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Chapter 6
Foundations of Business Intelligence: Database and Information
Management
Student Learning Objectives
1. How does a relational database organize data?
2. What are the principles of a database management system?
3. What are the principal tools and technologies for accessing information from databases to
improve business performance and decision-making?
4. What is the role of information policy and data administration in the management of
organizational data resources?
5. Why is data quality assurance so important for a business?
Chapter Outline
6.1 The Database Approach to Data Management
Entities and Attributes
Organizing Data in a Relational Database
Establishing Relationships
6.2 Database Management Systems
Operations of a Relational DBMS
Capabilities of Database Management Systems
Non-Relational Databases and Databases in the Cloud
6.3 Using Databases to Improve Business Performance and Decision Making
The Challenge of Big Data
Business Intelligence Infrastructure
Analytical Tools: Relationships, Patterns, Trends
Databases and the Web
6.4 Managing Data Resources
Establishing an Information Policy
Ensuring Data Quality
Key Terms
The following alphabetical list identifies the key terms discussed in this chapter. The page
number for each key term is provided.
Analytic platform, 200 Hadoop, 199
Attribute, 188 Information policy, 207
Big data, 198 In-memory computing, 200
Data administration, 207 Key field, 188
Data cleansing, 208 Non-relational database management systems, 196
Data definition, 194 Normalization, 191

Data dictionary, 194 Online analytical processing (OLAP), 201
Data manipulation language, 194 Primary key, 188
Data mart, 199 Query, 194
Data mining, 202 Record, 188
Data quality audit, 208 Referential integrity, 191
Data warehouse, 199 Relational DBMS, 188
Database, 187 Report generator, 196
Database administration, 207 Sentiment analysis, 203
Database management system (DBMS), 193 Structured Query Language (SQL), 194
Database server, 206 Text mining, 203
Entity, 188 Tuples, 188
Entity-relationship diagram, 190 Web mining, 203
Field, 188
Foreign key, 189
Teaching Suggestions
The essential message of this chapter is the statement that “How businesses store, organize, and
manage their data has a tremendous impact on organizational effectiveness.” Data have now
become central and even vital to an organization’s survival.
The opening vignette, “Better Data Management Helps the Toronto Globe and Mail Reach Its
Customers,” shows that data are not easy to access and analyze without the properly configured
systems. The Toronto Globe and Mail created numerous pockets of data in isolated databases.
There was no central repository where the most up-to-date data could be accessed from a single
place. That made it difficult to cross reference subscribers with prospective customers. It created
security issues because data were housed in multiple places.
The organization implemented a new system with a single data warehouse so it could be easily
accessed and analyzed. Data were reconciled to prevent errors in marketing campaigns and
reduce the cost of recruiting new customers and keeping the old ones.
Section 6.1, “The Database Approach to Data Management” introduces basic key terms such
as field, record, file, database, entity, and attribute. Try using a simple spreadsheet print-out to
demonstrate these terms. If you have access to a relational DBMS during class time, you can
demonstrate several of the concepts presented in this section. If you have time and as a class
activity, ask your students to prepare an entity-relationship diagram, as well as normalize the
data. Your students will need guidance from you to complete this activity, but it will help them
see and understand the logical design process.
Section 6.2, “Database Management System” introduces database design and management
requirements for database systems. Help your students see how a logical design allows them to
analyze and understand the data from a business perspective, while a physical design shows how
the database is arranged on direct access storage devices. At this point, you can use the
enrollment process at your university as an example. Have your students prepare a logical design
for the enrollment process. Discuss the three operations of a relational DBMS: select, project,

and join. A database management system is comprised of three components: a data definition
language, data dictionary, and data manipulation language. If you have access to a relational
DBMS during class time, you can demonstrate several of the concepts presented in this section.
The concept of using databases stored in cloud computing data centers is introduced in this
section and piggybacks on cloud computing concepts introduced earlier in the text.
Section 6.3: “Using Databases to Improve Business Performance and Decision Making.” This
section focuses on how data technologies are actually used: data warehouses, data marts,
business intelligence, multidimensional data analysis, and data mining. Regardless of their career
choice, students will probably use some or all of these in their jobs. For example, data
warehouses and data marts are important to many business functions. They are critical for those
who want to use data mining technologies which have many uses in management analysis and
business decisions. Keep in mind as you teach this chapter that managing data resources can be
very technical, but many students will need and want to know the business uses and business
values. In the end, effectively managing data is the goal. Doing it in a way that will enable your
students to contribute to the success of their organization is the reason why most students are in
this course.
This section discusses text mining and Web mining that are taking on significance as more data
and information are stored in text documents and on the Web. Web mining is divided into three
categories: content mining, structure mining, and usage mining. Each one provides specific
information on patterns in Web data.
Interactive Session: Organizations: Business Intelligence Helps the Cincinnati Zoo Know
Its Customers
Case Study Questions
1. What people, organization, and technology factors were behind Cincinnati Zoo losing
opportunities to increase revenue?
People: more than two thirds of the Zoo’s budget is paid from fundraising efforts with the
remainder coming from tax support, admission fees, food, and gifts. Senior management
embarked on a comprehensive review of its operations to determine how the Zoo could increase
revenues and improve performance.
Organization: Management had limited knowledge and understanding of what was actually
happening in the Zoo on a day-to-day basis, other than how many people visited every day and
the Zoo’s total revenue.
Technology: The Zoo’s four income streams—admissions, membership, retail, and food
service—had different point-of-sale platforms. The food service business which brings in $4
million a year still relied on manual cash registers. Management had to sift through paper
receipts just to understand daily sales totals. The Zoo used spreadsheets to collect visitors’ ZIP
codes to use for geographic and demographic analysis.

2. Why was replacing legacy point-of-sale systems and implementing a data warehouse
essential to an information system solution?
If all the data collected could be combined with insight into visitor activity at the Zoo the
information would be extremely valuable for guiding marketing efforts. Some of the data
management wanted to collect included the attractions people visited, what people ate and drank,
and what they bought at gift shops.
The information systems needed to focus more on analytics and data management. Four legacy
point-of-sale systems were replaced with a single platform. A centralized data warehouse was
built and implementation of a business intelligence program provided real-time analytics and
reporting.
3. Describe the types of information gleaned from data mining that helped the Zoo better
understand visitor behavior.
Weather plays a large part in people coming to the zoo and what they eat and drink while they
are there. By including weather-related data in the system, the Zoo can now make more accurate
decisions about labor scheduling and inventory planning.
When visitors scan membership cards at entrances, exits, attractions, restaurants, and stores, the
data provides insight into spending patterns and visitation behaviors. Management can segment
visitors based on the data and target marketing and promotions specifically for each customer
segment.
4. How did the Cincinnati Zoo benefit from business intelligence? How did it enhance
operational performance and decision making?
The Zoo is able to tailor campaigns more precisely to smaller groups of people, increasing its
chances of identifying the people who are most likely to respond to its mailings. More targeted
marketing helped the Zoo cut $40,000 from its annual marketing budget.
When the business intelligence platform showed management that food sales tail off significantly
after 3 p.m. each day, they started closing some of the food outlets at that time. On the other
hand, detailed data analysis showed a big spike in ice cream sales occurs during the last hour
before the Zoo closes. Management decided to keep open soft-serve ice cream outlets for the
entire day. Management can adjust inventory levels of its inventory of beer and determine which
beer is selling best, on what day, and at what time. Now they can assure plenty of inventories of
beer brands.
5. The Zoo’s management recently stated that it might have to ask for more revenue from
taxes in order to provide the same level of quality and service in the future. How might
business intelligence be used to prevent this from happening?

