Expert answer:Managing IT Professionals in Dynamics Organization
Solved by verified expert:Unit 2 Completed Section. 1.) Explain the concepts of distributed systems and client/server systems, including the role of middleware and the distinction between two-tier and three-tier client/server systems. Requirements: Three scholarly References sources Word count is 1,200 words. APA Format Intext citations is required.
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CHAPTER
4
The Data Resource
W
I
L has been devoted to information technology. The
This is the concluding chapter of Part I of this book, which
previous two chapters have discussed computer systems (both
S hardware and software) and telecommunications and
networking—all central information technology topics. The fourth information technology component that is just
Ohardware and software and sent through the network
as critical as those three is the data that are processed by the
both before and after processing. In fact, without the right N
data captured, stored, and disseminated, the other three
components have no value. This chapter focuses on the all-important data resource.
The data resource consists of the facts and information, an organization gathers while conducting business and
in order to conduct business at all levels of the organization. The data resource’s components include numeric, text,
audio, video, and graphical data collected both within the organization and from sources external to it, as well as
J
the metadata, which describe the business and technical characteristics of the data resource. The variety and
volume of data that are available to organizations has led
Ato data being recognized as a major organizational
resource, to be managed and developed like other assets, such as facilities, labor, and capital. In fact, many
M
observers of trends in business believe that the organizations that will excel in the twenty-first century will be those
I
that manage data and organizational knowledge as a strategic
resource, understand the usefulness of data for
business decisions, and structure data as efficiently as theyE
do other assets.
Organizations are now able to collect more data than ever before through normal business activity, through the
recording of data transactions from point-of-sale (POS) terminals and RFID readers, and via Web and electronic
commerce sites. And the rate of growth in data is enormous.
5 It is not uncommon for an organization to double the
size of its data resource every 18 months. All this data can be an asset only if they are available and understood
when needed and purged when no longer useful; and this 0
cannot occur unless an organization actively organizes
and manages its data. Financial resources are available to
5 build a new plant or to buy raw materials only if a
financial manager and other business managers have planned for enough funds to cover the associated cash
1
requirements. A new product can be designed only if engineering
and personnel managers have anticipated the
needs for certain skills in the workforce. A business certainly
would
not ever think about not planning and
B
managing facilities, labor, and capital. Similarly, data must be planned and managed.
U
The effort to manage organizational data is the responsibility
of every business manager; some business
managers, often called data stewards, are given defined roles to manage specified kinds of data like customer,
product, or employee subject area data. In addition, a special management unit, usually called data or database
administration, often provides overall organizational leadership in the data management function. Furthermore,
some organizations have built knowledge management functions and appointed a chief knowledge officer. Every
manager in an organization has some financial, personnel, equipment, and facilities/space responsibilities. Today,
data must be added to this list of managed assets.
95
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Part I • Information Technology
WHY MANAGE DATA?
One way to view the importance of managing the data
resource is to consider the following questions:
• How much would it cost your company to not comply with Sarbanes–Oxley or other financial reporting
laws because you failed to adequately control data
integrity or document the source (lineage) of data in
your financial statements?
• What would your company do if its critical business
data, such as customer orders, product prices, account
balances, or patient histories, were destroyed? Could
the organization function? For how long?
• What costs would your company incur if sensitive
customer, vendor, or employee data were stolen or
you violated a HIPAA requirement on protecting
health care data? What is the value of the trust you
would lose? Can you identify fraud when customers
return goods or make claims? Can you link all customer transactions together across different retail,
online, and catalog sales channels to determine legitimate and unscrupulous patterns?
• How much time does your organization spend reconciling inconsistent data? Do account balances in
your department always agree with those in central
accounting? What happens when these figures do
not agree? Are there problems with providing custom products because of different specifications by
sales and engineering? Can you track a customer
order all the way from receipt through production to
shipping and billing in a consistent, logical way?
• How difficult is it to determine what data are stored
about the part of the business you manage? What data
exist about customer sales in a particular market? In
what databases do these data reside? What is the
meaning of these data (e.g., do the data include lost
sales, blanket orders, special orders, private label
sales)? How can you gain access to these data, and
who else has access to data you consider that you own?
• Do you know all the contacts a customer has with
your organization? Do you know how profitable a
customer is given their purchases, customer support, billing, and service and warranty activities,
each with associated revenues and costs? And,
W based on profitability, can you make decisions on
to treat a customer whose flight is delayed,
I how
whose account is temporarily overdrawn, or who
L registers a complaint?
S All of these business questions have a foundation in
managing
data. Organizations win by making good decisions
O
fast, and organizations cannot do so without a high-quality
N resource. See the box “Hurricane Windfall.”
data
, Although managing data as a resource has many
general business dimensions, it is also important for the
cost-effective development and operation of information
systems.
