The Current and
Future Role of Data
Warehousing in
Corporate Application
Architecture
Χρήστος Μπακάλης.
Γιώργος Χαραλαμπόπουλος.
Part 1
Introduction
Data Warehouse: A repository of
historical data, subject-oriented
and organized, summarized and
integrated from various sources
so as to be easily accessed and
manipulated for decision support.
Data Warehouse as a
middleware layer
Decision support applications
Data Warehouse
Operational applications
The goal: Application
Integration
Examine the future role of Data
Warehousing in Corporate Application
Architectures.
Analyze the potentials of reusing data
warehousing methodology and
management concepts for decoupling
traditional transactional applications and
channel-oriented applications.
Foundation
An application architecture model.
Three dimensions.
1. Business process.
2. Business unit.
3. Business function.
3-D Model
Integration concepts can
be visualized in this 3-D
model.
Business
function
Business unit
Business process
Business Process
Comprises of all the processes that are
supported by applications.
Examples: CRM,order processing,
product development,risk management,
corporate planning.
Business unit
Comprises of all the organizational units
that result from customer segmentation,
product grouping, or a combination of
both.
Example scales: Retail banking units,
fixed line telephony units, life insurance
units.
Business function
Comprises of all the functions that are
supported by applications.
Examples: Create file orders, calculate
prices,create contracts, billing or plan
resource utilization.
Locating applications
in the 3-D model
Business unit
Business
function
Decision support applications
Data Warehouse
Application cluster B
Cross-product Application cluster A
applications
Vertical applications
Transactional applications
Business process
Product-oriented
integration (1)
It is a cross-functional integration strategy.
From these ‘vertical’ applications companies
transfer certain business functions into
dedicated cross-product applications.
Example:Customer data management to be
transferred from various product-specific
applications into a single cross-product
‘partner management’ application to avoid
problems of redundant customer data
management and create opportunities for
cross selling programs.
Product oriented
integration (2)
Although all the data managed by crossproduct applications are processed by all
other applications and thereby become ‘core’
data, they should be treated as operational
data.
As a result cross-product applications can be
treated as transactional applications.
Thus, product oriented integration is
complemented by core data integration.
The role of Data
Warehouse
It is the intermediate layer by which subjectoriented information for decision support
applications is derived from transaction data.
This database is used by all decision support
applications as a single source of consistent
data.
It has its own architecture components for
data extraction, data staging, data
transformation, data integration, data
correction, etc.
A data warehouse can be implemented as a
centralized system but can also be
implemented in a decentralized way.
Characteristics of Data
warehousing (1)
Organization: Data are organized by
detailed subject containing only
information relevant for decision
support.
Consistency: Data in different
operational databases may be encoded
differently. In the Data warehouse they
will be coded in a consistent manner.
Characteristics of Data
warehousing (2)
Time variant: The data are kept for
several years so they can be used for
trends, forecasting, and comparisons
over time.
Nonvolatile: Data in the warehouse are
not updated.
Relational: Relational structure is used.
Client/Server: Provide easy access to
data.
Channel management
and integration (1)
Customers demand multiple access
channels to products/services.
Management has to decide which
channels to use for which
products/services without being
restricted by IS/IT restrictions.
Access media: Cellular phone and WAP,
Internet, phone, etc.
Channel management
and integration (2)
As a consequence, vertical applications and
cross-product applications have to be
complemented by channel- specific
applications.
E.g.:WWW portal, WAP portal,etc.
Hence, product-oriented integration (along
with core data integration) should be
complemented by channel-oriented
integration.
Representation of
channel-specific
applications
Channel-specific applications can be
represented as ‘horizontal’ applications in the
3-D model.
Channel-specific applications are created by
transferring and integrating selected business
functions from vertical applications.
How to decouple horizontal and vertical
applications?
Operational data stores
(1)
Business function
Business unit
Application
cluster B
Application
cluster A
Crossproduct
applica
tions
Vertical
applications.
Data Oper
stagi ation
ng al
data
store
WAP
portal
WWW
portal
…….
…….
…….
Business process
Operational data stores
(2)
The concept of operational data stores is
introduced when real time access is required.
It is used for short term decisions involving
mission critical applications rather than for the
medium and long term decisions associated
with the regular data warehouse.
It can also be thought of as a source system
for the data warehouse to avoid duplication of
integration functionality.
Operational data stores
VS
Data warehouse (1)
Focus on providing actual data for
reporting: Data warehouse is sufficient.
