Transcript Data Resource - Wordpress@VIU

7/17/2015
1
Professor Lili Saghafi
MANAGING INFORMATION TECHNOLOGY
• Lecture 4
THE DATA
RESOURCE
• By : Prof. Lili Saghafi
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PART 1: IT BUILDING BLOCKS
Building Blocks of Information Technology
Hardware
Software
Network
Copyright © 2011 Pearson Education,
Inc. publishing as Prentice Hall
Data
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WHY MANAGE DATA?
- What costs would your company incur if it did not comply
with SOX or other financial reporting laws?
- What would your company do if its critical business data were
destroyed?
- What costs would your company incur if sensitive data were
stolen or you violated HIPAA requirements to protect
healthcare data?
- How much time does your company spend reconciling
inconsistent data?
- How difficult is it to determine what data are stored about the
part of the business you manage?
- Do you know all the contacts a customer has with your
organization?
HIPAA
is the
federal
Health
Insurance
Portability
and
Accountabi
lity Act of
1996.
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the challenges related to supporting the
communication needs
•
•
•
•
•
Network availability and reliability
Data security
Ease of use (with zero onsite support)
Inexpensive to deploy and operate
Network throughput (data rate) and latency
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Networking Requirement
Status and Challenges
Network Availability and Reliability

Major challenge: dependable and reliable wireless
coverage
Easy to use and zero-support networking equipment

Depends
upon
the
technology
knowledge/experience of the user
Inexpensive

Dependent on the networking equipment and technology
available and its position on the technology maturity
curve
IT capital costs compete with medical equipment costs,
and typically the latter take precedent unless there is a
regulatory requirement

and
the
Network throughput (data rate) and latency

Technology continues to evolve: 4G wireless technology
is now being offered, but the 3G/4G definitions are
somewhat blurred
Security

Not a technology challenge, but an increasing
administrative challenge due to increased security rules
and state-level oversight
Easy acquisition and deployment of new networking
technologies

