Mata Kuliah : M0014 / Konsep Sistem Informasi
Tahun
: 2008
Pertemuan 15 - 16
Chapter 04 : Data and Knowledge Management
Learning Outcomes
• Mahasiswa dapat menerangkan data, daur hidup data,
hierarki data. (C2)
• Mahasiswa dapat menerangkan pemakaian data
dalam perusahaan. (C2)
Bina Nusantara
Rincian Materi
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Managing Data
The Database Approach
Database Management System
Data Warehousing
Data Visualization Technologies
Knowledge Management
Bina Nusantara
Introduction to Information
Technology
 Authors: Turban, Rainer and Potter
 Publisher: John Wiley & Sons, Inc.
 Slides by: Hellene Bankowski, Professor, Philadelphia University
Copyright 2007 John
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Chapter 4
Data and Knowledge Management
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Chapter Outline
 4.1 Managing Data
 4.2 The Database Approach
 4.3 Database Management Systems
 4.4 Data Warehousing
 4.5 Data Visualization
 4.6 Knowledge Management
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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.
 Explain the operation of data warehousing and its
role in decision support.
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Learning Objectives (Continued)
 Understand the capabilities and benefits of
data mining.
 Describe data visualization.
 Explain geographic information systems and
virtual reality as decision support tools.
 Define knowledge and describe the different
types of knowledge.
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4.1 Managing Data
 Difficulties of Managing Data.
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Amount of data increases exponentially.
Data are scattered and collected by many
individuals using various methods and devices.
Data come from many sources including internal
sources, personal sources and external sources.
Data security, quality and integrity are critical.
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Managing Data (Continued)
 Clickstream data. Data that visitors and
customers produce when they visit a Website.
 An ever-increasing amount of data needs to
be considered in making organizational
decisions.
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Data Life Cycle
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Data Hierarchy
 Bit (a binary digit): a circuit that is either on
or off.
 Byte: group of 8 bits, represents a single
character.
 Field: name, number, or characters that
describe an aspect of a business object or
activity.
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Data Hierarchy (Continued)
 Record: collection of related data
fields.
 File (or table): collection of related
records.
 Database: a collection of integrated and
related files.
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4.2 Database Approach
 Database management system (DBMS)
provides all users with access to all the data.
 DBMSs minimizes the following problems:
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Data redundancy: the same data stored in many places.
Data isolation: applications cannot access data
associated with other applications.
Data inconsistency: various copies of the data do not
agree.
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Database Approach (Continued)
 DBMSs maximize the following issues:
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Data security.
Data integrity: data meets certain constraints, no
alphabetic characters in zip code field.
Data independence: applications and data are
independent of one another, all applications are
able to access the same data.
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Designing the Database
 Data model. Diagram that represents the entities in
the database and their relationships.
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Entity is a person, place, thing or event.
Attribute is a characteristic or quality of a particular
entity.
Primary key is a field that uniquely identifies that
record.
Secondary keys are fields that have identifying
information but may not identify with complete accuracy.
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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 are a group of entities of a given
type, i.e. STUDENT.
 Instance is the representation of a particular entity,
i.e. STUDENT(John Smith, 123-45-6789, …).
 Identifiers are attributes unique to that entity
instance, i.e. StudentIDNumber.
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4.3 Database Management
Systems
 Database management system (DBMS) is a set of
programs that provide users with tools to add,
delete, access and analyze data stored in one
location.
 Online transaction processing (OLTP) is when
transactions are processed as soon as they occur.
 Relational database model is based on the concept
of two-dimensional tables.
 Popular examples of relational databases are
Microsoft Access and Oracle.
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Query Languages
 Structured query language (SQL) is the
most popular query language used to request
information.
 Query by example (QBE) is a grid or
template that a user fills out to construct a
sample or description of the data wanted.
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Relational Database Management
Systems
 Normalization is a method for analyzing and
reducing a relational database to its most
streamlined form for:
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Mimimum redunancy;
Maximum data integrity;
Best processing performance.
 Normalized data is when attributes in the
table depend only on the primary key.
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Virtual Databases
 Software applications that provide a way of
managing many different data sources as
though they were all one large database.
 Benefits of virtual databases include:
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Lower development costs;
Faster development time;
Less maintenance;
Single point of entry into a company’s data.
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4.4 Data Warehousing
 Data warehouse is a repository of historical
data organized by subject to support decision
makers in the organization and include:
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Online analytical processing which involves
the analysis of accumulated data by end users;
Multidimensional data structure which allows
data to be represented in a three-dimensional
matrix (or data cube).
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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.
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Data Marts & Data Mining
 Data mart is a small data warehouse,
designed for the end-user needs in a strategic
business unit (SBU) or a department.
 Data mining involves searching for valuable
business information in a large database, data
warehouse, or data mart.
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Used to predict trends and behaviors.
Identify previously unknown patterns.
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Data Mining Applications
 Retailing and sales. Predict sales, prevent
theft and fraud, determine correct inventory
levels and distribution schedules.
 Banking. Forecast levels of bad loans,
fraudulent credit card use, predict credit card
spending by new customers, etc.
 Manufacturing and production. Predict
machinery failures, find key factors to help
optimize manufacturing capacity.
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 Insurance. Forecast
claim amounts, medical25
Data Mining Applications
(Continued)
 Policework. Track crime patterns, locations,
criminal behavior; identify attributes to assist in
solving criminal cases.
 Health care. Correlate demographics of patients
with critical illnesses, develop better insight to
identify and treat symptoms and their causes.
 Marketing. Classify customer demographics to
predict how customers will respond to mailing or
buy a particular product.
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4.5 Data Visualization
Technologies
 Geographic Information Systems (GIS) is a
computer-based system for capturing, integrating,
manipulating and displaying data using digitized
maps.
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Find locations for new restaurants.
Emerging GIS applications integrated with global
positioning systems (GPSs).
 Virtual Reality is interactive, computer-generated,
three-dimensional graphics delivered to the user
through a head-mounted display.
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4.6 Knowledge Management
 Knowledge management (KM) is a process that
helps organizations manipulate important
knowledge that is part of the organization’s
memory, usually in an unstructured format.
 Knowledge is information that is contextual,
relevant and actionable; information in action.
 Intellectual capital (or intellectual assets) is
another term often used for knowledge.
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Knowledge Management
(Continued)
 Explicit knowledge deals with more
objective, rational and technical knowledge.
 Tacit knowledge is the cumulative store of
subjective or experiential learning.
 Knowledge management systems (KMSs)
use modern information technologies –
Internet, intranets, extranets, data warehouses
- to systemize, enhance and expedite
intrafirm and interfirm knowledge
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Knowledge Management System
Cycle
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Create knowledge. Determine new ways.
Capture knowledge. Identify as valuable.
Refine knowledge. Make it actionable.
Store knowledge. Store in a reasonable format.
Manage knowledge. Verify it is relevant, accurate.
Disseminate knowledge. Made available.
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