L)- The most prominent data manipulation language today 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 SQL 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 RDBM S 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 SQ L 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 DBM S – 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 (HDF S): data storage  MapReduce: breaks data into clusters for work  Hbase: No SQ L 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 (RA M) 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 Interactive Session: Technology: Kraft Heinz Finds a New Recipe for Analyzing Its Data Class discussion – Identify the problem in this case study. To what extent was it a technology problem? Were any management and organizational factors involved? – How was information technology affecting business performance at Kraft Heinz? – How did new technology provide a solution to the problem? How effective was the solution? – Identify the management, organizational, and technology factors that had to be addressed in selecting and implementing Kraft- Heinz’s new data warehouse solution. 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 (O LA P) – 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? O L A P 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-are occurrences linked to a single event. – Sequences-events are linked over time – Classification-recognizes patterns that describe the group to which an item belongs by examining existing items that have been classified and by inferring a set of rules – Clustering-an discover different groupings within data – Forecasting-uses predictions in a different way. It uses a series of existing values to forecast what other values will be 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/CG I scripts – Database server (hosting DBM S) 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 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- is a structured survey of the accuracy and level of completeness of the data in an information system Data cleansing-also known as data scrubbing, consists of activities for detecting and correcting data in a database that are incorrect, incomplete, improperly formatted, or redundant. Copyright © 2020, 2018, 2016 Pearson Education, Inc. All Rights Reserved Interactive Session: Organizations: Databases Where the Data Aren’t There Class discussion – Define the problem described in this case. How serious a problem is it? – What management, organization, and technology factors contributed to this problem? – What is the political and social impact of incomplete recordkeeping in the FB I NCI C and NI C S databases? Copyright © 2020, 2018, 2016 Pearson Education, Inc. All Rights Reserved How Will MI S Help My Career? The Company: Mega Midwest Power Position Description: Entry-level data analyst Job Requirements Interview Questions Author Tips 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