Chapter 3Data Management,Big Data Analytics, andRecords Ma.docx

Chapter 3
Data Management,
Big Data Analytics, and
Records Management
Prepared by Dr. Derek Sedlack, South University
Copyright © 2015 John Wiley & Sons, Inc. All rights reserved.
Learning Objectives
Data Warehouse and Big Data Analytics
Data and Text Mining
Business Intelligence
Electronic Records Management
Database Management Systems

Databases
Collections of data sets or records stored in a systematic way.
Stores data generated by business apps, sensors, operations, and
transaction-processing systems (TPS).
The data in databases are extremely volatile.
Medium and large enterprises typically have many databases of
various types.
Volatile data changes frequently
Chapter 3
Data Warehouses
Integrate data from multiple databases and data silos, and
organize them for complex analysis, knowledge discovery, and
to support decision making.
May require formatting processing and/or standardization.
Loaded at specific times making them non-volatile and ready
for analysis.
Chapter 3

Data Marts
Small-scale data warehouses that support a single function or
one department.
Enterprises that cannot afford to invest in data warehousing may
start with one or more data marts.
Chapter 3
Business intelligence (BI)
Tools and techniques that process data and conduct statistical
analysis for insight and discovery.
Used to discover meaningful relationships in the data, keep
informed of real time, detect trends, and identify opportunities
and risks.
Chapter 3
Database Management System (DBMS)
Integrate with data collection systems such as TPS and business
applications.
Stores data in an organized way.
Provides facilities for accessing and managing data.
Standard database model adopted by most enterprises.
Store data in tables consisting of columns and rows, similar to
the format of a spreadsheet.
Chapter 3

Relational Management System (DBMS)
Provides access to data using a declarative language.
Declarative Language
Simplifies data access by requiring that users only specify what
data they want to access without defining how they will be
achieved.
Structured Query Language (SQL) is an example of a
declarative language:
SELECT column_name(s)
FROM table_name
WHERE condition
Chapter 3
DBMS Functions
Data filtering and profiling
Data integrity and maintenance
Data synchronization
Data security
Data access
Chapter 3
Online Transaction Processing and Online Analytics Processing
Online Transaction Processing (OLTP)
Designed to manage transaction data, which are volatile & break
down complex information into simpler data tables to strike a
balance between transaction-processing efficiency and query

efficiency.
Cannot be optimized for data mining
Online Analytics Processing (OLAP)
A means of organizing large business databases.
Divided into one or more cubes that fit the way business is
conducted.
Chapter 3
DBMSs (mid-2014)
Oracle’s MySQL
Microsoft’s SQL Server
PostgreSQL
IBM’s DB2
Teradata Database.
Chapter 3
Trend Toward NoSQL Systems
Higher performance
Easy distribution of data on different nodes
enables scalability and fault tolerance
Greater flexibility
Simpler administration
Chapter 3

Centralized and Distributed Database Architecture
Centralized Database Architecture
Better control of data quality.
Better IT security.
Distributed Database Architecture
Allow both local and remote access.
Use client/server architecture to process requests.
Chapter 3
Garbage In, Garbage Out
Dirty Data
Lacks integrity/validation and reduces user trust.
Incomplete, out of context, outdated, inaccurate, inaccessible,
or overwhelming.
Chapter 3
Cost of Poor
Quality Data
Lost Business
Cost to Prevent Errors
Cost to Correct Errors
Principle of Diminishing Data Value
The value of data diminishes as they age.

Blind spots (lack of data availability) of 30 days or longer
inhibit peak performance.
Global financial services institutions rely on near-real-time data
for peak performance.
Principle of 90/90 Data Use
As high as 90 percent, is seldom accessed after 90 days (except
for auditing purposes).
Roughly 90 percent of data lose most of their value after 3
months.
Chapter 3
Principle of data in context
The capability to capture, process, format, and distribute data in
near real time or faster requires a huge investment in data
architecture.
The investment can be justified on the principle that data must
be integrated, processed, analyzed, and formatted into
“actionable information.”
Chapter 3
Data Life Cycle
Chapter 3
Figure 3.11 Data life cycle.