The Zoo’s ability to make better decisions about operations led to dramatic improvements in
sales. Six months after deploying its business intelligence solution, the Zoo achieved a 30.7
percent increase in food sales and a 5.9 percent increase in retail sales compared to the same
period a year earlier.
With more data analysis and using the business intelligence tools at their disposal, management
can make more adjustments in marketing campaigns, employee work schedules, and inventories
to further increase profit margins and revenues, perhaps staving off any tax increases.
Section 5.4: “Managing Data Resources.” This section introduces students to some of the
critical issues surrounding corporate data. Students should realize that setting up the database is
only the beginning of the process. Managing the data is the real challenge. In fact, the main point
is to show how data management has changed and the reason why data must be organized,
accessed easily by those who need access, and protected from the wrong people accessing,
modifying, or harming the data.
Developing a database environment requires much more than selecting database technology. It
requires a formal information policy governing the maintenance, distribution, and use of
information in the organization. The organization must also develop a data administration
function and a data-planning methodology. The organization should use data-planning
techniques to make sure that the organization’s data model delivers information efficiently for its
business processes and enhances organizational performance. There is political resistance in
organizations to many key database concepts, especially the sharing of information that has been
controlled exclusively by one organizational group. Creating a database environment is a long-
term endeavor requiring large up-front investments and organizational change.
Interactive Session: People: American Water Keeps Data Flowing
Case Study Questions
1. Discuss the role of information policy, data administration, and efforts to ensure data
quality in improving data management at American Water.
An important step in creating a single source of data with enterprise-wide reporting was to
efficiently and effectively migrate the data from the old system to the new system. The company
data resided in many different systems in various formats. Each regional business maintained
some of its own data in its own systems and some data were redundant and inconsistent. Data
had to be standardized so it could be used across the organization.
All the business users had to buy into this new company-wide view of data.
2. Describe roles played by information systems specialists and end users in American
Water’s systems transformation project.

Management made business users responsible for the data. It was not just a responsibility of the
information systems department. The business “owns” the data and it is business needs that
determine the rules and standards for managing the data.
Business users were required to inventory and review all the pieces of data in the systems to
determine which would migrate from the old system to the new system and which would be left
behind. Business users were also required to review the data to make sure they are accurate and
consistent and that redundant data are eliminated.
3. Why was the participation of business users so important? If they didn’t play this role,
what would have happened?
Because business users are the ones who primarily will use the data, it should be the way they
want it and the way it works best for them. Users are the ones who know best what they need.
It’s too easy to blame someone else for faulty data if users don’t make their own decisions about
the data.
If someone else determines which data to migrate, which data to leave behind, or how the data
should be constructed it may simply end up a failure.
4. How did implementing a data warehouse help American Water move toward a more
centralized organization?
All data pertaining to materials used by the company were standardized to make the data
warehouse more efficient and to give a consolidated view across all business units. Standardized
data gave the company a better picture of how it was performing. Reports were easier to generate
and gave a more complete picture of operations. It made comparisons between operating units
easier and allowed business units to review best practices more easily than before.
5. Give some examples of problems that would have occurred at American Water if its
data were not “clean?”
If data are not clean, it makes the data warehouse much larger than necessary. For instance, a
particular type of material may have three or four different data descriptions. Comparisons and
consolidation of the data are more difficult if not impossible with more than one description and
definition of each data item.
Replenishing inventories could be less efficient if more than one data entry per item were used in
the database. The database could reflect that a particular item is out of stock based on one data
entry while in reality it is in full supply but under a different data entry.
6. How would American Water’s data warehouse improve operations and management
decision making?
The company is focusing on promoting the idea that data must be “clean” to be effective and has
poured an incredible amount of effort into its data cleansing work by identifying incomplete,

incorrect, inaccurate, and irrelevant pieces of data and then replacing, modifying, or deleting the
“dirty” data.
By having clean data in a single data warehouse that’s easily accessible and where reports are
easy to generate, management and users can make better decisions because the data are more
complete. Clean data gives a much better picture of the organization and a clearer direction for
management. Data mining is much easier and more complete with clean data.
Review Questions
6-1 How does a relational database organize data?
Define and explain the significance of entities, attributes, and key fields.
• Entity is a person, place, thing, or event on which information can be obtained.
• Attribute is a piece of information describing a particular entity.
• Key field is a field in a record that uniquely identifies instances of that record so that it
can be retrieved, updated, or sorted. For example, a person’s name cannot be a key field
because there can be another person with the same name, whereas a social security
number is unique. Also a product name may not be unique but a product number can be
designed to be unique.
Define a relational database and explain how it organizes and stores information.
The relational database is the primary method for organizing and maintaining data in most
modern information systems. It organizes data in two-dimensional tables with rows and
columns called relations. Each table contains data about an entity and its attributes. Each row
represents a record and each column represents an attribute or field. Each table also contains
a key field to uniquely identify each record for retrieval or manipulation.
Explain the role of entity-relationship diagrams and normalization in database design.
Relational databases organize data into two-dimensional tables (called relations) with
columns and rows. Each table contains data on an entity and its attributes. An entity-
relationship diagram graphically depicts the relationship between entities (tables) in a
relational database. A well-designed relational database will not have many-to-many
relationships, and all attributes for a specific entity will only apply to that entity.
Normalization is the process of creating small stable data structures from complex groups of
data when designing a relational database. Normalization streamlines relational database
design by removing redundant data such as repeating data groups. A well-designed relational
database will be organized around the information needs of the business and will probably be
in some normalized form. A database that is not normalized will have problems with
insertion, deletion, and modification.
Define a non-relational database management system and explain how it differs from a