Poor systems development productivity is freJ
quently due to a lack of data management, and some methA
ods, such as prototyping, cannot work unless the source of
data
M is clear and the data are available. Systems development time is greatly enhanced by the reuse of data and proIgrams as new applications are designed and built. Unless
data
E are cataloged, named in standard ways, protected but
5
0
Hurricane Windfall
5
What do customers of one of the largest retail chains, Walmart, do as a hurricane is heading their way?
1This was the conclusion of Walmart executives
Sure, they buy flashlights, but they also buy Pop-Tarts.
when they studied trillions of bytes of shopping history
B data from prior hurricane periods as they saw
Hurricane Francis approaching the Florida Atlantic coast. Their ability to quickly react to changes that
U
affect customer buying patterns turns into profits.
Walmart gathers data on purchases at the POS terminals and using credit card numbers and other
means enhances this data to match sales with customer demographics, inventory, supplier, and personnel data to insure that each store has enough of the right products on hand to meet customer
demand—no more, no less. Even in times of emergencies.
Walmart values its nearly 500 terabytes of data so much that it will not even share sales data with
information brokers such as Information Resources, Inc., and ACNielsen, which buy data from retailers.
Data and the ability to see patterns in the data are competitive weapons that allow Walmart, for example, to dynamically reroute trucks from suppliers to better meet anticipated demand. Walmart has been
a leader in the use of its data resource to become a leader in its marketplace.
[Based on Hays, 2004]
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Chapter 4 • The Data Resource
accessible to those with a need to know, and maintained
with high quality, the data and the programs that capture
and maintain them cannot be reused.
There are both technical and managerial issues
regarding the data resource. The next section examines the
technical aspects of managing the data resource that were
not already covered in Chapter 2. It provides an overview
of the most common tools used by database administrators
(DBAs) and systems analysts for describing and managing
data. As responsibilities for managing the data resource are
distributed to the business units, these topics also become
important to all managers.
W
I
L
The Data Model and Metadata
S
A key element in the effective management of data is an
overall map for business data—a data model. A manufac-O
turing company would never think about building a newN
product without developing a detailed design and using
common components and parts from existing products,
TECHNICAL ASPECTS OF MANAGING
THE DATA RESOURCE
where appropriate. The same is true for data. Data entities,
such as customer, order, product, vendor, market, and
J
employee, are analogous to the components of a detailed
design for a product. Just as the detailed blueprint for aA
product shows the relationships among components, the
M
data model shows the relationships among the data entities. A data model shows rules by which the organizationI
operates, such as whether a customer order must be associ-E
ated with a salesperson, an employee must have a social
security number, or the maximum number of direct reports
for a supervisor.
5
Data modeling involves both a methodology and a
notation. The methodology includes the steps that are fol-0
lowed to identify and describe organizational data entities,5
and the notation is a way to show these findings, usually
graphically. Managers must be integrally involved in these1
methodologies to insure that the data you need are plannedB
for inclusion in organizational databases and that the data
U
captured and stored have the required business meaning.
Several possible methodologies are introduced in the following paragraphs, but the reader is referred to texts on
database management for a detailed discussion of data
modeling notations. Figure 4.1 shows a sample data model.
Specifically, it is an entity-relationship diagram (ERD)
Customer
Submits
Order
Includes
FIGURE 4.1 Entity-Relationship Diagram
Product
97
that captures entities (i.e., customer, order, product) and
their relationships (i.e., submits, includes).
The entity-relationship diagram is the most commonly accepted notation for representing the data needs in
an organization. It consists of entities, or the things about
which data are collected; attributes, the actual elements of
data that are to be collected; and relationships, the relevant
associations between organizational entities. The model in
Figure 4.1 could have the attributes of customer last name,
customer first name, customer street, customer city, and so
on to represent the data that would be captured about each
customer. Because of its nontechnical nature, the ERD is a
very useful tool for facilitating communication between
end users who need the data and database designers and
developers who will create and maintain the database.
However, an ERD is not sufficient for documenting
data needs. An ERD is only part of metadata, or data
about data, needed to unambiguously describe data for the
enterprise. Metadata documents the meaning and all the
business rules that govern data. For example, some metadata about an attribute of customer name would define this
term, state its properties such as maximum length and the
type of data (alphanumeric characters) that a value of this
attribute might have, whether every customer has to have
a name to be stored in the database, whether the name can
change in value over time, whether there can be multiple
instances of the name, and who has rights to enter and
change the name. These metadata rules come from the
nature of the organization, so business managers are typically the source of the knowledge to develop these rules.
You can purchase business rules and metadata repository
software systems to help you manage the typically thousands of elements of metadata in an organization. Business
rule software usually covers more rules than just those that
address data (e.g., rules that govern when certain business
processes must be used or which govern how processes
are done).