Chapter 3
Figure 3.12 An enterprise has transactional, master, and
analytical data.
Describe a database and a database management system
(DBMS).
Explain what an online transaction-processing (OLAP) system
does.
Why are data in databases volatile?
Explain what processes DBMSs are optimized to perform.
What are the business costs or risks of poor data quality?
Describe the data life cycle.
What is the function of master data management (MDM)?
Chapter 3
Suggested Answers:
1. A database is a collection of data sets or records stored in a
systematic way. A database stores data generated by business
apps, sensors, and transaction processing systems. Databases
can provide access to all of the organization’s data collected for
a particular function or enterprise-wide, alleviating many of the
problems associated with data file environments. Central
storage of data in a database reduces data redundancy, data

isolation, and data inconsistency and allows for data to be
shared among users of the data. In addition, security and data
integrity are easier to control, and applications are independent
of the data they process. There are two basic types of databases:
centralized and distributed.
A database management system (DBMS) is software used to
manage the additions, updates, and deletions of data as
transactions occur; and support data queries and reporting.
DBMSs integrate with data collection systems such as TPS and
business applications; store the data in an organized way; and
provide facilities for accessing and managing that data.
2. OLTP is a database design that breaks down complex
information into simple data tables in order to be efficient for
capturing transactional data, including additions, updates, or
deletions. OLTP databases are capable of processing millions of
transactions every second.
3. Data in databases are volatile because they can be updated
millions of times every second, especially if they are
transaction processing systems (TPS).
4. Data filtering and profiling: Inspecting the data for errors,
inconsistencies, redundancies, and incomplete information.
Data integrity and maintenance: Correcting, standardizing, and
verifying the consistency and integrity of the data.
Data synchronization: Integrating, matching, or linking data
from disparate sources.
Data security: Checking and controlling data integrity over
time.
Data access: Providing authorized access to data in both
planned and ad hoc ways within acceptable time.
5. Poor quality data cannot be trusted and may result in the
inability to make intelligent business decisions. Poor data may

lead to lost business opportunities, increased time, and effort
trying to prevent errors, increased time, and effort trying to
correct errors, misallocation of resources, flawed strategies,
incorrect orders, and customers becoming frustrated and driven
away.
The cost of poor quality data spreads throughout the company
affecting systems from shipping and receiving to accounting and
customer services. Errors can be difficult, time-consuming, and
expensive to correct, and the impacts of errors can be
unpredictable or serious.
6. Three general data principles relate to the data life cycle
perspective and help to guide IT investment decisions.
Principle of diminishing data value. Viewing data in terms of a
life cycle focuses attention on how the value of data diminishes
as the data age. The more recent the data, the more valuable
they are. This is a simple, yet powerful, principle. Most
organizations cannot operate at peak performance with blind
spots (lack of data availability) of 30 days or longer.
Principle of 90/90 data use. Being able to act on real-time or
near real-time operational data can have significant advantages.
According to the 90/90 data-use principle, a majority of stored
data, as high as 90 percent, is seldom accessed after 90 days
(except for auditing purposes). Put another way, roughly 90
percent of data lose most of their value after three months.
Principle of data in context. The capability to capture, process,
format, and distribute data in near real-time or faster requires a
huge investment in data management architecture and
infrastructure to link remote POS systems to data storage, data
analysis systems, and reporting applications. The investment
can be justified on the principle that data must be integrated,
processed, analyzed, and formatted into “actionable
information.” End users need to see data in a meaningful format
and context if the data are to guide their decisions and plans.
7. Master data management (MDM) is a process whereby

companies integrate data from various sources or enterprise
applications to provide a more complete or unified view of an
entity (customer, product, etc.) Although vendors may claim
that their MDM solution creates “a single version of the truth,”
this claim probably is not true. In reality, MDM cannot create a
single unified version of the data because constructing a
completely unified view of all master data simply is not
possible. Realistically, MDM consolidates data from various
data sources into a master reference file, which then feeds data
back to the applications, thereby creating accurate and
consistent data across the enterprise.
19
Learning Objectives

Market share
Percentage of total sales in a market captured by a brand,
product, or company.
Operating Margin
A measure of the percent of a company’s revenue left over after
paying variable costs: wages, raw materials, etc.
Increased margins mean earning more per dollar of sales.
The higher the operating margin, the better.
Chapter 3
TORTURE DATA LONG ENOUGH AND IT WILL CONFESS .
. .
BUT MAY NOT TELL THE TRUTH

Chapter 3
Human Expertise and Judgment Required
Data are worthless if you cannot analyze, interpret, understand,
and apply the results in context.
Data need to be prepared for analysis.
Dirty data degrade the value of analytics.
Data must be put into meaningful context.
Chapter 3
Enterprise data warehouses (EDW)
Data warehouses that pull together data from disparate sources
and databases across an entire.
Warehouses are the primary source of cleansed data for
analysis, reporting, and Business Intelligence (BI).
Their high costs can be subsidized by using Data marts.
Chapter 3
Procedures to Prepare EDW Data for Analytics
Extract from designated databases.
Transform by standardizing formats, cleaning the data,
integration.
Loading into a data warehouse.