relational database.
There are four main reasons for the rise in non-relational databases: cloud computing,
unprecedented data volumes, massive workloads for Web services, and the need to store
new types of data. These systems use more flexible data models and are designed for
managing large data sets across distributed computing networks. They are easy to scale up
and down based on computing needs.
They can process structured and unstructured data captured from Web sites, social media,
and graphics. Traditional relational databases aren’t able to process data from most of those
sources. Non-relational databases can also accelerate simple queries against large volumes
of structured and unstructured data. There’s no need to predefine a formal database
structure or change that definition if new data are added later.
Relational databases contain very structured data in tables with rows and columns and don’t
handle unstructured data such as videos, pictures, texts, and emails very well, if at all.
Relational databases require a defined database and the definitions must be changed and
updated if new data are later added.
6-2 What are the principles of a database management system?
Define a database management system (DBMS), describe how it works and explain how
it benefits organizations.
A database management system (DBMS) is a specific type of software for creating, storing,
organizing, and accessing data from a database. A DBMS consists of software that permits
centralization of data and data management so that businesses have a single, consistent
source for all their data needs. A single database services multiple applications. The most
important feature of the DBMS is its ability to separate the logical and physical views of
data. The user works with a logical view of data. The DBMS retrieves information so that the
user does not have to be concerned with its physical location.
Define and compare the logical and a physical view of data.
The DBMS relieves the end user or programmer from the task of understanding where and
how the data are actually stored by separating the logical and physical views of the data. The
logical view presents data as end users or business specialists would perceive them, whereas
the physical view shows how data are actually organized and structured on physical storage
media, such as a hard disk.
Define and describe the three operations of a relational database management system.
In a relational database, three basic operations are used to develop useful sets of data: select,
project, and join.
• Select operation: creates a subset consisting of all records in the file that meet stated

criteria. In other words, select creates a subset of rows that meet certain criteria.
• Join operation: combines relational tables to provide the user with more information
than is available in individual tables.
• Project operation: creates a subset consisting of columns in a table, permitting the user
to create new tables that contain only the information required.
Name and describe the three major capabilities of a DBMS.
A DBMS includes capabilities and tools for organizing, managing, and accessing the data in
the database. The principal capabilities of a DBMS include a data definition language, data
dictionary, and data manipulation language.
• The data definition language specifies the structure and content of the database.
• The data dictionary is an automated or manual file that stores information about the data
in the database, including names, definitions, formats, and descriptions of data elements.
• The data manipulation language, such as SQL, is a specialized language for accessing
and manipulating the data in the database.
6-3 What are the principal tools and technologies for accessing information from databases
to improve business performance and decision making?
Define big data and describe the technologies for managing and analyzing big data.
Traditional databases rely on neatly organized content in rows and columns. Much of the
data collected nowadays by companies don’t fit into that mold.
Big data describes datasets with volumes so huge they are beyond the ability of typical
database management system to capture, store, and analyze. The term doesn’t refer to any
specific quantity of data but it’s usually measured in the petabyte and exabyte range. It
includes structured and unstructured data captured from Web traffic, email messages, and
social media content such as tweets and status messages. It also includes machine-generated
data from sensors.
Big data contains more patterns and interesting anomalies than smaller data sets. That
creates the potential to determine new insights into customer behavior, weather patterns,
financial market activity, and other phenomena.
Hadoop: open-source software framework that enables distributed parallel processing of
huge amounts of data across inexpensive computers. The software breaks huge problems
into smaller ones, processes each one on a distributed network of smaller computers, and
then combines the results into a smaller data set that is easier to analyze. It uses non-
relational database processing and structured, semistructured, and unstructured data.
In-memory computing: rather than using disk-based database software platforms, this
technology relies primarily on a computer’s main memory for data storage. It eliminates

bottlenecks that result from retrieving and reading data in a traditional database and
shortens query response times. Advances in contemporary computer hardware technology
makes in-memory processing possible.
Analytic platforms: uses both relational and non-relational technology that’s optimized for
analyzing large datasets. They feature preconfigured hardware–software systems designed
for query processing and analytics.
List and describe the components of a contemporary business intelligence
infrastructure.
Business intelligence (BI) infrastructures include an array of tools for obtaining useful
information from all the different types of data used by businesses today, including
semistructured and unstructured big data in vast quantities. Data warehouses, data marts,
Hadoop, in-memory processing, and analytical platforms are all included in BI
infrastructures.
Powerful tools are available to analyze and access information that has been captured and
organized in data warehouses and data marts. These tools enable users to analyze the data to
see new patterns, relationships, and insights that are useful for guiding decision making.
These tools for consolidating, analyzing, and providing access to vast amounts of data to help
users make better business decisions are often referred to as business intelligence. Principal
tools for business intelligence include software for database query and reporting tools for
multidimensional data analysis and data mining.
Describe the capabilities of online analytical processing (OLAP).
Data warehouses support multidimensional data analysis, also known as online analytical
processing (OLAP), which enables users to view the same data in different ways using
multiple dimensions. Each aspect of information represents a different dimension.
OLAP represents relationships among data as a multidimensional structure, which can be
visualized as cubes of data and cubes within cubes of data, enabling more sophisticated data
analysis. OLAP enables users to obtain online answers to ad hoc questions in a fairly rapid
amount of time, even when the data are stored in very large databases. Online analytical
processing and data mining enable the manipulation and analysis of large volumes of data
from many perspectives, for example, sales by item, by department, by store, by region, in
order to find patterns in the data. Such patterns are difficult to find with normal database
methods, which is why a data warehouse and data mining are usually parts of OLAP.
Define data mining, describe what types of information can be obtained from it, and
explain how it differs from OLAP.
Data mining provides insights into corporate data that cannot be obtained with OLAP by
finding hidden patterns and relationships in large databases and inferring rules from them to
predict future behavior. The patterns and rules are used to guide decision making and

forecast the effect of those decisions. The types of information obtained from data mining
include associations, sequences, classifications, clusters, and forecasts.
Explain how text mining and Web mining differ from conventional data mining.
Conventional data mining focuses on data that have been structured in databases and files.
Text mining concentrates on finding patterns and trends in unstructured data contained in text
files. The data may be in email, memos, call center transcripts, survey responses, legal cases,
patent descriptions, and service reports. Text mining tools extract key elements from large
unstructured data sets, discover patterns and relationships, and summarize the information.
Web mining helps businesses understand customer behavior, evaluate the effectiveness of a
particular Web site, or quantify the success of a marketing campaign. Web mining looks for
patterns in data through:
• Web content mining: extracting knowledge from the content of Web pages
• Web structure mining: examining data related to the structure of a particular Web site
• Web usage mining: examining user interaction data recorded by a Web server
whenever requests for a Web site’s resources are received
Describe how users can access information from a company’s internal database
through the Web.
Conventional databases can be linked via middleware to the Web or a Web interface to
facilitate user access to an organization’s internal data. Web browser software on a client PC
is used to access a corporate Web site over the Internet. The Web browser software requests
data from the organization’s database, using HTML commands to communicate with the
Web server. Because many back-end databases cannot interpret commands written in HTML,
the Web server passes these requests for data to special middleware software that then
translates HTML commands into SQL so that they can be processed by the DBMS working
with the database. The DBMS receives the SQL requests and provides the required data. The
middleware transfers information from the organization’s internal database back to the Web
server for delivery in the form of a Web page to the user. The software working between the
Web server and the DBMS can be an application server, a custom program, or a series of
software scripts.
6-4 What is the role of information policy and data administration in the management of
organizational data resources?
Define information policy and data administration and explain how they help
organizations manage their data.
An information policy specifies the organization’s rules for sharing, disseminating,
acquiring, standardizing, classifying, and inventorying information. Information policy lays
out specific procedures and accountabilities, identifying which users and organizational units

