Chapter 03 it-8ed-volonino

Part II. Data and Network Infrastructure

C hapter 3
Data, Text, and
Document Management

M a n a g e m e n t I n fo r m a t i o n S y s te m s
EIMBA

Copyright 2012 John Wiley & Sons, Inc.
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Chapter 3 Outline

3.1 Data, Text, and Document Management

3.2 File Management Systems

3.3 Database Management Systems

3.4 Data Warehouses, Data Marts, and Data Centers

3.5 Enterprise Content Management

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Chapter 3 Learning Objectives
 Describe data, text, and document
management, and their impacts on performance.
 Understand file management systems.
 Understand the functions of databases and
database management systems.
 Describe the tactical and strategic benefits of data
warehouses, data marts, and data centers.

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For Class Discussion & Debate
Wendy's International Relies on Text Mining for
Customer Experience Management

Scenario for Brainstorming & Discussion (see book for full text)
1. Select an industry, company, or public sector.
2. Identify costs due to ignorance about customers’ or
constituents’ experiences.
3. Explain how your selection could benefit from text
analytics that provided feedback within 24 hours.
4. Compare and assess your answers with others in class.

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Debate (see book for full text)

• Select one side of the argument, as described in
the textbook.
• Debate whether investments in text message
collection and mining should be made even if no
ROI can be determined in advance.
• Provide convincing arguments either in favor of
or against the investment in text message
collection and mining.

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3.1 Data, Text, and Document Management

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 trusted.
• Organizations with at least 1,000 knowledge workers lose
~ $5.7 million annually in time wasted by employees
reformatting 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 through
quality control checks.

• Data may be redundant and out-of-date, creating a huge
maintenance problem.

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Data Management
Current key issues

 Master data management (MDM): Processes to integrate
data from various sources and enterprise apps in order to
create a unified view of the data.

 Document management system (DMS): Hardware and
software to manage, archive, and purge files and other
electronic documents (e-documents).

 Green computing: Efforts to conserve natural resources
and reduce effects of computer usage on the
environment.

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IT at Work 3.1 – Healthcare Sector
Data Errors Cost Billions of Dollars and Put Lives at Risk

 Every day, healthcare administrators and others throughout
the healthcare supply chain waste 24% --30% of their time
correcting data errors.

 Each incorrect transaction costs $60 to $80 to correct.

 About 60% of all invoices among supply chain partners have
errors, and each invoice error costs $40 to $400 to reconcile.

 Each year, billions of dollars are wasted in the healthcare
supply chain because of supply chain data disconnects.

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IT at Work 3.1 (continued)
Data Errors Cost Billions of Dollars and Put Lives at Risk
Benefits from data synchronization in the healthcare sector
and supply chair:
• Easier and faster product sourcing because of accurate
and consistent item information
• Significantly reduces the amount of fraud or unauthorized
purchasing
• Reduces unnecessary inventories
• Lowers prices because purchase volumes became
apparent
• Improves patient safety

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Data management is a structured approach for
capturing, storing, processing, integrating, distributing, securi
ng, and archiving data effectively throughout their life cycle.

Figure 3.2 Data life cycle

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Data problems

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Data principles

 Principle of diminishing data value. The more
resent the information, the more valuable it is
 Principle of 90/90 data use: 90% of data is
seldom accessed after 90 days.
 Principle of data in context. Investment in DM
infrastructure may be huge.

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IT at Work 3.4

 Check page 69

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Transforming data into knowledge

 Text mining and analytics:
• Exploration
• Preprocessing
• Categorizing and modeling

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Data from various sources are extracted, transformed, & loaded (ETL) into a data
warehouse; then used to support functions and apps throughout 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 Environment
 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-alone 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
redundancy, inconsistency, and isolation.
•Database management systems helped solve the
data problems of file-based systems.
Copyright 2012 John Wiley & Sons, Inc. 3-20

Figure 3.10 Database management system provides access to all data in
the database.

