Transcript Data Text, and Document Management

Lecture 2
Ch.3
Data, Text, and Document
Management
3.1 Data, Text, and Document Man
agement
Data, text, and documents are strategic assets.
Vast quantities are:
– created and collected
– then stored – often in 5 or more locations
Data, text, and document management helps
companies improve productivity by insuring that
people can find what they need without having
to conduct a long and difficult search.
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Data Management
Why does data management matter?
– No enterprise can be effective without high quality data
that is accessible when needed.
– Data that’s incomplete or out of context cannot be trust
ed.
– Organizations with at least 1,000 knowledge workers los
e ~ $5.7 million annually in time wasted by employees r
eformatting data as they move among applications.
What is the goal of data management?
– To provide the infrastructure and tools to transform raw
data into usable information of the highest quality.
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Data Management
Why is data management difficult and expensive?
– Volume of data is increasing exponentially.
– Data is scattered throughout the organization.
– Data is created and used offline without going thro
ugh quality control checks.
– Data may be redundant and out-of-date, creating a
huge maintenance problem.
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Information System – Primary Purpose
Collects data, processes it into information then converts
information into knowledge for a specific purpose.
• Data
– Elementary description of things, events, activities, and tran
sactions that are recorded, classified, and stored, but not o
rganized to convey any specific meeting
• Information
– Data that has been organized so that they have meaning a
nd value to the recipient
• Knowledge
– Information that has been organized and processed to con
vey understanding, experience and expertise as they apply
to a current problem or activity
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IT at Work 3.1 – Healthcare Sector
Data Errors Cost Billions of Dollars and Put Lives a
t Risk
• Every day, healthcare administrators and others throug
hout the healthcare supply chain waste 24% --30% of t
heir time correcting data errors.
• Each incorrect transaction costs $60 to $80 to correct.
• About 60% of all invoices among supply chain partner
s have errors, and each invoice error costs $40 to $400
to reconcile.
• Each year, billions of dollars are wasted in the healthca
re supply chain because of supply chain data disconne
cts.
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IT at Work 3.1
(continued)
Data Errors Cost Billions of Dollars and Put Lives a
t Risk
Benefits from data synchronization in the healthcare se
ctor and supply chain:
– Easier and faster product sourcing because of accu
rate and consistent item information
– Significantly reduces the amount of fraud or unauth
orized purchasing
– Reduces unnecessary inventories
– Lowers prices because purchase volumes became a
pparent
– Improves patient safety
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Data management is a structured approach for
capturing, storing, processing, integrating, distributing,
securing, and archiving data effectively throughout
their life cycle.
Figure 3.2 Data life cycle
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Data from various sources are extracted, transformed, & loaded (ETL) in
to a data warehouse; then used to support functions and apps through
out the enterprise.
Figure 3.4. Model of an Enterprise Data
Warehouse
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3.2 File Management Systems
Computer systems organize data into a hierarchy:
bits, bytes, fields, records, files, and databases
Figure 3.6 Hierarchy of data for a computer-based file.
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Limitations of the File Environmen
t
• When organizations began using computers, they started
with one application at a time, usually accounting, billing
, and payroll. Each app was designed to be a stand-alon
e system, which led to data problems.
• Data problems with a file environment:
– data redundancy
– data inconsistency
– data isolation
– data security
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• Stand-alone systems result in data redundanc
y, inconsistency, and isolation.
•Database management systems helped solve t
he data problems of file-based systems.
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3.3 Database Management System
s (DMBS)
• Numerous data sources
– clickstream data from Web and e-commerce applica
tions
– detailed data from POS terminals
– filtered data from CRM, supply chain, and enterprise
resource planning applications
• DBMS permits an organization to centralize data, man
age them efficiently, and give application programs ac
cess to the stored data.
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2 types of databases:
a) Centralized database
b) Distributed database
with complete or
partial copies of the
central database in
more than one location
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Functions of a Database Management System (D
BMS)
• Data filtering and profiling: Inspecting the data for er
rors, inconsistencies, redundancies, and incomplete inf
ormation.
• Data quality: Correcting, standardizing, and verifying
the integrity of the data.
• Data synchronization: Integrating, matching, or linkin
g data from disparate sources.
• Data enrichment: Enhancing data using information fr
om internal and external data sources.
• Data maintenance: Checking and controlling data inte
grity over time.
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•
3.4 Data Warehouses, Data Marts,
and
Data
Centers
Data warehouse: a repository in which data are organized
so that they can be readily analyzed using methods such as
data mining, decision support, querying, and other applicati
ons.
– enable managers and knowledge workers to leverage enterprise d
ata to make the smartest decisions
– enable OLAP (online analytic processing)
• Data marts: designed for a strategic business unit (SBU) or
a single department.
• Data centers: facilities containing mission-critical ISs
and components that deliver data and IT services to the ent
erprise.
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