can share information, where information can be distributed, and who is responsible for
updating and maintaining the information.
Data administration is responsible for the specific policies and procedures through which
data can be managed as an organizational resource. These responsibilities include developing
information policy, planning for data, overseeing logical database design and data dictionary
development, and monitoring how information systems specialists and end-user groups use
data.
In large corporations, a formal data administration function is responsible for information
policy, as well as for data planning, data dictionary development, and monitoring data usage
in the firm.
6-5 Why is data quality assurance so important for a business?
List and describe the most common data quality problems.
Data that are inaccurate, incomplete, or inconsistent create serious operational and financial
problems for businesses because they may create inaccuracies in product pricing, customer
accounts, and inventory data, and lead to inaccurate decisions about what actions an
organization should take. Firms must take special steps to make sure they have a high level
of data quality. These include using enterprise-wide data standards, databases designed to
minimize inconsistent and redundant data, data quality audits, and data cleansing software.
List and describe the most important tools and techniques for assuring data quality.
A data quality audit is a structured survey of the accuracy and level of completeness of the
data in an information system. Data quality audits can be performed by surveying entire data
files, surveying samples from data files, or surveying end users for their perceptions of data
quality.
Data cleansing consists of activities for detecting and correcting data in a database that are
incorrect, incomplete, improperly formatted, or redundant. Data cleansing not only corrects
data but also enforces consistency among different sets of data that originated in separate
information systems.
Discussion Questions
6-6 It has been said that you do not need database management software to create a
database environment. Discuss.
A database is a collection of data organized to service many applications at the same time by
storing and managing data so that they appear to be in one location. It is not mandated that a
database have a DBMS. What is most important is the concept of a database—a model for
organizing information so that it can be stored and accessed flexibly and efficiently. Without
the right vision of a database and data model, a DBMS is not effective. A DBMS is special

software to create and maintain a database. It enables individual business applications to
extract the data they need without having to create separate files or data definitions in their
computer programs. However, the use of a DBMS can reduce program-data dependence along
with program development and maintenance costs. Access and availability of information can
be increased because users and programmers can perform ad-hoc queries of data in the
database. The DBMS allows the organization to centrally manage data, its use, and security.
6-7 To what extent should end users be involved in the selection of a database management
system and database design?
End users should be involved in the selection of a database management system and the
database design. Developing a database environment requires much more than just selecting
the technology. It requires a change in the corporation’s attitude toward information. The
organization must develop a data administration function and a data planning methodology.
The end-user involvement can be instrumental in mitigating the political resistance
organizations may have to many key database concepts, especially to sharing information that
has been controlled exclusively by one organizational group.
6-8 What are the consequences of an organization not having an information policy?
Without an information policy anyone could:
• Reorganize data.
• Maintain it in non-conforming ways that would make it difficult to use the data
throughout the organization.
• View data even if their job didn’t require it—that leads to data compromise,
misuse, and abuse.
• Change data even if they don’t have a viable reason to.
Well-constructed information policies specify the rules for sharing, disseminating, acquiring,
standardizing, classifying, and inventorying information. Information policies lay out specific
procedures and accountabilities, identifying which users and units can share information,
where information can be distributed, and who is responsible for updating and maintaining
the information. Overall, information policies can protect one of an organization’s most
valuable resources.
Hands-On MIS Projects
Management Decision Problems
6-9 Emerson Process Management: data warehouse was full of inaccurate and redundant data
gathered from numerous transaction processing systems. The design team assumed all users
would enter data the same way. Users actually entered data in multiple ways. Assess the
potential business impact of these data quality problems. What decisions have to be made and
steps taken to reach a solution?

Managers and employees can’t make accurate and timely decisions about customer activity
because of inaccurate and redundant data. The company could be wasting resources pursuing
customers it shouldn’t and neglecting its best customers. The company could be experiencing
financial losses resulting from the inaccurate data.
Managers, employees, and data administrators need to identify and correct the faulty data and
then establish better routines for editing data when it’s entered. The company should perform a
data quality audit by surveying entire data files, surveying samples from data files, or surveying
end users for perceptions of data quality. The company needs to perform data cleansing
operations to correct errors and enforce consistency among the different sets of data at their
origin.
6-10 Industrial supply company: the company wants to create a single data warehouse by
combining several different systems. The sample files from the two systems that would supply
the data for the data warehouse contain different data sets.
1. What business problems are created by not having these data in a single standard
format?
Managers are unable to make good decisions about the company’s sales and products because of
inconsistent data. Managers can’t determine which products are selling the best worldwide; they
can only determine product sales by region.
2. How easy would it be to create a database with a single standard format that could store
the data from both systems? Identify the problems that would have to be addressed.
It may not be too hard to create a database with a single standard format if the company used
middleware to pull both data sets into a consolidated database. The company should use
specialized data-cleansing software that would automatically survey data files, correct errors in
the data, and integrate the data in a consistent company-wide format. Problems that may occur
would stem from inconsistent data names such as the Territory and Customer ID in the old sets
and data element names such as Division in the new set. The data administrators, managers, and
employees may have to track the data conversion and manually convert some data.
3. Should the problems be solved by the database specialist or general business managers?
Explain.
Both the database specialist and general business managers should help solve the problems. Data
administrators are responsible for developing an information policy, planning for data,
overseeing logical database design and data dictionary development, and monitoring how
information system specialists and end-user groups use data. However, end users and business
managers have the final decision-making authority and responsibility for the data.
4. Who should have the authority to finalize a single company-wide format for this
information in the data warehouse?