A key challenge here will be testing the new
technologies under different MMC conditions 6
TECHNICAL ASPECTS OF MANAGING DATA
DATA MODELS
• An overall “map” for business data
• Involves:
– A methodology (process) to identify and describe data
entities
– A notation = a way to describe data entities
Copyright © 2011 Pearson Education,
Inc. publishing as Prentice Hall
4-7
DATA MODEL: CONCEPTUAL DESIGN PHASE
ENTITY-RELATIONSHIP DIAGRAM (ERD)
- Entities = things about which data are
collected
(e.g., Customer, Order,
Product)
- Attributes = actual elements of data to be
collected
- Relationships = associations between entities
(e.g., Submits, Includes)
MOST COMMON DATA MODEL FOR CONCEPTUAL DESIGN PHASE
Copyright © 2011 Pearson Education,
Inc. publishing as Prentice Hall
4-8
Data Relationships and EntityRelationship Diagrams
• Entity-relationship diagrams (ERDs)
– An ERD is a graphical model that shows
relationships among system entities
Data Relationships and EntityRelationship Diagrams
• Entity-relationship diagrams (ERDs)
– An ERD is a graphical model that shows
relationships among system entities
– Each entity is a rectangle, labeled with a noun
– Each relationship is a diamond, labeled with a verb
– Types of relationships
• One-to-one (1:1)
• One-to-many (1:M)
• Many-to-many (M:N)
– A full ERD shows all system relationships
Data Relationships and EntityRelationship Diagrams
• One-to-one (1:1) relationship
– Exists when exactly one of the second entity
occurs for each instance of the first entity
– Examples
•
•
•
•
One office manager heads one office
One vehicle ID number is assigned to one vehicle
One driver drives one delivery truck
One faculty member is chairperson of one department
Data Relationships and EntityRelationship Diagrams
• One-to-many (1:M) relationship
– Exists when one occurrence of the first entity
can be related to many occurrences of the
second entity, but each occurrence of the
second entity can be associated with only one
occurrence of the first entity
– Examples
•
•
•
•
One individual owns many automobiles
One customer places many orders
One department employs many employees
One faculty advisor advises many students
Data Relationships and EntityRelationship Diagrams
• Many-to-many (M:N) relationship
– Exists when one instance of the first entity can be
related to many instances of the second entity,
and one instance of the second entity can be
related to many instances of the first
– Examples
• A student enrolls in one or more classes, and each
class has one or more students registered
• A passenger buys tickets for one or more flights, and
each flight has one or more passengers
• An order lists one or more products, and each product
is listed on one or more orders
Data Relationships and EntityRelationship Diagrams
• A full ERD shows all system relationships
– Examples
• A sales rep serves one or more customers, but
each customer has only one sales rep
• A customer places one or more orders, but each
order has only one customer
• An order lists one or more products, and each
product can be listed in one or more orders
• A warehouse stores one or more products, and
each product can be stored in one or more
warehouses
Data Relationships and EntityRelationship Diagrams
• Cardinality
– Describes how instances of one entity relate to
another
– Mandatory vs. optional relationships
– Crow’s foot notation is one method of showing
cardinality
Data Relationships and EntityRelationship Diagrams
• Cardinality
– Describes how instances of one entity relate to
another
– Mandatory vs. optional relationships
– Crow’s foot notation is one method of showing
cardinality
– Most CASE products support the drawing of ERDs
Data Relationships and EntityRelationship Diagrams
• Creating an ERD
1. Consider the nature of business
2. Identify the entities
2. Determine all significant events or activities
for two or more entities
3. Analyze the nature of the interaction
4. Draw the ERD
Relationships within Relational
Database
• Relationship classifications
– 1:1
– 1:M
– M:N
• E-R Model
– ERD Maps E-R model
– Chen
– Crow’s Feet
TECHNICAL ASPECTS
METADATA
• Data about data
• Unambiguous data description
• Documents “business rules” that govern data
(e.g., type of data such as alphanumeric;
whether a name can change; etc.
• Quality data requires high-quality metadata
Copyright © 2011 Pearson Education,
Inc. publishing as Prentice Hall
4-28
DATA MODEL: LOGICAL DESIGN PHASE
NOTATION
• ERDs are converted into sets of Relations, or
Tables:
– Structure consisting of rows and columns
– Each row represents a single entity
– Each column represents an attribute
Copyright © 2011 Pearson Education,
Inc. publishing as Prentice Hall
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DATA MODELING
LOGICAL DESIGN NOTATION
ERD Example:
to relations:
Copyright © 2011 Pearson Education,
Inc. publishing as Prentice Hall
Convert ERD
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TECHNICAL ASPECTS: DATA MODELING
ENTERPRISE MODELING
- Top-down approach
- High-level model
- Describes organization and data requirements at
high level, independent of reports, screens, or
detailed descriptions of data processing
requirements
Copyright © 2011 Pearson Education,
Inc. publishing as Prentice Hall
4-37
ENTERPRISE MODELING
Future-oriented Corporate Data Model
1. Divide work into major
functions
2. Divide each function
into processes
3. Divide processes into
