The Current and Future Role of Data Warehousing in Corporate Application Architecture

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The Current and Future Role of Data Warehousing
in Corporate Application Architecture
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Part 1
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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.

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Data Warehouse as a middleware layer
Decision support applications
Data Warehouse
Operational applications
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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.

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Foundation

• An application architecture model.
• Three dimensions.
• Business process.
• Business unit.
• Business function.

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3-D Model
Integration concepts can be visualized in this
3-D model.
Business function
Business unit
Business process
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Business Process

• Comprises of all the processes that are supported
  by applications.
• Examples CRM,order processing, product
  development,risk management, corporate planning.

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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.

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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.

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Locating applications in the 3-D model
Business unit
Business function
Decision support applications
Data Warehouse
Application cluster B
Cross-product applications
Application cluster A
Vertical applications
Transactional applications
Business process
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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.
• ExampleCustomer 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.

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Product oriented integration (2)

• Although all the data managed by cross-product
  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.

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The role of Data Warehouse

• It is the intermediate layer by which
  subject-oriented 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.

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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.

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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.

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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.

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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.

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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?

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Operational data stores (1)
Business unit
Business function
Application cluster B
Application cluster A
WAP portal
Operational data store
WWW portal
Cross-product applications
Data staging
Vertical applications.
.
.
.
Business process
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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.

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Operational data stores VSData 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.

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Operational data stores VSData 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.

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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.

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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.

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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
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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.

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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.

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Critical view of Data Warehousing
Part 2
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Basic Roles

• Utility.
• Dependence.
• Enabling.

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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.

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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.

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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.

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Strategic alignment

• 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.

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(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.

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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).

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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).

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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.

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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.

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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.

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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.

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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 non-human
  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.

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THE END