Owners and managers are the only ones who have the authority to finalize the format for the
information in the data warehouse. They could develop an information policy that specifies the
organization’s rules for sharing, disseminating, acquiring, standardizing, classifying, and
inventorying information.
Achieving Operational Excellence: Building a Relational Database for Inventory
Management
Software skills: Database design, querying and reporting
Business skills: Inventory Management
6-11 This exercise requires that students know how to create queries and reports using
information from multiple tables. The solutions provided here were created using the query
wizard and report wizard capabilities in Microsoft Access. Students can, of course, create more
sophisticated reports if they wish.
The database would need some modification to answer other important questions about the
business. The owners might want to know, for example, which are the fastest-selling bicycles.
The existing database shows products in inventory and their suppliers. The owners might want to
add an additional table (or tables) in the database to house information about product sales, such
as the product identification number, date placed in inventory, date of sale, purchase price, and
customer name, address, and telephone number. Management could use this enhanced database
to create reports on best selling bikes over a specific period, the number of bicycles sold during a
specific period, total volume of sales over a specific period, or best customers. Students should
be encouraged to think creatively about what other pieces of information should be captured on
the database that would help the owners manage the business.
The answers to the following questions can be found in the Microsoft Access File named:
Ess10ch05solutionfile.mdb.
1. Prepare a report that identifies the five most expensive bicycles. The report should list the
bicycles in descending order from most expensive to least expensive, the quantity on hand
for each, and the markup percentage for each.
2. Prepare a report that lists each supplier, its products, quantities on hand, and associated
reorder levels. The report should be sorted alphabetically by supplier. Within each supplier
category, the products should be sorted alphabetically.
3. Prepare a report listing only the bicycles that are low in stock and need to be reordered. The
report should provide supplier information for the items identified.
4. Write a brief description of how the database could be enhanced to further improve
management of the business. What tables or fields should be added? What additional reports
would be useful?
Improving Decision Making: Searching Online Databases for Overseas Business Resources

Software skills: Online databases
Business skills: Researching services for overseas operations
6-12 List the companies you would contact to interview on your trip to determine whether
they can help you with these and any other functions you think vital to establishing your
office.
Student answers will vary based on the companies they choose to contact.
Rate the databases you used for accuracy of name, completeness, ease-of-use, and general
helpfulness.
The U.S. Department of Commerce Web site contains a fair amount of economic information.
However, it may be simpler to direct your students to go to http://www.aol.com. The Web site
for the Nationwide Business Directory of Australia is http://www.nationwide.com.au.
What does this exercise tell you about the design of databases?
Students may not understand that the World Wide Web is one massive data warehouse, but in
nontechnical terms that is exactly what it is. Remind them of this when they are completing this
assignment. This assignment may best be accomplished in groups, where they can consolidate
their findings into a written or oral presentation.
Video Case Questions
You will find a video case illustrating some of the concepts in this chapter on the Laudon Web
site at www.pearsonhighered.com/laudon along with questions to help you analyze the case.
Collaboration and Teamwork: Identifying Entities and Attributes in an Online
Database
6-13 With a group of two or three of your fellow students, select an online database to
explore, such as AOL Music or the Internet Movie Database. Explore these Web sites to see
what information they provide. Then list the entities and attributes that they must keep
track of in their databases. Diagram the relationship between the entities you have
identified. If possible, use Google Sites to post links to Web pages, team communication
announcements, and work assignments; to brainstorm; and to work collaboratively on
project documents. Try to use Google Docs to develop a presentation of your findings for
the class.
There are hundreds of Internet Movie Databases so students will have to select the one that
interests them. The Web sites for AOL Music and Gracenote.com are listed below.
http://music.aol.com/

http://gracenote.com/
In their analysis, students should explain that many of these sites use the same entities and
attributes to keep track of their data. Some of the entities that AOL Music tracks include artists,
videos, songs, radio, pictures, news, lyrics, and concerts. The Web site tracks the following
attributes for the artist entity: popularity ranking of each artist, number of people that have
viewed a particular album by the artist, the genre (country, pop, rock, etc), a biography, who the
artist is most influenced by, photos, videos, all the albums the artist has published, lyrics of each
song, and ringtones users can send to their cellphones.
Business Problem-Solving Case: Does Big Data Bring Big Rewards?
6-14 Describe the kinds of “big data” collected by the organizations described in this case.
The New York City Police Department is collecting data on crimes and criminals to make it
easier to determine future criminal activity. Information on criminals, such as a suspect’s photo
with details of past offenses or addresses with maps can be visualized in seconds on a video wall
or instantly relayed to officers at a crime scene.
Other organizations are using the data to go green, or in the case of Vestas, to go even greener.
Vestas is the world’s largest wind energy company. Location data are important to Vestas so that
it can accurately place its turbines for optimal wind power generation. Vestas relies on location-
based data to determine the best spots to install their turbines. The company combines data from
global weather systems along with data from existing turbines.
Vestas increased the size of its wind library and is able to manage and analyze location and
weather data with models that are much more powerful and precise thanks to big data.
AutoZone uses big data to help it adjust inventory and product prices at some of its 5,000 stores.
To help it target deals at the local level, the company analyzes information gleaned from a
variety of databases. It uses cloud services models to quickly increase the amount of data
analyzed without bringing down the system or changing a line of code.
6-15 List and describe the business intelligence technologies described in this case.
Most of the companies are relying on data warehouses that are easy to maintain and easy to use.
Data marts that store smaller subsets of data from the warehouses are also being used to analyze
smaller chunks of data.
Sears is using Hadoop open-source software that allows the retailer to get closer to its customers
and offer special deals to individual customers. It used to take Sears six weeks to analyze
marketing campaigns for loyalty club members using a mainframe. Now with Hadoop it can be
done weekly. Sears’ old model was able to use 10 percent of available data, but the new models
are able to work with 100 percent of the data. The company now keeps data indefinitely rather
than the 90 days to two years it stored data previously.

Google used special analytics platforms to try to determine how many people were contracting
influenza based on the number of search queries users were entering in its search engines.
Unfortunately, the data it used was narrowly defined and did not put the search queries in
context.
6-16 Why did the companies described in this case need to maintain and analyze big data?
What business benefits did they obtain? How much were they helped by analyzing big
data?
By using big data and business intelligence techniques, companies can make better decisions
about the effective and efficient use of resources. For instance, Hertz determined it had a
problem at its Philadelphia location based on data it had collected. It solved the problem by
increasing staffing levels at certain hours and ensured a manager was present to solve bottlenecks
and make customers happier.
Vestas made better decisions about where to place its wind turbines based on data it collected
and processed using business intelligence techniques. It analyzed the best locations and did not
waste resources placing turbines where there was not enough wind to make them efficient or
where there was too much wind that in turn damaged the turbines.
The NYPD was able to discover hidden patterns in criminal activity such as correlations between
time, opportunity, and organizations or non-obvious relationships between individuals and
criminal organizations that would be difficult to uncover in smaller data sets. That allowed the
Department to deploy its officers to locations most likely to experience criminal activity and
protect citizens while making appropriate arrests.
6-17 Identify three decisions that were improved by using big data.
• Vestas’ placement of turbines to maximize the amount of power generated
• AutoZone’s offers to customers for discounts and specials based on location-based data
• Sears’ marketing efforts to offer individual customers special deals and discounts
6-18 Should all organizations try to analyze big data? Why or why not? What people,
organization, and technology issues should be addressed before a company decides to work
with big data?
Based on the information provided about Sears’ failed attempts at boosting sales and market
share, big data is not a magic bullet. Having a lot of data without the organizational structure to
support it doesn’t accomplish much. Sears has spent gobs of money on technology.
Unfortunately, it hasn’t had the organizational success to increase profits.
Sears has been slow to reduce operating costs, keep pace with current merchandising trends, and
remodel its stores. Its efforts to use its data to more effectively target customers and boost sales
have failed to translate into a competitive advantage because the rest of the organization and its
people didn’t match those efforts. Sales have been declining every year since 2005.