Focus on applications that have to
exchange subject-oriented data in real
time: Operational data store should be
introduced.
Operational data stores
VS
Data warehouse (2)
Operational data stores: A ‘local’ closed
loop approach can be supported
between vertical and horizontal
applications.
Data warehousing: Efficient information
supply between transactional
applications and decision support
applications can be achieved.
Reusing Data
warehousing concepts
for application
integration based on
Operational data stores
1) Project justification.
2) Permanent organization.
3) Development methodology.
4) Meta data management.
Project justification
Application integration provides tangible
benefits.
As a result project justification can
benefit from data warehousing-relating
issues like the division between the IT
and business units.
Permanent organization
Data ownership has emerged as a
conceptual foundation from which roles
and responsibilities as well as
processes for permanent data
warehousing were derived.
Data warehousing
Application
integration.
Organizational issues
Development
methodology
Missing specifications.
Data marts can be used to avoid them.
By focusing on Data warehouse
development phases it is interesting to
find that they appear to have high reuse
potential for application integration.
Meta data management
Meta-data: Data about data,including
summaries, indices, software programs
about data, etc.
All meta data that are relevant for Data
warehousing are also relevant for
application integration based on
Operational data stores and vice versa.
Critical view of Data
Warehousing
Part 2
Basic Roles
Utility.
Dependence.
Enabling.
Utility
It is aimed at reducing the costs of
processing and communicating
information throughout the organization.
 This is achieved by the aggregation of
data and their organization by subject
containing information relevant for
decision support.
Dependence
The performance of a business process
depends upon the information
infrastructure, like the use of an ERP
package.
The link between the business strategy
and infrastructure investment is
obvious.
 Whether to use Data warehousing or
Operational data stores should be
decided carefully depending on the
focus.
Enabling
Enabling infrastructures provide
architectures and platforms for new
applications. This yields flexibility.
 Time savings for data suppliers and
users, availability of better information
as a foundation for better decisions.
 Coexistence of Data warehouse and
Operational data stores.
Strategic alignment


1.
2.
3.
How to link infrastructure to business
strategy.
Specify the needs of the corporation:
Example: Data warehouse suitability.
Large amounts of data.
Data stored in different systems.
Necessity for users to conduct
extensive analysis.
(Knowledge) Sharing
An infrastructure is usually shared by
the members of a community in the
sense that it is the same single object
used by all of them.
 Users access the Data warehouse take
a copy of the needed data for analysis.
This analysis is done using mining tools
and leads to knowledge.
Openness
Infrastructures are open in the sense
that there are no limits to the number of
users , stakeholders, vendors, etc.
involved in the network.
 In the case of Data warehousing this
leads to varying constellations and
alliances between humans (users) that
access the data and non-human tools
(Data warehouse).
Heterogeneity
Data warehousing constituencies
include technological components and
humans,( socio-technical networks) thus
interaction is a crucial factor of success.
 Lack of incentive to share data and
Knowledge can be costly.
Data warehouse as a middleware layer
can link DS applications with
Operational applications, and integrate
independent components (ecologies of
infrastructures).
Increasing Returns
Increasing Returns: The more a product
is produced, sold, or used the more
valuable or profitable it becomes.
The same applies for infrastructure
standards.
 Data warehousing: Lowering the cost.
 Exploitation of warehouse data leads to
knowledge.
 Greater efficiency.
Path dependence
Path dependence means that the past
events will have large impacts on future
development.
Form of path dependence: Compatibility.
 Operational data stores should not be
developed from scratch.
Switching costs and
Lock-in
As the community using the same
technology or standard grows, switching
to a new technology or standard
becomes an increasingly larger
coordination challenge.
 How to introduce Operational data
stores?
 Coexistence with Data warehousing.
 Key issue: Strategy to avoid lock-in –
Evolution strategy.
Evolution strategy
Evolution strategy offers an easy
migration path, and centers on reducing
switching costs so that the users, can
try the new technology gradually.
 Key issue: Linkage between the new
technology and the old one.
Actor-Network theory
Infrastructure is a powerful actor in itself,
seeking allies and fighting battles in order to
survive.
Separating a priori human actors and nonhuman tools creates difficulties in
understanding the implementation of
infrastructure.
 Well-run infrastructure: Successful alliance
between human and non-human actors.
 Data warehousing cost: Lack of incentive to
share data.
THE END