Creating and maintaining high-quality metadata
takes dedication, yet we cannot insure quality data without
quality metadata. For example, unless everyone in the
organization knows exactly what is meant by the attribute
employee salary, different people might interpret values
for this attribute differently. One of the authors of this text
once worked in an organization that had 17 different definitions of the term customer, each relevant to different
parts of the organization (e.g., billing, retail sales, and
commercial sales). There were good business reasons to
have 17 different interpretations, but it was confusing
when people thought they were working off the same definition but were not. What this organization needed were 17
different, yet related, entities (e.g., retail customer,
business customer, bill-to-customer). Eventually the
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Part I • Information Technology
organization made a commitment, using the concept of
data steward, to actively manage the metadata for each
subject area of the business. This allowed subtle differences in customer data to be recognized and accepted and
for data to be stored accurately. Until this was done, customers were inaccurately billed, product markets were not
accurately understood, and many employees wasted much
time trying to resolve misunderstandings.
Data Modeling
The role of data modeling as part of IS planning is essential. In practice, two rather different approaches are
followed—one top-down, called enterprise modeling, and
one bottom-up, called view integration. Many organizations choose to use both approaches because they are
complementary methods that emphasize different aspects
of data and, hence, check and balance each other.
The enterprise modeling approach involves
describing the organization and its data requirements at a
very high level, independent of particular reports, screens,
or detailed descriptions of data processing requirements.
First, the work of the organization is divided into its major
functions (e.g., selling, billing, manufacturing, and servicing). Then each of these functions is further divided into
processes and each process into activities. An activity is
usually described at a rather high level (e.g., “forecast sales
for next quarter”). This three-level decomposition of the
business is depicted in Figure 4.2.
Given a rough understanding of each activity, a list
of data entities is then assigned to each. For example, quarterly forecasting activity might have the entities product,
customer order history, and work center associated with it.
Function n
Function 1
Process 1.1
Activity 1.1.1
Process 1.m
Activity 1.1.t
Customer
Market
Product
Customer
Order
Channel
Bill
FIGURE 4.2 Enterprise Decomposition for Data
Modeling
The lists of entities are then checked to make sure that consistent names are used and the meaning of each entity is
clear. Finally, based on general business policies and rules
of operation, relationships between the entities are identified, and a corporate data model is drawn. Priorities are
set for what parts of the corporate data model are in need
of greatest improvement, and more detailed work assignments are defined to describe these more clearly and to
revise databases accordingly.
Enterprise modeling has the advantage of not being
biased by a lot of details, current databases and files, or how
the business actually operates today. It is future oriented
and should identify a comprehensive set of generic data
requirements. On the other hand, it can be incomplete or
W
inaccurate because it might ignore some important details.
IThis is where the view integration approach can help.
L In view integration, each report, computer screen,
form,
S document, and so on to be produced from organizational databases is identified (usually starting from what is
O
done today). Each of these is called a user view. The data
elements
in each user view are identified and put into a
N
basic structure called a normal form. Normalization, the
,process of creating simple data structures from more complex ones, consists of a set of rules that yields a data structure that is very stable and useful across many different
J
requirements. In fact, normalization is used as a tool to rid
data of troublesome anomalies associated with inserting,
A
deleting, and updating data. When the data structure is norM
malized, the database can evolve with very few changes to
Ithe parts that have already been developed and populated.
After each user view has been normalized, they are all
E
combined (or integrated) into one comprehensive description. Ideally, this integrated set of entities from normalization will match those from enterprise modeling. In practice,
5
however, this is often not the case because of the different
0
focuses (top-down and bottom-up) of the two approaches.
Therefore,
the enterprise and view-integrated data models
5
are reconciled, and a final data model is developed.
1 An alternative approach to data modeling, which overcomes
B the difficulties of starting from a clean sheet of paper,
is to begin not within the organization but rather from
U
outside, using a generic data model developed for situations
similar to your own. So-called universal, logical, or packaged
data models have been developed from years of experience in
different industries or business areas. Prepackaged data models are customizable for the terminology and business rules
of your organization. Consultants and database software vendors sell these starting points for your corporate data model.
The price for such a packaged data model is roughly the cost
of one to two senior database analysts for a year. Such
prepackaged corporate data models have several significant
advantages, including the following:
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Chapter 4 • The Data Resource
• Data models can be developed using proven components evolved from cumulative experiences. These data
models are kept up-to-date by the provider as new
kinds of data are recognized in an industry (e.g., RFID).
• Projects take less time and cost less because the
essential components and structures are already
defined and only need to be quickly customized to
the particular situation.
• Because prepackaged data models are developed
from best practices, your data model is easier to
evolve as additional data requirements are identified
for the given situation. You avoid missing important
components because prepackaged data models are
designed using thousands of business questions andW
performance indicators.
I
• Adaptation of a data model from your DBMS vendor
usually means that your data model will easily workL
with other applications from this same vendor orS
their software partners.
• A prepackaged data model provides a starting pointO
for asking requirements questions that will help toN
surface unspoken requirements.
• Prepackaged data models use structures that promote,
holistic and flexible, rather than narrow and rigid,
views of data in an organization, thus promoting
J
managing data as an organizational resource.
• If multiple companies in the same industry use theA
same universal data model as the basis for their organizational databases, it may be easier to share data for …
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