Jim Sullivan, a partner at loyalty marketing firm Colloquy, notes that a good loyalty program
that gives a company better intelligence about what its customers really want can be a strategic
advantage, but even the best loyalty programs can’t fix a fundamentally broken brand.

Exploring the Variety of Random
Documents with Different Content

But when I draw the scanty cloak of silence over my eyes,
Piteous Love comes peering under the hood.
Touches the clasp with trembling fingers, and tries
To put her ear to the painful sob of my blood,
While her tears soak through to my breast,
Where they burn and cauterise.
III
The moon lies back and reddens.
In the valley, a corncrake calls
Monotonously,
With a piteous, unalterable plaint, that deadens
My confident activity:
With a hoarse, insistent request that falls
Unweariedly, unweariedly,
Asking something more of me,
Yet more of me!

R E M I N D E R
Do you remember
How night after night swept level and low
Overhead, at home, and had not one star,
Nor one narrow gate for the moon to go
Forth to her field of November.
And you remember,
How towards the north a red blot on the sky
Burns like a blotch of anxiety
Over the forges, and small flames ply
Like ghosts the shadow of the ember.
Those were the days
When it was awful autumn to me,
When only there glowed on the dark of the sky
The red reflection of her agony,
My beloved smelting down in the blaze
Of death—my dearest
Love who had borne, and was now leaving me.
And I at the foot of her cross did suffer
My own gethsemane.
xvi

So I came to you,
And twice, after great kisses, I saw
The rim of the moon divinely rise
And strive to detach herself from the raw
Blackened edge of the skies.
Strive to escape;
With her whiteness revealing my sunken world
Tall and loftily shadowed. But the moon
Never magnolia-like unfurled
Her white, her lamp-like shape.
For you told me no,
And bade me not to ask for the dour
Communion, offering—“a better thing.”
So I lay on your breast for an obscure hour
Feeling your fingers go
Like a rhythmic breeze
Over my hair, and tracing my brows,
Till I knew you not from a little wind:
—I wonder now if God allows
Us only one moment his keys.
If only then
You could have unlocked the moon on the night,
And I baptized myself in the light
Of your love; we both have entered then the white
Pure passion, and never again.
I wonder if only
You had taken me then, how different
Life would have been: should I have spent
Myself in waste, and you have bent
Your pride, through being lonely?
xvii

B E I H E N N E F
The little river twittering in the twilight,
The wan, wondering look of the pale sky,
This is almost bliss.
And everything shut up and gone to sleep,
All the troubles and anxieties and pain
Gone under the twilight.
Only the twilight now, and the soft “Sh!” of the river
That will last for ever.
And at last I know my love for you is here,
I can see it all, it is whole like the twilight,
It is large, so large, I could not see it before
Because of the little lights and flickers and interruptions,
Troubles, anxieties and pains.
You are the call and I am the answer,
You are the wish, and I the fulfilment,
You are the night, and I the day.
What else—it is perfect enough,
It is perfectly complete,
You and I,
What more——?
Strange, how we suffer in spite of this!
xviii

L I G H T N I N G
I felt the lurch and halt of her heart
Next my breast, where my own heart was beating;
And I laughed to feel it plunge and bound,
And strange in my blood-swept ears was the sound
Of the words I kept repeating,
Repeating with tightened arms, and the hot blood’s blindfold art.
Her breath flew warm against my neck,
Warm as a flame in the close night air;
And the sense of her clinging flesh was sweet
Where her arms and my neck’s blood-surge could meet.
Holding her thus, did I care
That the black night hid her from me, blotted out every speck?
I leaned me forward to find her lips,
And claim her utterly in a kiss,
When the lightning flew across her face,
And I saw her for the flaring space
Of a second, afraid of the clips
Of my arms, inert with dread, wilted in fear of my kiss.
xix

A moment, like a wavering spark,
Her face lay there before my breast,
Pale love lost in a snow of fear,
And guarded by a glittering tear,
And lips apart with dumb cries;
A moment, and she was taken again in the merciful dark.
I heard the thunder, and felt the rain,
And my arms fell loose, and I was dumb.
Almost I hated her, she was so good,
Hated myself, and the place, and my blood,
Which burned with rage, as I bade her come
Home, away home, ere the lightning floated forth again.
xx

S O N G - D AY I N AU T U M N
When the autumn roses
Are heavy with dew,
Before the mist discloses
The leaf’s brown hue,
You would, among the laughing hills
Of yesterday
Walk innocent in the daffodils,
Coiffing up your auburn hair
In a puritan fillet, a chaste white snare
To catch and keep me with you there
So far away.
When from the autumn roses
Trickles the dew,
When the blue mist uncloses
And the sun looks through,
You from those startled hills
Come away,
Out of the withering daffodils;
Thoughtful, and half afraid,
Plaiting a heavy, auburn braid
And coiling it round the wise brows of a maid
Who was scared in her play.
xxi

When in the autumn roses
Creeps a bee,
And a trembling flower encloses
His ecstasy,
You from your lonely walk
Turn away,
And leaning to me like a flower on its stalk,
Wait among the beeches
For your late bee who beseeches
To creep through your loosened hair till he reaches,
Your heart of dismay.
xxii

AWA R E
Slowly the moon is rising out of the ruddy haze,
Divesting herself of her golden shift, and so
Emerging white and exquisite; and I in amaze
See in the sky before me, a woman I did not know
I loved, but there she goes and her beauty hurts my heart;
I follow her down the night, begging her not to depart.
xxiii

A PA N G O F R E M I N I S C E N C E
High and smaller goes the moon, she is small and very far from me,
Wistful and candid, watching me wistfully, and I see
Trembling blue in her pallor a tear that surely I have seen before,
A tear which I had hoped that even hell held not again in store.
xxiv

A W H I T E B LO SS O M
A tiny moon as white and small as a single jasmine flower
Leans all alone above my window, on night’s wintry bower,
Liquid as lime-tree blossom, soft as brilliant water or rain
She shines, the one white love of my youth, which all sin cannot
stain.
xxv

