Seminar 2 – Part 1
Managing Data to Improve Business
Performance
Ref: Chapter 3 of Turban and Volonino
Learning Objectives
1.
2.
3.
4.
5.
Describe how data and document
management impact profits and performance.
Understand how managers are supported or
constrained by data quality.
Discuss the functions of databases and
database management systems.
Understand how logical views of data provide
a customized support and improve data
security.
Describe the tactical and strategic benefits of
data warehouses, data marts, and data centers.
Learning Objectives cont’d
6. Describe transaction and analytic processing
systems.
7. Explain how enterprise content management
and electronic records management reduce
cost, support business operations, and help
companies meet their regulatory and legal
requirements.
Applebee’s International Learns &
Earns
Problem: Huge quantities of data in many
Databases.
Solution: Enterprise data warehouse
implemented.
Results: Improved profitability.
Applebee’s enterprise data warehouse
and feedback loop.
Data, Master Data, and Document
Management
Data management is crucial for
productivity of managers and employees
 Data – Organisations key asset
 Data decisions  high quality data
 High Quality Data  how data is
managed

Data life cycle

Data Management – a structured
approach to managing data.
◦ Principles of Diminishing Value,
◦ 90/90 Rule, and
◦ Principles of data in context.
Analyzing Any Business
Business analytics
Data Visualization
Dow Jones industrial average (DJIA) for a single day in
tabular display and graphical display.
MDM – Master Data Management
Master Data Management (MDM) – integrating to provide a more unified
view of data
Operational versus Analytical Master Data
Management
Demystifying Master Data Management
Would You Like Fries With That? And Does CrossSelling Justify Master Data Management?
Data management's top eight stories of 2008
Human resources data analytics brings
metrics to workforce management
Master Data Entities

Main entities of company
◦ Customers
◦ Suppliers
◦ Employees

Different Master Data needs
Model of an enterprise data
warehouse.
Data Quality
Data’s usefulness
 Quality of the decision based on the data
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◦
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Accuracy
Accessibility
Data Quality Example
Relevance vs. Reliability
Timeliness
Completeness
Document Management
Effective business continuity planning requires the
timely restoration of critical functions after a
disruption to normal business operations.
 This would include developing plans for recovery
of essential IT infrastructure, critical applications,
and time sensitive business processes.
 In the aftermath of emergency situations,
however, it is all too common to find that
organizations fail to plan adequately to protect
and replicate paper records, as necessary to
mitigate risk and continue operations.

Document Management
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Implementation of any record management
and/or paper digitization solution is a
significant undertaking and must be
approached with a detailed plan of action
and significant buy-in and approval from all
stakeholders. Implementation breaks down
into 4 distinct phases:
Identifying objectives
Identifying products that meet those
objectives
Implementing a system
Digitization of records
File Management Systems Example of primary and foreign keys.
Hierarchy of data for a computerbased file.
A file Management Approach
Computer-based files of this type cause problems such as redundancy,
inconsistency, and data isolation.
Database and Database
Management Systems
DBMS Data Access – An integrated
Approach
Database management system provides access to all data in the
database.
Data Warehouse, Marts and Centers Data warehouse framework and views.
Electronic records management from creation to retention
or destruction.
Content Management
ERM Vendors – check these out!
ACCUTRAC®
SOFTWARE
Managerial Issues
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Reducing uncertainty.
Cost-benefit issues & justification.
Where to store data physically.
Legal issues.
Internal or external collection, storage,
maintenance, & purging of databases of
information.
Disaster recovery.
Data security & ethics.
Privacy.
Legacy systems.
Data delivery.