Chapter 3
Chapter 3
Active Data Warehouse (ADW)
Real-time data warehousing and analytics.
Transform by standardizing formats, cleaning the data,
integration.
They Provide
Interaction with a customer to provide superior customer
service.
Respond to business events in near real time.
Share up-to-date status data among merchants, vendors,
customers, and associates.
Chapter 3
Supporting Actions as well as Decisions
Marketing and Sales
Pricing and Contracts
Forecasting
Sales
Financial
Chapter 3

Really Big Data
Low-cost sensors collect data in real time in all types of
physical things (machine-generated sensor data):
Regulate temperature and climate
Detect air particles for contamination
Machinery conditions/failures
Engine wear/maintenance
Chapter 3
Chapter 3
Figure 3.16 Machine generated data from physical objects are
becoming a much larger portion of big data and analytics..
Hadoop and MapReduce
Hadoop is an Apache processing platform that places no
conditions on the processed data structure.
MapReduce provides a reliable, fault-tolerant software
framework to write applications easily that process vast
amounts of data (multi-terabyte data-sets) in-parallel on large
clusters (thousands of nodes) of commodity hardware.
Map stage: breaks up huge data into subsets

Reduce stage: recombines partial results
Chapter 3
Why are human expertise and judgment important to data
analytics? Give an example.
What is the relationship between data quality and the value of
analytics?
Why do data need to be put into a meaningful context?
What are the differences between databases and data
warehouses?
Explain ETL and CDC.
What is an advantage of an active data warehouse (ADW)?
Why might a company invest in a data mart?
How can manufacturers and health care benefit from data
analytics?
Explain how Hadoop implements MapReduce in two stages.
Chapter 3
Suggested Answers
1. Human expertise and judgment are needed to interpret the
output of analytics (refer to Figure 3.1). Data are worthless if
you cannot analyze, interpret, understand, and apply the results
in context.
For example, some believe that Super Bowl results in February
predict whether the stock market will go up or down that year.
If the National Football Conference (NFC) wins, the market

goes up; otherwise, stocks take a dive. Looking at results over
the past 30 years, most often the NFC has won the Super Bowl
and the market has gone up. Does this mean anything? No.
2. Dirty data degrade the value of analytics. The “cleanliness”
of data is very important to data mining and analysis projects.
3. Managers need context in order to understand how to
interpret traditional and big data. If the wrong analysis or
datasets are used, the output would be nonsense, as in the
example of the Super Bowl winners and stock market
performance.
4. Databases are:
Designed and optimized to ensure that every transaction gets
recorded and stored immediately.
Volatile because data are constantly being updated, added, or
edited.
OLTP systems.
Medium and large enterprises typically have many databases of
various types.
Data warehouses are:
Designed and optimized for analysis and quick response to
queries.
Nonvolatile. This stability is important to being able to analyze
the data and make comparisons. When data are stored, they
might never be changed or deleted in order to do trend analysis
or make comparisons with newer data.
OLAP systems.
Subject-oriented, which means that the data captured are
organized to have similar data linked together.
Data warehouses integrate data collected over long time periods
from various source systems, including multiple databases and
data silos.
5. ETL refers to three procedures – Extract, Transform, and

Load – used in moving data from databases to a data warehouse.
Data are extracted from designated databases, transformed by
standardizing formats, cleaning the data, integrating them, and
loaded into a data warehouse.
CDC, the acronym for Change Data Capture, refers to processes
which capture the changes made at data sources and then apply
those changes throughout enterprise data stores to keep data
synchronized. CDC minimizes the resources required for ETL
processes by only dealing with data changes.
6. An ADW provides real-time data warehousing and analytics,
not for executive strategic decision making, but rather to
support operations. Some advantages for a company of using an
ADW might be interacting with a customer to provide superior
customer service, responding to business events in near real
time, or sharing up-to-date status data among merchants,
vendors, customers, and associates.
7. The high cost of data warehouses can make them too
expensive for a company to implement. Data marts are lower-
cost, scaled-down versions that can be implemented in a much
shorter time, for example, in less than 90 days. Data marts serve
a specific department or function, such as finance, marketing, or
operations. Since they store smaller amounts of data, they are
faster, and easier to use and navigate.
8. Machine-generated sensor data are becoming a larger
proportion of big data (Figure 3.16). Analyzing them can lead to
optimizing cost savings and productivity gains. Manufacturers
can track the condition of operating machinery and predict the
probability of failure, as well as track wear and determine when
preventive maintenance is needed.
Federal health reform efforts have pushed health-care
organizations toward big data and analytics. These
organizations are planning to use big data analytics to support
revenue cycle management, resource utilization, fraud

prevention, health management, and quality improvement, in
addition to reducing operational expenses.
9. Apache Hadoop is a widely used processing platform which
places no conditions on the structure of the data it can process.
Hadoop implements MapReduce in two stages:
Map stage: MapReduce breaks up the huge dataset into smaller
subsets; then distributes the subsets among multiple servers
where they are partially processed.
Reduce stage: The partial results from the map stage are then
recombined and made available for analytic tools
32
Learning Objectives