R E D M O O N - R I S E
The train in running across the weald has fallen into a steadier stroke
So even, it beats like silence, and sky and earth in one unbroke
Embrace of darkness lie around, and crushed between them all the
loose
And littered lettering of leaves and hills and houses closed, and we
can use
The open book of landscape no more, for the covers of darkness
have shut upon
Its written pages, and sky and earth and all between are closed in
one.
And we are smothered between the darkness, we close our eyes and
say “Hush!” we try
To escape in sleep the terror of this immense deep darkness, and we
lie
Wrapped up for sleep. And then, dear God, from out of the twofold
darkness, red
As if from the womb the moon arises, as if the twin-walled darkness
had bled
In one great spasm of birth and given us this new, red moon-rise
Which lies on the knees of the darkness bloody, and makes us hide
our eyes.
xxvi

The train beats frantic in haste, and struggles away
From this ruddy terror of birth that has slid down
From out of the loins of night to flame our way
With fear; but God, I am glad, so glad that I drown
My terror with joy of confirmation, for now
Lies God all red before me, and I am glad,
As the Magi were when they saw the rosy brow
Of the Infant bless their constant folly which had
Brought them thither to God: for now I know
That the Womb is a great red passion whence rises all
The shapeliness that decks us here-below:
Yea like the fire that boils within this ball
Of earth, and quickens all herself with flowers,
God burns within the stiffened clay of us;
And every flash of thought that we and ours
Send up to heaven, and every movement, does
Fly like a spark from this God-fire of passion;
And pain of birth, and joy of the begetting,
And sweat of labour, and the meanest fashion
Of fretting or of gladness, but the jetting
Of a trail of the great fire against the sky
Where we can see it, a jet from the innermost fire:
And even in the watery shells that lie
Alive within the cozy under-mire,
A grain of this same fire I can descry.
xxvii

And then within the screaming birds that fly
Across the lightning when the storm leaps higher;
And then the swirling, flaming folk that try
To come like fire-flames at their fierce desire,
They are as earth’s dread, spurting flames that ply
Awhile and gush forth death and then expire.
And though it be love’s wet blue eyes that cry
To hot love to relinquish its desire,
Still in their depths I see the same red spark
As rose to-night upon us from the dark.

R E T U R N
Now I am come again, you who have so desired
My coming, why do you look away from me?
Why does your cheek burn against me—have I inspired
Such anger as sets your mouth unwontedly?
Ah, here I sit while you break the music beneath
Your bow; for broken it is, and hurting to hear:
Cease then from music—does anguish of absence bequeath
Me only aloofness when I would draw near?
xxviii

T H E A P P E A L
You, Helen, who see the stars
As mistletoe berries burning in a black tree,
You surely, seeing I am a bowl of kisses,
Should put your mouth to mine and drink of me.
Helen, you let my kisses steam
Wasteful into the night’s black nostrils; drink
Me up I pray; oh you who are Night’s Bacchante,
How can you from my bowl of kisses shrink!
xxix

R E P U L S E D
The last, silk-floating thought has gone from the dandelion stem,
And the flesh of the stalk holds up for nothing a blank diadem.
The night’s flood-winds have lifted my last desire from me,
And my hollow flesh stands up in the night abandonedly.
As I stand on this hill, with the whitening cave of the city beyond,
Helen, I am despoiled of my pride, and my soul turns fond:
Overhead the nightly heavens like an open, immense eye,
Like a cat’s distended pupil sparkles with sudden stars,
As with thoughts that flash and crackle in uncouth malignancy
They glitter at me, and I fear the fierce snapping of night’s thought-
stars.
Beyond me, up the darkness, goes the gush of the lights of two
towns,
As the breath which rushes upwards from the nostrils of an immense
Life crouched across the globe, ready, if need be, to pounce
Across the space upon heaven’s high hostile eminence.
All round me, but far away, the night’s twin consciousness roars
With sounds that endlessly swell and sink like the storm of thought
in the brain,
Lifting and falling like slow breaths taken, pulsing like oars
Immense that beat the blood of the night down its vein.
xxx
xxxi

The night is immense and awful, Helen, and I am insect small
In the fur of this hill, clung on to the fur of shaggy, black heather.
A palpitant speck in the fur of the night, and afraid of all,
Seeing the world and the sky like creatures hostile together.
And I in the fur of the world, and you a pale fleck from the sky,
How we hate each other to-night, hate, you and I,
As the world of activity hates the dream that goes on on high,
As a man hates the dreaming woman he loves, but who will not
reply.

D R E A M - C O N F U S E D
Is that the moon
At the window so big and red?
No one in the room,
No one near the bed——?
Listen, her shoon
Palpitating down the stair?
—Or a beat of wings at the window there?
A moment ago
She kissed me warm on the mouth,
The very moon in the south
Is warm with a bloody glow,
The moon from far abysses
Signalling those two kisses.
And now the moon
Goes slowly out of the west,
And slowly back in my breast
My kisses are sinking, soon
To leave me at rest.
xxxii

C O R OT
The trees rise tall and taller, lifted
On a subtle rush of cool grey flame
That issuing out of the dawn has sifted
The spirit from each leaf’s frame.
For the trailing, leisurely rapture of life
Drifts dimly forward, easily hidden
By bright leaves uttered aloud, and strife
Of shapes in the grey mist chidden.
The grey, phosphorescent, pellucid advance
Of the luminous purpose of God, shines out
Where the lofty trees athwart stream chance
To shake flakes of its shadow about.
The subtle, steady rush of the whole
Grey foam-mist of advancing God,
As He silently sweeps to His somewhere, his goal,
Is heard in the grass of the sod.
Is heard in the windless whisper of leaves
In the silent labours of men in the fields,
In the downward dropping of flimsy sheaves
Of cloud the rain skies yield.
xxxiii

In the tapping haste of a fallen leaf,
In the flapping of red-roof smoke, and the small
Foot-stepping tap of men beneath
These trees so huge and tall.
For what can all sharp-rimmed substance but catch
In a backward ripple, God’s purpose, reveal
For a moment His mighty direction, snatch
A spark beneath His wheel.
Since God sweeps onward dim and vast,
Creating the channelled vein of Man
And Leaf for His passage, His shadow is cast
On all for us to scan.
Ah listen, for Silence is not lonely:
Imitate the magnificent trees
That speak no word of their rapture, but only
Breathe largely the luminous breeze.
xxxiv

M O R N I N G W O R K
A gang of labourers on the piled wet timber
That shines blood-red beside the railway siding
Seem to be making out of the blue of the morning
Something faery and fine, the shuttles sliding,
The red-gold spools of their hands and faces shuttling
Hither and thither across the morn’s crystalline frame
Of blue: trolls at the cave of ringing cerulean mining,
And laughing with work, living their work like a game.
xxxv

