Presentation on theme: "CHAPTER 5 Data and Knowledge Management. CHAPTER OUTLINE 5.1 Managing Data 5.2 The Database Approach 5.3 Database Management Systems 5.4 Data Warehouses."— Presentation transcript:
CHAPTER 5 Data and Knowledge Management
CHAPTER OUTLINE 5.1 Managing Data 5.2 The Database Approach 5.3 Database Management Systems 5.4 Data Warehouses and Data Marts 5.5 Knowledge Management
LEARNING OBJECTIVES 1. Identify three common challenges in managing data, and describe one way organizations can address each challenge using data governance. 2. Name six problems that can be minimized by using the database approach. 3. Demonstrate how to interpret relationships depicted in an entity-relationship diagram. 4. Discuss at least one main advantage and one main disadvantage of relational databases.
Learning Objectives (continued) 5. Identify the six basic characteristics of data warehouses, and explain the advantages of data warehouses and marts to organizations. 6. Demonstrate the use of a multidimensional model to store and analyze data. 7. List two main advantages of using knowledge management, and describe the steps in the knowledge management system cycle.
Big Data Case – pages 112 & 113 Walmart processes over 1,000,000 transactions per hour From 2006 to 2010 IBM invested over $12,000,000,000 for setting up business intelligence centers Using big data to spot trends before your competitors spot them can be a strategic advantage (Best Buy success, Nestle failure)
Annual Flood of Data from….. Credit card swipes s Digital video Online TV RFID tags Blogs Digital video surveillance Radiology scans Source: Media Bakery
5.1 Managing Data The Difficulties of Managing Data Data Governance
Difficulties in Managing Data Source: Media Bakery
Data Governance See videovideo Data Governance – manage data across the entire organization Master Data Management – have all organization processes access a single version of the data Master Data – an enterprise system of core data Big data can have big data errors
Master Data Management John Stevens registers for Introduction to Management Information Systems (ISMN 3140) from 10 AM until 11 AM on Mondays and Wednesdays in Room 41 Smith Hall, taught by Professor Rainer. Transaction Data Master Data John StevensStudent Intro to Management Information SystemsCourse ISMN 3140Course No. 10 AM until 11 AMTime Mondays and WednesdaysWeekday Room 41 Smith HallLocation Professor RainerInstructor
5.2 The Database Approach Database management system (DBMS) minimize the following problems: Data redundancy Data isolation Data inconsistency
Database Approach (continued) DBMSs maximize the following issues: Data security Data integrity Data independence
Database Management Systems
Data Hierarchy Bit Byte Field Record File (or table) Database A zero or a one 8 bits, a single character or number A column in a spreadsheet like a name A row in a spreadsheet like name and address and phone # A collection of related records A collection of related files
Hierarchy of Data for a Computer-Based File
Data Hierarchy (continued) Bit (binary digit) Byte (eight bits)
Data Hierarchy (continued) Example of Field and Record
Data Hierarchy (continued) Example of Field and Record
Designing the Database Data model Entity Attribute Primary key Secondary keys The data model is a diagram that represents the entities in the database and their relationships. An entity is a person, place, thing, or event about which information is maintained. A record generally describes an entity. An attribute is a particular characteristic or quality of a particular entity. The primary key is a field that uniquely identifies a record. Secondary keys are other field that have some identifying information but may not identify the file with complete accuracy.
Entity-Relationship Modeling Database designers plan the database design in a process called entity-relationship (ER) modeling. ER diagrams consists of entities, attributes and relationships. Entity classes Instance Identifiers
Relationships Between Entities (see page 120) Maximum number of instances Minimum number of instances
Entity-relationship diagram model
5.3 Database Management Systems Database management system (DBMS) [defines both the data structure and the data relationships] Relational database model Structured Query Language (SQL) Query by Example (QBE) One table is a flat file, it is the relationship between tables that make a database
Student Database Example Can you determine an attribute? A primary key? A secondary key? An instance?
Normalization Minimum redundancy Maximum data integrity Best processing performance Normalized data occurs when attributes in the table depend only on the primary key.
Normalizing the Database (part A)
Normalizing the Database (part B)
Normalization Produces Order
5.4 Data Warehousing Data warehouses and Data Marts Organized by business dimension or subject Multidimensional Historical Use online analytical processing A data warehouse is a repository of historical data organized by subject to support decision makers in the organization. Historical data in data warehouses can be used for identifying trends, forecasting, and making comparisons over time. Online analytical processing (OLAP) involves the analysis of accumulated data by end users (usually in a data warehouse). In contrast to OLAP, online transaction processing (OLTP) typically involves a database, where data from business transactions are processed online as soon as they occur.
Data Warehouse Framework & Views
Equivalence Between Relational and Multidimensional Databases
Benefits of Data Warehousing End users can access data quickly and easily via Web browsers because they are located in one place. End users can conduct extensive analysis with data in ways that may not have been possible before. End users have a consolidated view of organizational data.
Data Concepts Metadata – data about data such as relationships between tables or table definitions Data quality – data is seldom 100% clean Data governance (link)link Users include information producers and consumers