Creating Business Value
Business Analytics: the entire function of applying
technologies, algorithms, human expertise, and judgment.
Data Mining: software that enables users to analyze data from
various dimensions or angles, categorize them, and find
correlative patterns among fields in the data warehouse.
Text Mining: broad category involving interpreted words and
concepts in context.
Sentimental Analysis: trying to understand consumer intent.
Chapter 3
Text Analytics (Mining) Procedure
Exploration
Simple word counts
Topics consolidation
Preprocessing
Standardization

May be 80% of processing time
Grammar and spell checking
Categorizing and Modelling
Create business rules and train models for accuracy and
precision
Chapter 3
Text Analytics Procedure
Exploration
Simple word counts
Topics consolidation
Preprocessing
Standardization
May be 80% of processing time
Grammar and spell checking
Categorizing and Modelling
Create business rules and train models for accuracy and
precision
Chapter 3
Describe data mining.
How does data mining generate or provide value? Give an

example.
What is text mining?
Explain the text mining procedure.
Chapter 3
Suggested Answers:
1. Data mining is the process of analyzing data from various
dimensions or angles, categorizing them, and finding
correlations or patterns among fields in the data warehouse.
2. Data mining is used to discover knowledge that you did not
know existed in the databases.
Answers may vary. A data mining example: The mega-retailer
Walmart wanted its online shoppers to find what they were
looking for faster. Walmart analyzed clickstream data from its
45 million monthly online shoppers then combined that data
with product and category related popularity scores which were
generated by text mining the retailer’s social media streams.
Lessons learned from the analysis were integrated into the
Polaris search engine used by customers on the company’s
website. Polaris has yielded a 10 to 15 percent increase in
online shoppers completing a purchase, which equals roughly $1
billion in incremental online sales.
3. Up to 75 percent of an organization’s data are non-structured
word processing documents, social media, text messages, audio,
video, images and diagrams, fax and memos, call center or
claims notes, and so on. Text mining is a broad category that
involves interpreting words and concepts in context. Then the
text is organized, explored, and analyzed to provide actionable
insights for managers. With text analytics, information is
extracted out of large quantities of various types of textual

information. It can be combined with structured data within an
automated process. Innovative companies know they could be
more successful in meeting their customers’ needs if they just
understood them better. Text analytics is proving to be an
invaluable tool in doing this.
4. The basic steps involved in text mining/analytics include:
Exploration. First, documents are explored. This might be in the
form of simple word counts in a document collection, or
manually creating topic areas to categorize documents by
reading a sample of them. For example, what are the major
types of issues (brake or engine failure) that have been
identified in recent automobile warranty claims? A challenge of
the exploration effort is misspelled or abbreviated words,
acronyms, or slang.
Preprocessing. Before analysis or the automated categorization
of the content, the text may need to be preprocessed to
standardize it to the extent possible. As in traditional analysis,
up to 80 percent of the time can be spent preparing and
standardizing the data. Misspelled words, abbreviations, and
slang may need to be transformed into a consistent term. For
instance, BTW would be standardized to “by the way” and “left
voice message” could be tagged as “lvm.”
Categorizing and Modeling. Content is then ready to be
categorized. Categorizing messages or documents from
information contained within them can be achieved using
statistical models and business rules. As with traditional model
development, sample documents are examined to train the
models. Additional documents are then processed to validate the
accuracy and precision of the model, and finally new documents
are evaluated using the final model (scored). Models then can
be put into production for automated processing of new
documents as they arrive.
37

