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- Chapter 7
- Information Systems Management In Practice 5E
- McNurlin & Sprague
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- Data vs. Information vs. Knowledge
- Data: facts
- Information: data in context
- Knowledge: information with direction or intent - it facilitates a
decision or action
- Companies’ greatest asset: the
knowledge “embedded” in employees’ heads (tacit knowledge)
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- Problem: data definitions incompatible from application to application,
from department to department, from site to site, and from division to
division
- Reason: to get application systems up and running quickly, system
designers sought data from the cheapest source or politically expedient
source
- Result: different files with different names for same data and same name
for different data
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- In order to get corporate data under control, use of DBMS - database
management software
- A “database administrator” to manage DBMS to improve the problem of
inconsistent and redundant data
- Broader definition of the data administration role - Standard format for
data crossing organization boundaries
- Effective use of data dictionaries to control standard data definitions
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- Clean up data definitions: getting rid of data redundancies and
inconsistencies, e.g., two names for same data item, design integrity
flags, and train users on meaning of data
- Control shared data - data used by two or more units - and analyze
impact of changes to programs that use shared data; data dictionaries
provides one place to look for all uses of data.
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- Manage distributed data - geographically dispersed - it also may cross
hierarchical levels of the organization
- Maintain data quality: involves the design and implementation of
procedures to maintain quality of data, usually owners of data edit and
verify the accuracy, defining who owns the data
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- Systems and procedures for storing and handling an organization’s data
definitions
- Purpose: eliminate errors of understanding, ambiguities, and
difficulties in interpreting data
- Ideal sequence:
- Set up the data administration function
- Develop data standards
- Purchase and install a DBMS
- Install the data dictionary as the first dB application
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- International, decentralized.
- Vision:
- responsive to customer needs
- thinking and acting from global perspective
- taking risks to enter new markets
- treating earth as a closed system -consumption and contamination cannot
be sustained
- creating a thriving environment for employees
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- Three enterprise-wide IT projects:
- Financial Transaction System: SAP - covers all core business
transactions, including finance, order processing, inventory
management, product planning, and manufacturing resource planning.
- Knowledge Management Architecture: Data warehouses that can be sliced
and diced with drill-down capability; compare and leverage information.
- Enterprise Reference Data: SAP repository of master table information
in the company. Includes vendors, customers, suppliers, etc. with
different views for purchasing, accounting, etc. Enables integration.
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- Getting the data into shape - ERD Stewardship Dept.
- Set data standards and enforce quality
- Entity specialists, key managers. with the greatest stake in the
quality of data
- Analysts who manage the data - a global resource that the entire
company uses
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- See Figure 7-4
- Level 1 - The external, conceptual, or local level, containing the
various “user views” of the corporate data that each application program
uses
- Level 2 - The logical or “enterprise data” level, encompassing all an
organization’s relevant data, under the control of the data
administration.
- Level 3 - The physical or storage level, specifying the way the data is
physically stored
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- Logical data can be separated from the method of physical storage,
different physical devices can be used without changing application
programs.
- Logical data relationships can vary from different programs that use the
data, without requiring data redundancy.
- Applications can use a subset of the DB and organize it, again without
redundancy, in the best manner for the application.
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- Hierarchical model: structures data so that each element is subordinate
to another in a strict hierarchical manner, like boxes in organization
chart
- Network model: allows each data item to have more than one parent,
relationships stated by pointers
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- Relational DBMS: where the relationships among data items are not
expressly stated by pointers.
Instead it is up to the DBMS to find the related items, based on
the values of specified data fields. Store data in tables, each row=tuple,
col.=attributive entity, DB of choice today.
- See Figure 7-5 for relational operations
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- Designed to widen the use of DB to new kinds of applications: CAD,
medical applications, knowledge representation for AI.
- Retains traditional DBMS features and add two new concepts.
- Object management: The mgt. of complex kinds of data, e.g., multimedia
and procedures
- Knowledge Management: the management of large numbers of complex rules
for reasoning
- Objects can be of any type: e.g., spreadsheet, video clip, photograph.
- A collection of objects is an object-base or object-oriented database.
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- NASA needs to store 1016 bytes of satellite images, enough to
fill 10K optical disk jukeboxes
- CAD data for a skyscraper
- DNA sequence of human genome, several billion elements long for genetic
make-up research
- Customer buying patters at large retail chains - data mining
- Multimedia DB of insurance policies
- Design databases should notify designers when another designer has made
a modification to the system
- Need new data models to handle spatial data, time, and uncertainty
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- A single worldwide file system (?)
- Distributed heterogeneous DB - for inter-company DB
- Security - authenticating enquirers
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- Distributed databases - 12 rules for a distributed database, see Figure
7-6. Operating principles depend
on underlying DB being relational.
- SQL - Standard Query Language - a standard language for accessing
relational DB.
- A data definition language for creating relational tables, indexes to
data, and fields of data
- A data manipulation language for entering information into a DB
- A data control language for handling security functions
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- Downloaded data files: e.g., sending data from mainframes to PCs, most
popular method for distributing data
- Copies of data stored at nodes: helps process customer activity during
the day, night official updating of the master files
- Not fully synchronized DB: second copies in cache, errors cause a new
primary copy
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- Server-based DB: In a distributed DB, each node has a copy of the DBMS
and dictionary. In server DB, applications must know where data is
located; does not support location transparency. Appropriate for higher-performance
transaction processing.
- Federated databases: Existing DB defined independently and retain rules
for others to access its data.
Works when incompatible DB (text, alphanumeric, and image) are
needed in a single application.
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- Value Issues: Information’s value depends on the recipient and the
context. The only practical way to establish the value of information is
to put a price on it, and see if anyone buys. Tools:
- Information maps: Point to the location of information, e.g., where to
get quick answers to questions
- Information guides: People who know where information can be found.
- Business documents: Provide organization and context - what documents
an organization needs
- Groupware: People to share information across distances
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- Usage Issues: Deals with how people use information
- Information’s complexity needs to be preserved - information should not
be simplified to be made to fit into a computer, because this truncates
sharing and conversations.
- People do not easily share information, even though its value grows as
it is shared. Culture often blocks sharing.
- Technology does not change culture.
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- Sharing Issues:
- A sharing culture must be in place or the existing disincentives will
thwart the use of sharing systems.
- Information architectures have failed because they do not take into
account how people use the information.
- Sharing of corporate performance figures is beneficial, but sharing of
rumors can be de-moralizing.
- Separating information from non-information is an information
management issue.
- Getting value out of information requires more than a technology.
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- Fig. 7.2 presents a matrix representative of these four types of
information.
- Internal/record-based has been the focus of attention of information
systems, the type of information computer-based applications generate
and manage easily.
- External/record-based: can be accessed over the Internet via public
DBs.
- Internal/document-based: intranet document management 95% of the
information. In most organizations it is in document form, 5% data.
- External/document-based: WWW
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- Figure 7-8 lists the four categories of information and shows typical
corporate authority, sources of information, and examples of
technologies used in managing each:
- Internal record-based
- Internal document-based
- External record-based
- External document-based
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- The more people are connected, and the more they exchange ideas, the
more their knowledge spreads and can thus be leveraged.
- Process is the key - how do we transfer tacit knowledge? IT is an
enabler.
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- The job of the IS department is widening:
- get data into shape
- create and build an infrastructure for managing the full range of
information types
- helping the firm leverage the tacit knowledge of its employees
- Companies that address all three areas, and start implementing IT-based
programs in all three, will have a significant edge over their
competitors because they will be able to leverage their intellectual
assets.
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