activities (e.g., forecast
sales for next quarter)
4. List data entities
assigned to each activity
5. Check for consistent
names
Copyright © 2011 Pearson Education,
Inc. publishing as Prentice Hall
4-38
TECHNICAL ASPECTS: DATA MODELING
VIEW INTEGRATION
– Bottom-up approach
– Each report, screen, form, and document
produced from databases (called user views) is
identified
1. Create user views
2. Identify data element in each user view and put into a
structure called a normal form
3. Normalize user views
4. Combine user views
5. Reconcile any differences with enterprise model
Copyright © 2011 Pearson Education,
Inc. publishing as Prentice Hall
4-39
TECHNICAL ASPECTS: DATA MODELING
NORMALIZATION
• The process of creating simple data structures from more complex ones
using a set of rules that yields a stable structure.
Copyright © 2011 Pearson Education,
Inc. publishing as Prentice Hall
Source: Kenneth C. Laudon and Jane P. Laudon
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TECHNICAL ASPECTS: DATA MODELING
PACKAGED (UNIVERSAL) DATA MODELS
• Advantages:
- Developed using proven components
- Requires less time and money
- Easier to evolve
- Will easily work with other applications from the
same vendor
- Provides a starting point for requirements
- Promotes holistic and flexible views
- Easier to share data across organizations in same
industry
Copyright © 2011 Pearson Education,
4-41
Inc. publishing as Prentice Hall
TECHNICAL ASPECTS: DATA MODELING
DATA MODELING GUIDELINES
Objective
Some overriding need
Scope
Coverage for a data model
Outcome
The more uncertain the outcome, the
lower the chances for success
Start with high-level model and fill in
details as major systems projects
undertaken
Timing
Copyright © 2011 Pearson Education,
Inc. publishing as Prentice Hall
4-42
TECHNICAL ASPECTS: DATA MODELING
DATABASE PROGRAMMING
Database processing activity can be specified
with a:
- Procedural language (3GL)
- One or more special purpose languages
(4GL)
Structured query language (SQL)
Data exchange language (XML)
Example: SQL Query
SELECT OrderID, CustomerID, CustomerName, OrderDate
FROM Customer, Order
Copyright
2011 Pearson
Education, AND
WHERE ©OrderDate
> ‘04/12/11’
Inc.
publishing
as
Prentice
Customer.CustomerID = Hall
Order.CustomerID
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MANAGERIAL ISSUES
1.
2.
3.
4.
5.
6.
7.
PRINCIPLES IN MANAGING DATA
The need to manage data is permanent.
Data can exist at several levels within the
organization.
Application software should be separate from the
database.
Application software can be classified by how it
treats data.
Application software should be considered
disposable.
Data should be captured once.
There should be strict data standards.
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Albert Einstein defined insanity
as “doing the same thing over
and over again and expecting
different results.”
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Perhaps throwing appliances at
a network to make IT security
headaches go away fits this
definition.
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MANAGERIAL ISSUES
PRINCIPLES
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PRINCIPLES IN MANAGING DATA
1. The Need to Manage Data is Permanent
• Data values may change, but a company will always have
customers, products, employees, etc. about which it needs to
keep current data
• Business processes will change, but only the programs will
need to be rewritten
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PRINCIPLES IN MANAGING DATA
2. Data can exist at several levels within an organization
• Most new data are captured in operational databases
• Managerial and strategic databases typically subsets,
summaries, or aggregates of operational databases
• If managerial databases are constructed from external sources,
there may be problems with data consistency
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PRINCIPLES IN MANAGING DATA
3. Application Software should be separate from the database
• Application independence = separation or decoupling of
data from application systems
- Raw data captured and stored
- When needed, data are retrieved but not consumed
- Data are transferred to other parts of the organization when
authorized
• Meaning and structure of data not hidden from other
applications
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PRINCIPLES IN MANAGING DATA
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PRINCIPLES IN MANAGING DATA
4. Application Software can be classified by how it treats data
Data capture: gather data and populate the database
Data transfer: move data from one database to another or
otherwise bring data together
Data analysis and presentation: provide data and information to
authorized persons
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PRINCIPLES IN MANAGING DATA
5. Application Software should be considered disposable
Due to application independence:
- Company can replace the capture, transfer, and presentation
software modules separately if necessary
- Applications and data are not intertwined
- Aging systems do not need to be retained because of the
need to access the data stored in them
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PRINCIPLES IN MANAGING DATA
6. Data should be captured once
• Too costly to capture data multiple times and reconcile across
applications