Learning Objectives
Key to competitive advantage
Across industries in all size enterprises
Used in operational management, business process, and decision

making
Provides moment of value to decision makers
Unites data, technology, analytics, & human knowledge to
optimize decisions
Chapter 3
Challenges
Data Selection & Quality
Alignment with Business Strategy and BI Strategy
Alignment
Clearly articulates business strategy
Deconstructs business strategy into targets
Identifies PKIs
Prioritizes PKIs
Creates a plan based on priorities
Transform based on strategic results and changes
Chapter 3
Chapter 3
Smart Devices
Everywhere
have created demand for
effortless 24/7 access to
insights.
Data is Big
Business
when they provide insight that supports decisions and action.
Advanced BI and Analytics

help to ask questions that were previously unknown and
unanswerable.
Cloud Enabled BI and Analytics
are providing low-cost and flexible solutions.
Figure 3.20 Four factors contributing to increased use of BI.
BI Architecture and Analytics
Advances in response to big data and end-user performance
demands.
Hosted on public or private clouds.
Limits IT staff and controls costs
May slow response time, add security and backup risks
Chapter 3
How has BI improved performance management at Quicken
Loans?
What are the business benefits of BI?
What are two data-related challenges that must be resolved for
BI to produce meaningful insight?
What are the steps in a BI governance program?
What is a business-driven development approach?
What does it mean to drill down, and why is it important?
What four factors are contributing to increased use of BI?
How did BI help CarMax achieve record-setting revenue
growth?
Chapter 3

Suggested Answers:
1. Using BI, the company has increased the speed from loan
application to close, which allows it to meet client needs as
thoroughly and quickly as possible. Over almost a decade,
performance management has evolved from a manual process of
report generation to BI-driven dashboards and user-defined
alerts that allow business leaders to proactively deal with
obstacles and identify opportunities for growth and
improvement.
2. BI provides data at the moment of value to a decision
maker—enabling it to extract crucial facts from enterprise data
in real time or near real time. BI solutions help an organization
to know what questions to ask and to find answers to those
questions. BI tools integrate and consolidate data from various
internal and external sources and then process them into
information to make smart decisions. According to The Data
Warehousing Institute (TDWI), BI “unites data, technology,
analytics, and human knowledge to optimize business decisions
and ultimately drive an enterprise’s success. BI programs…
transform data into usable, actionable business information”
(TDWI, 2012).
Managers use business analytics to make better-informed
decisions and hopefully provide them with a competitive
advantage. BI is used to analyze past performance and identify
opportunities to improve future performance.
3. Data selection and data quality.
Information overload is a major problem for executives and for
employees. Another common challenge is data quality,
particularly with regard to online information, because the

source and accuracy might not be verifiable.
4. The mission of a BI governance program is to achieve the
following:
Clearly articulate business strategies.
Deconstruct the business strategies into a set of specific goals
and objectives—the targets.
Identify the key performance indicators (KPIs) that will be used
to measure progress toward each target.
Prioritize the list of KPIs.
Create a plan to achieve goals and objectives based on the
priorities.
Estimate the costs needed to implement the BI plan.
Assess and update the priorities based on business results and
changes in business strategy.
5. A business-driven development approach starts with a
business strategy and work backward to identify data sources
and the data that need to be acquired and analyzed.
6. Drilling down into the data is going from highly consolidated
or summarized figures into the detail numbers from which they
were derived. Sometimes a summarized view of the data is all
that is needed; however, drilling down into the data, from which
the summary came, provides the ability to do more in-depth
analyses.
7. Smart Devices Everywhere creating demand for effortless
24/7 access to insights.
Data is Big Business when they provide insight that supports
decisions and action.
Advanced Bl and Analytics help to ask questions that were
previously unknown and unanswerable.
Cloud Enabled Bl and Analytics are providing low-cost and
flexible solutions.

8. The ISs that helped CarMax include:
A proprietary IS that captures, analyzes, interprets, and
distributes data about the cars CarMax sells and buys.
Data analytics applications that track every purchase; number of
test drives and credit applications per car; color preference in
every demographic and region.
Proprietary store technology that provides management with
real-time data about every aspect of store operations, such as
inventory management, pricing, vehicle transfers, wholesale
auctions, and sales consultant productivity.
An advanced inventory management system helps management
anticipate future inventory needs and manage pricing.
43
Learning Objectives

Business Records
Documentation of a business event, action, decision, or
transaction.
Electronic Records Management (EMR)
Workflow software, authoring tools, scanners, and databases
that manage and archive electronic documents and image paper
documents.
Index and store documents according to company policy or legal
compliance.
Success depends on partnership of key players.
Chapter 3
Best Practices
Effective systems capture all business data.
Input from online forms, bar codes, sensors, websites, social
sites, copiers, e-mails, and more.
Industry Standards

Association for Information and Image Management (AIIM;
www.aiim.org)
National Archives and Records Administration (NARA;
www.archives.gov)
ARMA International (formerly the Association of Records
Managers and Administrators; www.arma.org)
Chapter 3
Primary Benefits
Access and use the content contained in documents.
Cut labor costs by automating business processes.
Reduce time and effort to locate required information for
decision making.
Improve content security, thereby reducing intellectual property
theft risks.
Minimizes content printing, storing, and searching costs.
Chapter 3
DISASTER RECOVERY, BUSINESS CONTINUITY, AND
COMPLIANCE
Does the software meet the organization’s needs? For example,
can the DMS be installed on the existing network? Can it be
purchased as a service?
Is the software easy to use and accessible from Web browsers,
office applications, and e-mail applications? If not, people will
not use it.
Does the software have lightweight, modern Web and graphical
user interfaces that effectively support remote users?

