1. Data and Knowledge Management
CHAPTER 5
Data and Knowledge Management

2. 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

3. 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.

4. 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.

5. 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)

6. Annual Flood of Data from…..
Credit card swipes s
Digital video
Online TV
RFID tags
Blogs
Digital video surveillance
Radiology scans
Source: Media Bakery

7. Annual Flood of New Data!
In the zettabyte range
A zettabyte is a trillion gigabytes
According to the annual survey of the global digital output by International Data Corporation, the total amount of global data was expected to pass 1.2 zettabytes sometime during This is equivalent to the amount of data that would be generated by everyone in the world posting messages on Twitter continuously for a century.[
© Fanatic Studio/Age Fotostock America, Inc.

8. 5.1 Managing Data The Difficulties of Managing Data Data Governance
Difficulties in managing data:
Amount of data increasing exponentially
Data are scattered throughout organizations and collected by many individuals
using various methods and devices.
Data come from many sources.
Data security, quality, and integrity are critical.

9. Difficulties in Managing Data
Difficult to manage data for many reasons:
Amount of data increasing exponentially over time;
Data are scattered throughout organizations;
Data obtained from multiple internal and external sources;
Data degrade over time;
Data subject to data rot;
Data security, quality, and integrity are critical, yet easily jeopardized;
Information systems that do not communicate with each other can result in inconsistent data;
Federal regulations.
Source: Media Bakery

10. Data Governance
Big data can have big data errors
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
Data governance is an approach to managing information across an entire organization.
Master data management is a process that spans all of an organization’s business processes
and applications.
Master data are a set of core data that span all of an enterprise’s information systems.
See video

11. 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 Stevens Student
Intro to Management Information Systems Course
ISMN Course No.
10 AM until 11 AM Time
Mondays and Wednesdays Weekday
Room 41 Smith Hall Location
Professor Rainer Instructor

12. 5.2 The Database Approach
Database management system (DBMS) 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.

13. 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.

14. Database Management Systems

15. 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 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.
A collection of related files

16. Hierarchy of Data for a Computer-Based File

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

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

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

20. 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.
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.

21. 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.

22. Relationships Between Entities (see page 120)
Maximum number of instances
Minimum number of instances

23. Entity-relationship diagram model

24. 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
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
Can you determine an attribute?
A primary key?
A secondary key?
An instance?

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

27. Non-Normalized Relation

28. Normalizing the Database (part A)

29. Normalizing the Database (part B)

30. Normalization Produces Order

31. Non-Normalized Relation

32. 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.
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.

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

34. Relational Databases
This is the first slide (Figure 3.10) of five showing the relationship between relational databases
and a multidimensional data structure (or data cube).

35. Multidimensional Database
Figure 3.11 a, b, and c.

36. Equivalence Between Relational and Multidimensional Databases
Figure 3.12 a, b, and c.

37. Equivalence Between Relational and Multidimensional Databases

38. Equivalence Between Relational and Multidimensional Databases

39. 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.

40. Data Concepts
Metadata – data about data such as relationships between tables or table definitions
Data quality – data is seldom 100% “clean”
Data governance (link)
Users include information producers and consumers

41. 5.5 Knowledge Management Knowledge management (KM)
Knowledge (should be contextual, relevant, and actionable)
Intellectual capital (a.k.a. knowledge 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.
© Peter Eggermann/Age Fotostock America, Inc.

42. Knowledge Management (continued)
Explicit Knowledge (above the waterline)
Tacit Knowledge
(below the waterline, all the stuff you know but that you don’t explicitly realize you know)
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
© Ina Penning/Age Fotostock America, Inc.

43. 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.
© Peter Eggermann/Age Fotostock America, Inc.

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

45. Knowledge Management System Cycle

46. Chapter Closing Case
The Problem
The Solution
The Results