1. Data and Knowledge Management
CHAPTER 4
Data and Knowledge Management

2. CHAPTER OUTLINE 4.1 Managing Data 4.2 The Database Approach
4.3 Database Management Systems
4.4 Data Warehousing
4.5 Data Governance
4.6 Knowledge Management

3. LEARNING OBJECTIVES
Recognize the importance of data, issues involved in managing data and their lifecycle.
Describe the sources of data and explain how data are collected.
Explain the advantages of the database approach.

4. Learning Objectives (continued)
Explain the operation of data warehousing and its role in decision support.
Explain data governance and how it helps to produce high-quality data.
Define knowledge, and describe different types of knowledge.

5. Chapter Opening Case

6. Chapter Opening Case (continued)
Push Model
Products

7. Chapter Opening Case (continued)
Pull Model
Orders

8. Examples of Data Sources
RFID tags
Credit card swipes
Digital video surveillance
Blogs s
Radiology scans

9. 4.1 Managing Data Difficulties in Managing Data
Amount of data increases exponentially.
Data are scattered and collected by many individuals using various methods and devices.
Data come from many sources.
Data security, quality and integrity are critical.

10. Difficulties in Managing Data (continued)
An ever-increasing amount of data needs to be considered in making organizational decisions.
The Data Deluge

11. Data Life Cycle (Figure 4.1)
Figure 4.1 illustrates the processing of data into information and then knowledge.

12. Data, Information, Knowledge, Wisdom
This figure puts data, information, knowledge, and wisdom into perspective.

13. 4.2 The Database Approach
Database management system (DBMS) provides all users with access to all the data.
DBMSs minimize the following problems:
Data redundancy
Data isolation
Data inconsistency
Data redundancy: The same data are stored in many places.
Data isolation: Applications cannot access data associated with other applications.
Data inconsistency: Various copies of the data do not agree.

14. Database Approach (continued)
DBMSs maximize the following issues:
Data security
Data integrity
Data independence
Data security: Keeping the organization’s data safe from theft, modification,
and/or destruction.
Data integrity: Data must meet constraints (e.g., student grade point averages
cannot be negative).
Data independence: Applications and data are independent of one another.
applications and data are not linked to each other, meaning that
applications are able to access the same data.

15. Database Management Systems

16. Data Hierarchy Bit Byte Field Record File (or table) Database
A bit is a binary digit, or a “0” or a “1”.
A byte is eight bits and represents a single character (e.g., a letter, number or symbol).
A field is a group of logically related characters (e.g., a word, small group of words,
or identification number).
A record is a group of logically related fields (e.g., student in a university database).
A file is a group of logically related records.
A database is a group of logically related files.

17. Hierarchy of Data for a Computer-Based File

18. Data Hierarchy (continued)
Bit (binary digit)
Byte (eight bits)

19. Data Hierarchy (continued)
Example of Field and Record

20. Data Hierarchy (continued)
Example of Field and Record

21. 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 typically do not
identify the file with complete accuracy.

22. 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
Entity classes are groups of entities of a certain type.
An instance of an entity class is the representation of a particular entity.
Entity instances have identifiers, which are attributes that are unique to that entity instance.

23. Entity-Relationship Diagram Model

24. 4.3 Database Management Systems
Database management system (DBMS)
Relational database model
Structured Query Language (SQL)
Query by Example (QBE)
A database management system is a set of programs that provide users with tools to add,
delete, access, and analyze data stored in one location.
The relational database model is based on the concept of two-dimensional tables.
Structured query language allows users to perform complicated searches by using
relatively simple statements or keywords.
Query by example allows users to fill out a grid or template to construct a sample or
description of the data he or she wants.

25. Student Database Example

26. Normalization
Normalization is a method for analyzing and reducing a relational database to its most streamlined form for:
Minimum redundancy
Maximum data integrity
Best processing performance
Normalized data is when attributes in the table depend only on the primary key.

27. Non-Normalized Relation

28. Normalizing the Database (part A)

29. Normalizing the Database (part B)

30. Normalization Produces Order

31. Turnitin (IT’s About Business 4.1)
A Turnitin originality report

32. 4.4 Data Warehousing Data warehouse
Data warehouses are organized by business dimension or subject.
Data warehouses are multidimensional.
A data warehouse is a repository of historical data organized by subject to support
decision makers in the organization.
The data cube has three dimensions: customer, product, and time.
A Data Cube

33. Data Warehousing (continued)
Data warehouses are historical.
Data warehouses use online analytical processing.
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.

34. Data Warehouse Framework & Views
This figure (Figure 4.9) shows the process of building and using a data warehouse.

35. Relational Databases
This is the first slide (Figure 4.10) of five showing the relationship between relational databases
and a multidimensional data structure (or data cube).

36. Multidimensional Database
Figure 4.11 a, b, and c.

37. Equivalence Between Relational and Multidimensional Databases
Figure 4.12 a, b, and c.

38. Equivalence Between Relational and Multidimensional Databases

39. Equivalence Between Relational and Multidimensional Databases

40. 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.

41. Data Marts
A data mart is a small data warehouse, designed for the end-user needs in a strategic business unit (SBU) or a department.

42. 4.5 Data Governance Data governance Master data management Master data
Data governance is an approach to managing data and information across an
entire organization.
Master data management is a method that organizations use in data governance.
Master data are a set of core data that span all enterprise information systems.

43. Data Governance (continued)
This slide shows the relationship among executive management,
IT governance, and data governance.
The slide also shows the relationship between data governance and
data management.
The green square should really read “master data management”
rather than just “data management” as we see on the next slide.

44. Data Governance (continued)
This image shows where data governance and master data management
fit into the organization’s IT governance.

45. 4.6 Knowledge Management Knowledge management (KM) Knowledge
Intellectual capital (or intellectual assets)
Knowledge management is a process that helps organizations manipulate important
knowledge that is part of the organization’s memory, usually in an unstructured format.
Knowledge that is contextual, relevant, and actionable.
Intellectual capital is another term often used for knowledge.

46. Knowledge Management (continued)
Explicit Knowledge (above the waterline)
Tacit Knowledge
(below the waterline)
Explicit knowledge: objective, rational, technical knowledge that has been documented.
Examples: policies, procedural guides, reports, products, strategies, goals, core competencies
Tacit knowledge: cumulative store of subjective or experiential learning.
Examples: experiences, insights, expertise, know-how, trade secrets, understanding,
skill sets, and learning

47. Knowledge Management (continued)
Knowledge management systems (KMSs)
Best practices
Knowledge management systems refer to the use of information technologies to systematize,
enhance, and expedite intrafirm and interfirm knowledge management.
Best practices are the most effective and efficient ways of doing things.

48. Knowledge Management System Cycle
Create knowledge
Capture knowledge
Refine knowledge
Store knowledge
Manage knowledge
Disseminate knowledge

49. Knowledge Management System Cycle

50. Chapter Closing Case
High CVM passengers
travel in style