Before selecting a vendor, it is important to examine workflows
and how data, documents, and communications flow throughout
the company.
Chapter 3
What are business records?
Why is ERM a strategic issue rather than simply an IT issue?
Why might a company have a legal duty to retain records? Give
an example.
Why is creating backups an insufficient way to manage an
organization’s documents?
What are the benefits of ERM?
Chapter 3
Suggested Answers:
1. All organizations create and retain business records. A record
is documentation of a business event, action, decision, or
transaction. Examples are contracts, research and development,
accounting source documents, memos, customer/client
communications, hiring and promotion decisions, meeting
minutes, social posts, texts, e-mails, website content, database
records, and paper and electronic files. Business documents
such as spreadsheets, e-mail messages, and word-processing
documents are a type of records. Most records are kept in
electronic format and maintained throughout their life cycle—
from creation to final archiving or destruction by an electronic
records management (ERM) system.

2. Because senior management must ensure that their companies
comply with legal and regulatory duties, managing electronic
records (e-records) is a strategic issue for organizations in both
the public and private sectors. The success of ERM depends
greatly on a partnership of many key players, namely, senior
management, users, records managers, archivists,
administrators, and most importantly, IT personnel. Properly
managed, records are strategic assets. Improperly managed or
destroyed, they become liabilities.
3. Companies need to be prepared to respond to an audit, federal
investigation, lawsuit, or any other legal action against them.
Types of lawsuits against companies include patent violations,
product safety negligence, theft of intellectual property, breach
of contract, wrongful termination, harassment, discrimination,
and many more.
4. Simply creating backups of records is not sufficient because
the content would not be organized and indexed to retrieve them
accurately and easily. The requirement to manage records—
regardless of whether they are physical or digital—is not new.
ERM systems consist of hardware and software that manage and
archive electronic documents and image paper documents; then
index and store them according to company policy. Properly
managed, records are strategic assets. Improperly managed or
destroyed, they become liabilities.
5. Departments or companies whose employees spend most of
their day filing or retrieving documents or warehousing paper
records can reduce costs significantly with ERM. These systems
minimize the inefficiencies and frustration associated with
managing paper documents and workflows. However, they do
not create a paperless office as had been predicted.
An ERM can help a business to become more efficient and
productive by:

Enabling the company to access and use the content contained
in documents.
Cutting labor costs by automating business processes.
Reducing the time and effort required to locate information the
business needs to support decision making.
Improving the security of content, thereby reducing the risk of
intellectual property theft.
Minimizing the costs associated with printing, storing, and
searching for content.
When workflows are digital, productivity increases, costs
decrease, compliance obligations are easier to verify, and green
computing becomes possible.
49
Management Information Systems
Presented by:
Dr. Rickey Casey
Welcome to Class
Edit the text with your own short phrases.
The animation is already done for you; just copy and paste the
slide into your existing presentation.
To change the background image, on the Design tab of the
ribbon, click Format Background, and then Insert picture from
File.
Sample picture courtesy of Bill Staples.
What is Management?
The organization and coordination of the activities of a business
in order to achieve defined objectives.

Management is often included as a factor of production along
with� machines, materials, and money. According to the
management guru Peter Drucker (1909-2005), the basic task of
management includes both marketing and innovation. Practice
of modern management originates from the 16th century study
of low-efficiency and failures of certain enterprises, conducted
by the English statesman Sir Thomas More (1478-1535).
What is Management
Management is the process of reaching organizational goals by
working with and through people and other organizational
resources.
Management has the following 3 characteristics:
It is a process or series of continuing and related activities.
It involves and concentrates on reaching organizational goals.
It reaches these goals by working with and through people and
other organizational resources.
Vision and Mission
Goals and Objectives
What is Management?
MANAGEMENT FUNCTIONS:
The 4 basic management functions that make up the
management process are described in the following sections:
PLANNING
ORGANIZING
INFLUENCING
CONTROLLING.