• Instead, data should be captured once and synchronized across
different databases
• Data architecture should include inventory of data and plan to
distribute data
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PRINCIPLES IN MANAGING DATA
7. There should be strict data standards
• Data must be clearly identified and defined so that all users know
exactly what they are manipulating
• Only business managers have the knowledge necessary to set data
standards
• Database contents must be unambiguously described, and stored
in a metadata repository or data dictionary/directory (DD/D)
Data steward
A business manager responsible for the quality of data in a
particular subject or process area
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PRINCIPLES IN MANAGING DATA
5 TYPES OF DATA STANDARDS
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MANAGERIAL ISSUES
• Master data management (MDM):
disciplines, technologies, and methods
to ensure the currency, meaning, and quality
of reference data within and across subject
areas
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DATA MANAGEMENT PROCESS
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DATA MANAGEMENT PROCESS
• Plan: develop a blueprint for data and the relationships among data
across business units and functions
• Source: identify the timeliest and highest-quality source for each
data element
• Acquire and maintain: build data capture systems to acquire and
maintain data
• Define/describe and inventory: define each data entity, element, and
relationship that is being managed
• Organize and make accessible: design the database so that data can
be retrieved and reported efficiently in the format that business
managers require
o One popular method to make data accessible is to create a Data Warehouse
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DATA MANAGEMENT PROCESS
Data Warehouse
a large data storage facility containing data on major
aspects of the enterprise
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DATA MANAGEMENT PROCESS, CONT.
• Control quality and integrity: controls must be stored as part of data
definitions and enforced during data capture and maintenance
• Protect and secure: define rights that each manager has to access
each type of data
• Account for use: cost to capture, maintain, and report data must be
identified and reported with an accounting system
• Recover/restore and upgrade: establish procedures for recovering
damaged and upgrading obsolete hardware and software
• Determine retention and dispose: decide, on legal and other
grounds, how much data history needs to be kept
• Train and consult for effective use: train users to use data effectively
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MANAGERIAL ISSUES
DATA MANAGEMENT POLICIES
• Two key policy areas for data governance:
- Data ownership
- Data administration
• Data governance
- Data governance council sets standards about
metadata, data ownership and access, and data
infrastructure and architecture
- High-level oversight for establishing strategy,
objectives, and policies for organizational data
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MANAGERIAL ISSUES
DATA OWNERSHIP
Rationales for data ownership:
- The need to protect personal privacy, trade secrets, etc.
Data sharing requires business management participation
- Commitment to quality data is essential for obtaining the greatest
benefits from a data resource
- Data must also be made accessible to decrease data processing costs for
the enterprise
Corporate Information Policy:
provides the foundation for managing the ownership of data
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MANAGERIAL ISSUES
Example: Corporate Information Policy for Data Access
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MANAGERIAL ISSUES
• Transborder data flows:
electronic movements of data that cross a country’s national
boundary for processing, storage, or data retrieval
• Data are subject to laws of exporting country
• Laws to control flows are justified by perceived need to:
- Prevent economic and cultural imperialism
- Protect domestic industry
- Protect individual privacy
- Foster international trade
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MANAGERIAL ISSUES
DATA ADMINISTRATION UNIT
• IS unit accountable for data management in an
organization
Key Functions of the Data Administration Group
• Promote and control data sharing
• Analyze the impact of changes to application systems
when data definitions change
• Maintain metadata
• Reduce redundant data and processing
• Reduce system maintenance costs and improve systems
development productivity
• Improve quality and security of data
• Insure data integrity
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MANAGERIAL ISSUES
DATABASE ADMINISTRATOR (DBA)
• IS position with the responsibility for
managing an organization’s electronic
databases
Key Functions of the Database Administrator
• Tuning database management systems
• Selection and evaluation of and training on database
technology
• Physical database design
• Design of methods to recover from damage to databases
• Physical placement of databases on specific computers and
storage devices
• The interface of databases with telecommunications and
other technologies
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Thank you
Any
Question?
7/17/2015
70
Professor Lili Saghafi
7/17/2015
Professor Lili Saghafi