T R A N S F O R M AT I O N S
I
The Town
Oh you stiff shapes, swift transformation seethes
About you: only last night you were
A Sodom smouldering in the dense, soiled air;
To-day a thicket of sunshine with blue smoke-wreaths.
To-morrow swimming in evening’s vague, dim vapour
Like a weeded city in shadow under the sea,
Beneath an ocean of shimmering light you will be:
Then a group of toadstools waiting the moon’s white taper.
And when I awake in the morning, after rain,
To find the new houses a cluster of lilies glittering
In scarlet, alive with the birds’ bright twittering,
I’ll say your bond of ugliness is vain.
xxxvi

II
The Earth
Oh Earth, you spinning clod of earth,
And then you lamp, you lemon-coloured beauty;
Oh Earth, you rotten apple rolling downward,
Then brilliant Earth, from the burr of night in beauty
As a jewel-brown horse-chestnut newly issued:—
You are all these, and strange, it is my duty
To take you all, sordid or radiant tissued.
III
Men
Oh labourers, oh shuttles across the blue frame of morning,
You feet of the rainbow balancing the sky!
Oh you who flash your arms like rockets to heaven,
Who in lassitude lean as yachts on the sea-wind lie!
You who in crowds are rhododendrons in blossom,
Who stand alone in pride like lighted lamps;
Who grappling down with work or hate or passion,
Take strange lithe form of a beast that sweats and ramps:
You who are twisted in grief like crumpled beech-leaves,
Who curl in sleep like kittens, who kiss as a swarm
Of clustered, vibrating bees; who fall to earth
At last like a bean-pod: what are you, oh multiform?
xxxvii

R E N AS C E N C E
We have bit no forbidden apple,
Eve and I,
Yet the splashes of day and night
Falling round us no longer dapple
The same Eden with purple and white.
This is our own still valley
Our Eden, our home,
But day shows it vivid with feeling
And the pallor of night does not tally
With dark sleep that once covered its ceiling.
My little red heifer, to-night I looked in her eyes,
—She will calve to-morrow:
Last night when I went with the lantern, the sow was grabbing her
litter
With red, snarling jaws: and I heard the cries
Of the new-born, and after that, the old owl, then the bats that
flitter.
And I woke to the sound of the wood-pigeons, and lay and listened,
Till I could borrow
A few quick beats of a wood-pigeon’s heart, and when I did rise
The morning sun on the shaken iris glistened,
And I saw that home, this valley, was wider than Paradise.
xxxviii

I learned it all from my Eve
This warm, dumb wisdom.
She’s a finer instructress than years;
She has taught my heart-strings to weave
Through the web of all laughter and tears.
And now I see the valley
Fleshed all like me
With feelings that change and quiver:
And all things seem to tally
With something in me,
Something of which she’s the giver.
xxxix

D O G -T I R E D
If she would come to me here,
Now the sunken swaths
Are glittering paths
To the sun, and the swallows cut clear
Into the low sun—if she came to me here!
If she would come to me now,
Before the last mown harebells are dead,
While that vetch clump yet burns red;
Before all the bats have dropped from the bough
Into the cool of night—if she came to me now!
The horses are untackled, the chattering machine
Is still at last. If she would come,
I would gather up the warm hay from
The hill-brow, and lie in her lap till the green
Sky ceased to quiver, and lost its tired sheen.
I should like to drop
On the hay, with my head on her knee
And lie stone still, while she
Breathed quiet above me—we could stop
Till the stars came out to see.
xl

I should like to lie still
As if I was dead—but feeling
Her hand go stealing
Over my face and my hair until
This ache was shed.

M I C H A E L-A N G E LO
God shook thy roundness in His finger’s cup,
He sunk His hands in firmness down thy sides,
And drew the circle of His grasp, O Man,
Along thy limbs delighted, thine, His bride’s.
And so thou wert God-shapen: His finger
Curved thy mouth for thee, and His strong shoulder
Planted thee upright: art not proud to see
In the curve of thine exquisite form the joy of the Moulder?
He took a handful of light and rolled a ball,
Compressed it till its beam grew wondrous dark,
Then gave thee thy dark eyes, O Man, that all
He made had doorway to thee through that spark.
God, lonely, put down His mouth in a kiss of creation,
He kissed thee, O Man, in a passion of love, and left
The vivid life of His love in thy mouth and thy nostrils;
Keep then the kiss from the adultress’ theft.
xli

V I O L E TS
Sister, tha knows while we was on the planks
Aside o’ th’ grave, while th’ coffin wor lyin’ yet
On th’ yaller clay, an’ th’ white flowers top of it
Tryin’ to keep off ’n him a bit o’ th’ wet,
An’ parson makin’ haste, an’ a’ the black
Huddlin’ close together a cause o’ th’ rain,
Did t’ ’appen ter notice a bit of a lass away back
By a head-stun, sobbin’ an’ sobbin’ again?
—How should I be lookin’ round
An’ me standin’ on the plank
Beside the open ground,
Where our Ted ’ud soon be sank?
Yi, an’ ’im that young,
Snapped sudden out of all
His wickedness, among
Pals worse n’r ony name as you could call.
Let be that; there’s some o’ th’ bad as we
Like better nor all your good, an’ ’e was one.
—An’ cos I liked him best, yi, bett’r nor thee,
I canna bide to think where he is gone.
xlii

Ah know tha liked ’im bett’r nor me. But let
Me tell thee about this lass. When you had gone
Ah stopped behind on t’ pad i’ th’ drippin wet
An’ watched what ’er ’ad on.
Tha should ha’ seed her slive up when we’d gone,
Tha should ha’ seed her kneel an’ look in
At th’ sloppy wet grave—an’ ’er little neck shone
That white, an’ ’er shook that much, I’d like to begin
Scraïghtin’ my-sen as well. ’En undid her black
Jacket at th’ bosom, an’ took from out of it
Over a double ’andful of violets, all in a pack
Ravelled blue and white—warm, for a bit
O’ th’ smell come waftin’ to me. ’Er put ’er face
Right intil ’em and scraïghted out again,
Then after a bit ’er dropped ’em down that place,
An’ I come away, because o’ the teemin’ rain.
xliii

W H E T H E R O R N OT
I
Dunna thee tell me its his’n, mother,
Dunna thee, dunna thee.
—Oh ay! he’ll be comin’ to tell thee his-sèn
Wench, wunna he?
Tha doesna mean to say to me, mother,
He’s gone wi that—
—My gel, owt’ll do for a man i’ the dark,
Tha’s got it flat.
But ’er’s old, mother, ’er’s twenty year
Older nor him—
—Ay, an’ yaller as a crowflower, an’ yet i’ the dark
Er’d do for Tim.
Tha niver believes it, mother, does ter?
It’s somebody’s lies.
—Ax him thy-sèn wench—a widder’s lodger;
It’s no surprise.
xliv

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