What is Management?
PLANNING: Planning involves choosing tasks that must be
performed to attain organizational goals, outlining how the
tasks must be performed, and indicating when they should be
performed.
Planning activity focuses on attaining goals. Managers outline
exactly what organizations should do to be successful. Planning
is concerned with the success of the organization in the short
term as well as in the long term.
ORGANIZING:
Organizing can be thought of as assigning the tasks developed
in the planning stages, to various individuals or groups within
the organization. Organizing is to create a mechanism to put
plans into action.
People within the organization are given work assignments that
contribute to the company’s goals. Tasks are organized so that
the output of each individual contributes to the success of
departments, which, in turn, contributes to the success of
divisions, which ultimately contributes to the success of the
organization.
What is Management?
INFLUENCING:
Influencing is also referred to as motivating,leading or
directing.Influencing can be defined as guiding the activities of
organization members in he direction that helps the organization
move towards the fulfillment of the goals.
The purpose of influencing is to increase productivity. Human-
oriented work situations usually generate higher levels of
production over the long term than do task oriented work
situations because people find the latter type distasteful.
CONTROLLING:

Controlling is the following roles played by the manager:
Gather information that measures performance
Compare present performance to pre established performance
norms.
Determine the next action plan and modifications for meeting
the desired performance parameters.
Controlling is an ongoing process.
Motivation
HOW DO WE IN MANAGEMENT MOTIVATE?
What do you think?????
I want to share with you a way to evaluate motivation in the
company you are working for.
What is Information?
Information, as we know it today, includes both electronic and
physical information. The organizational structure must be
capable of managing this information throughout the
information lifecycle regardless of source or format (data, paper
documents, electronic documents, audio, video, etc.) for
delivery through multiple channels that may include cell phones
and web interfaces.
Medical
Financial
Operations Management
Supply chain and logistics
HR
What is Information?
Data that is (1) accurate and timely, (2) specific and organized
for a purpose, (3) presented within a context that gives it

meaning and relevance, and (4) can lead to an increase in
understanding and decrease in uncertainty.
Information is valuable because it can affect behavior, a
decision, or an outcome. For example, if a manager is told
his/her company'snet profit decreased in the past month, he/she
may use this information as a reason to cut financialspending
for the next month. A piece of information is considered
valueless if, after receiving it, things remain unchanged.
COMPETING IN THE
INFORMATION AGE
Did you know . . .
Avatar, the movie, took over 4 yrs to make and cost $450
million
Lady Gaga’s real name is Joanne Angelina Germanotta
It costs $2.6 million for a 30-second advertising time slot
during the Super Bowl
10
Why do you think you need to study management information
systems?
Technology is everywhere in business
Be sure to point-out that these are business magazines, not
technology magazines, and yet they are filled with technology
CLASSROOM EXERCISE
Understanding the Relevance of Technology In Business

This is a great exercise to begin the course. It clearly
demonstrates why anyone involved in business must understand
technology. It can be a real revelation for students who do not
see the need for taking an IT course.
Bring in several copies of BusinessWeek, Fortune, Fast
Company, or any popular business magazine. The magazines do
not have to be current. Provide a marking tool such as a small
set of Post-It Notes. Ask for a few volunteers and have the
students review the magazine and stick a Post-It Note on each
technology-related article, advertisement, etc. When the
student has completed this task, the magazine will be covered in
Post-it Notes, clearly demonstrating that technology is
everywhere in business, even in the popular business magazines
such as BusinessWeek.
Since this task can be time consuming, you can put in the Post-
It Notes prior to class and simply show your students the
completed magazine. You can have one student sit in the front
of the class and begin the exercise, placing Post-It Notes on a
copy of BusinessWeek. After they have completed several
pages on their own, you can produce the same “completed”
magazine with all of the Post-It Notes. This saves classroom
time and still reinforces the point that technology is everywhere
in business.
Be sure to reinforce that these are business magazines, not
technology magazines. Yet they are completely filled with
technology – which is clearly demonstrated by the Post-It
Notes. How can any business student today possibly argue that
they do not need to know or understand technology when faced
with a magazine, such as BusinessWeek, that is filled with
technology? Read a few of the articles or advertisements. Ask
how many of your students are familiar with Siebel, Oracle, or
PeopleSoft and can articulate what they can do for a company?
The goal of this course is to help your students understand the
business side of technology. Being able to understand all of the
technology articles in BusinessWeek is one of the benefits your
students will receive upon completion of the course.