Copyright 2012 John Wiley & Sons, Inc. 3-21

3.3 Database Management Systems (DMBS)

 Numerous data sources
• clickstream data from Web and e-commerce applications
• detailed data from POS terminals
• filtered data from CRM, supply chain, and enterprise
resource planning applications
 DBMS permits an organization to centralize data, manage
them efficiently, and give application programs access 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 (DBMS)

 Data filtering and profiling: Inspecting the data for
errors, inconsistencies, redundancies, and incomplete
information.
 Data quality: Correcting, standardizing, and verifying the
integrity of the data.
 Data synchronization: Integrating, matching, or linking data
from disparate sources.
 Data enrichment: Enhancing data using information from
internal and external data sources.
 Data maintenance: Checking and controlling data integrity
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 applications.
• enable managers and knowledge workers to leverage enterprise data 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 enterprise.

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Figure 3.11 Data warehouse framework and views.

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Characteristics of a data warehouse

 Organization. Data are organized by subject (e.g., by
customer, vendor, product, price level, and region), and contain
information relevant for decision support only.
 Consistency. Data in different operational databases may be encoded
differently. For example, gender data may be encoded 0 and 1 in one
operational system and “m” and “f” in another. In the warehouse they will
be coded in a consistent manner.
 Time variant. The data are kept for many years so they can be used for
trends, forecasting, and comparisons over time.
 Nonvolatile. Once entered into the warehouse, data are not updated.
 Relational. Typically the data warehouse uses a relational structure.
 Client/server. The data warehouse uses the client/server architecture
mainly to provide the end user an easy access to its data.
 Web-based. Today’s data warehouses are designed to provide an efficient
computing environment for Web-based applications (Rundensteiner et.
al., 2000).

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Building an Enterprise Data Warehouse (EDW)

A company that is considering building a DW first needs to
address a series of basic questions to avoid a failure:
• Does top management support the DW?
• Do users want access to a broad range of data
• Do users want data access and analysis tools?
• Do users understand how to use the DW to solve business
problems?
• Does the unit have one or more power users who can
understand DW technologies?

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Figure 3.12 Teradata Corp.’s EDW
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Suitability

Data warehousing is most appropriate for organizations
that have some of the following characteristics:
 End users need to access large amounts of data
 Operational data are stored in different systems
 The organization serves a large, diverse customer base
 The same data are represented differently in different systems
 Extensive end-user computing is performed

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3.5 Enterprise Content Management

ECM includes:
 electronic document management
 Web content management
 digital asset management, and
 electronic records management (ERM)

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Figure 3.13 Electronic records management from creation
to retention or destruction
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Unstructured business records
 Businesses generate volumes of documents, messages, and
memos that, by their nature, contain unstructured content
that cannot be put into a database.
 Many of these materials are business records that must be
retained and made available when requested by
auditors, investigators, the SEC, the IRS, or other authorities.
 To be retrievable, business records must be organized and
indexed.
 Records are not needed for current operations or
decisions, are archived—moved into longer-term storage.

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Business Value of E-Records Management
 Companies need to be prepared to respond to an
audit, federal investigation, lawsuit, or other legal action
against it.
• Examples of lawsuits: patent violations, fraud, product safety
negligence, theft of intellectual property, breach of contract, wrongful
termination, harassment, and discrimination

 E-discovery is the process of gathering electronically stored
information in preparation for trial, legal or regulatory
investigation, or administrative action as required by law.
• When a company receives an e-discovery request, the company must
produce what is requested—or face charges of obstructing justice or
being in contempt of court.
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Companies have incurred huge costs for not
responding to e-discovery

 Failure to save e-mails resulted in a $2.75 million fine for
Phillip Morris.
 Failure to respond to e-discovery requests cost Bank of
America $10 million in fines.
 Failure to produce backup tapes and deleted e-mails resulted
in a $29.3 million jury verdict against UBS Warburg in the
landmark case, Zubulake v. UBS Warburg.

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Exercise

 Visit Analysis Factory at analysisfactory.com
Click view the interactive business solution
dashboards.
 Select one type of dashboard and explain its
value or features.

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Chapter 3 Link Library

 Advizor Solutions, data analytics and visualization
http://advizorsolutions.com/

 Clarabridge: How Text Mining Works http://clarabridge.com/

 SAS Text Miner http://sas.com/

 Tableau data visualization software http://tableausoftware.com/data-
visualization-software/

 EMC Corp., enterprise content management http://emc.com

 Oracle DBMS http://oracle.com/

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Chapter 03 it-8ed-volonino

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