Data integration is a major objective of many public
and private sector organizations. Resolving the existing data disparity and
creating an integrated data resource is a key strategy to improving data resource
quality. However, there are three major problems with most current data integration
strategies. First, organizations are not stopping the ongoing creation of disparate
data before they begin integrating the existing disparate data. Allowing the
continued creating of disparate data simply makes the problem worse because
disparate data are created faster than they can ever be resolved through data
integration. Second, organizations are not integrating all components of the
data resource, including the data descriptions, data structure, data integrity,
data documentation, data models, and data management practices. At best, they
are concentrating only on the data, not management of the data resource. Third,
organizations are not integrating their entire data resource. They are simply
concentrating on integrating disparate data relating to their current business
problems.
This seminar is oriented toward the basic concepts,
principles, and techniques for stopping the creation of disparate data, resolving
the existing disparate data, and creating a high-quality enterprise-wide data
resource that is readily shared. The concepts of a common data architecture,
overall data resource quality, a formal data resource guide, meaningful data
resource data, formal data integrity rules developed as a subset of business
rules, a five-tier five-schema approach to data models, architecture driven
data models will be presented, and coordinated management of the data resource.
Examples will be provided that show the existing problems with disparate data
and how those problems can be resolved.
This seminar is designed for people that are, or
will be, actively involved in managing or carrying out the tasks related to
stopping the rapid increase in disparate data and resolving the existing disparate
data. Both business clients and information technology staff will benefit from
this seminar because both are needed in any data resource integration project.
Business clients have the knowledge about data and the current data problems
while the information technology staff has the skills to perform the actual
data integration. It is the partnership between business clients and information
technology staff that make data integration successful.
In addition, this seminar will be useful for anyone
interested in selling the concepts of stopping the creation of disparate data
or integrating disparate data, anyone planning a data integration strategy for
an organization, or anyone managing a data integration project. These people,
though not actively involved in data integration tasks, can better sell, plan,
and manage a data integration project if they have a basic understanding of
the concepts, principles, and techniques involved in data integration.
This seminar is oriented toward the practical aspects
of data integration. It is based on the actual integration of disparate data
in many different public and private sector organizations. It provides different
approaches to integrating disparate data depending on an organizations problems
and needs. It explains how the concepts, principles, and techniques of the common
data architecture are combined with an organizations problems, needs, and environment
to form a customized method for resolving data disparity and developing a readily
shared enterprise data resource.
The seminar attendee will learn:
The current situation
that most organizations have with rapidly increasing quantities of disparate
data.
Why disparate
data are being created and what needs to be done to stop the creation of disparate
data.
The concept of
a common data architecture for understanding and resolving disparate data
and creating a high-quality integrated data resource.
The concept of
overall data resource quality and how to improve data resource quality within
a common data architecture
The techniques
for naming, defining, structuring, perfecting, and documenting data.
The techniques
for inventorying and understanding disparate data, and documenting the results.
The techniques
for designating preferred data sources, preferred data forms, and preferred
data integrity rules.
The techniques
for developing data translation schemes.
The concepts of
formal data resource transition for disparate data to a high-quality enterprise-wide
data resource.
The techiques
for selectively sourcing data that are the most current and correct.
The techniques
for transforming disparate data, including data reconstruction, data translation,
data recasting, data restructuring, and data derivation.
The techniques
for developing a high-quality data resource that is shared across organizational
units and business activities.
Cultural considerations
that support development of a high-quality data resource.
Current Situation
Business Information Demand
Disparate Data
Data Dilemma
Data Resource Concepts
Business Intelligence Value Chain
Data Resource Quality
Data Resource Framework
Common Data Architecture
Architecture Driven Data Modeling
Five-Tier Five-Schema Concept
Definition of Terms
Stopping Data Disparity
Bad Habits & Good Practices
Architectural Good Practices
Data Names
Data Definitions
Data Structure
Data Integrity Rules
Data Documentation
Non-Architectural Good Practices
Data Resource Orientation
Data Availability
Data Resource Responsibility
Data Resource Vision
Data Resource Recognition
Best Practices
Data Resource Transition
Transition States
Data Inventory
Data Profiling
Data Tracking
Data Understanding & Cross-Referencing
Preferred Data Designations
Data Translation Schemes
Enterprise Data Architecture
Data Transformation
Data Extract
Data Transformation
Data Loading
Prominent Data Issues
Non-Tabular Data
Data Globalization
Conflicting Standards
Generic Data Architecture
Purchased Applications
Acquired Data
Cultural Considerations
Mr.
Brackett retired from the State of Washington in 1996 where he was
the State's Data Resource Coordinator where he was responsible for developing
a common data architecture for the State that spans multiple jurisdictions.
Mr. Brackett has been in the data processing field for nearly 40 years,
during which time he developed many innovative concepts and techniques
for designing applications and managing data resources. He is considered
the originator of the common data architecture concept, the data resource
framework, the data naming taxonomy and data naming vocabulary, the five-tier
five-schema concept, the data rule concept, and the business intelligence
value chain. He is the founder of Data Resource Design and Remodeling
and is the consulting data architect specializing in developing integrated
data resources.
Mr.
Brackett has written six books on the topic of application design, data
design, and common data architectures. His books on Data Sharing Using
a Common Data Architecture and The Data Warehouse Challenge: Taming
Data Chaos explain the concept and uses of a common data architecture
for developing an integrated data resource. His latest book on Data
Resource Quality: Turning Bad Habits into Good Practices explains
how to stop the creation of disparate data. He has written many articles
and is a well-known author, speaker, and trainer on data resource design
and data resource quality. He is the President of DAMA I for 2000, 2001,
and 2002, and is listed in Who's Who in the West, Who's Who in Education,
and International Who's Who.