What are systems?
A set of detailedmethods, procedures and routines created to
carry out a specific activity, perform a duty, or solve a problem.
2.An organized, purposeful structure that consists of
interrelated and interdependent elements (components, entities,
factors, members, parts etc.). These elements continually
influence one another (directly or indirectly) to maintain their
activity and the existence of the system, in order to achieve the
goal of the system.
All systems have (a) inputs, outputs and feedbackmechanisms,
(b) maintain an internal steady-state (calledhomeostasis) despite
a changing external environment, (c) display properties that are
different than the whole (called emergent properties) but are not
possessed by any of the individual elements, and (d) have
boundaries that are usually defined by the system observer.
What is a Management Information System?
A management information system (MIS) is a computerized
database of financial information organized and programmed in
such a way that it produces regular reports on operations for
every level of management in a company. It is usually also
possible to obtain special reports from the system easily. The
main purpose of the MIS is to give managers feedback about
their own performance; top management can monitor the
company as a whole. Information displayed by the MIS
typically shows "actual" data over against "planned" results and
results from a year before; thus it measures progress against
goals.
What is an MIS
The MIS receives data from company units and functions. Some
of the data are collected automatically from computer-linked
check-out counters; others are keyed in at periodic intervals.

Routine reports are preprogrammed and run at intervals or on
demand while others are obtained using built-in query
languages; display functions built into the system are used by
managers to check on status at desk-side computers connected
to the MIS by networks. Many sophisticated systems also
monitor and display the performance of the company's stock.
Factors that contribute to a successful MIS System
Factors Contributing to Success
If a MIS is to be success then it should have all the features
listed as follows:
The MIS is integrated into the managerial functions. It sets
clear objectives to ensure that the MIS focuses on the major
issues of the business.
An appropriate information processing technology required to
meet the data processing and analysis needs of the users of the
MIS is selected.
The MIS is oriented, defined and designed in terms of the user's
requirements and its operational viability is ensured.
The MIS is kept under continuous surveillance, so that its open
system design is modified according to the changing
information needs.
MIS focuses on the results and goals, and highlights the factors
and reasons for non achievement.
MIS is not allowed to end up into an information generation
mill avoiding the noise in the information and the
communication system.

The MIS recognizes that a manager is a human being and
therefore, the systems must consider all the human behavioral
factors in the process
of the management.
Factors that contribute to a successful MIS System
The MIS recognizes that the different information needs for
different objectives must be met with. The globalization of
information in isolation
from the different objectives leads to too much information and
information and its non-use.
The MIS is easy to operate and, therefore, the design of the MIS
has such features which make up a user-friendly design.
MIS recognizes that the information needs become obsolete and
new needs emerge. The MIS design, therefore, has a basic
potential capability to
quickly meet new needs of information.
The MIS concentrates on developing the information support to
manager critical success factors. It concentrates on the mission
critical applications
serving the needs of the top management.
Factors that cause and MIS System to Fail
The MIS is conceived as a data processing and not as an
information processing system.
The MIS does not provide that information which is needed by
the managers but it tends to provide the information generally
the function calls for. The MIS then becomes
an impersonal system.

Underestimating the complexity in the business systems and not
recognizing it in the MIS design leads to problems in the
successful implementation.
Adequate attention is not given to the quality control aspects of
the inputs, the process and the outputs leading to insufficient
checks and controls in the MIS.
The MIS is developed without streamlining the transaction
processing systems in the organization.
Factors that cause and MIS System to Fail
Lack of training and appreciation that the users of the
information and the generators of the data are different, and
they have to play an important
responsible role in the MIS.
The MIS does not meet certain critical and key factors of its
users such as a response to the query on the database, an
inability to get the
processing done in a particular manner, lack of user-friendly
system and the dependence on the system personnel.
A belief that the computerized MIS can solve all the
management problems of planning and control of the business.
What causes and MIS to Fail
Lack of administrative discipline in following the standardized
systems and procedures, wrong coding and deviating from the
system
specifications result in incomplete and incorrect information.

The MIS does not give perfect information to all the users in the
organization.
Now we have broken down MIS
Before we move on, I would like for us to select our groups
We will do this by class.
I would like to have 4 per group.
We will use the groups today and for the class paper.

Chapter 3Data Management,Big Data Analytics, andRecords Ma.docx

Recommended

Recommended

More Related Content

Similar to Chapter 3Data Management,Big Data Analytics, andRecords Ma.docx

Similar to Chapter 3Data Management,Big Data Analytics, andRecords Ma.docx (20)

More from keturahhazelhurst

More from keturahhazelhurst (20)

Recently uploaded

Recently uploaded (20)

Chapter 3Data Management,Big Data Analytics, andRecords Ma.docx