The Current and Future Role of Data Warehousing in Corporate Application Architecture - PowerPoint PPT Presentation

Loading...

PPT – The Current and Future Role of Data Warehousing in Corporate Application Architecture PowerPoint presentation | free to view - id: 831167-ZDk4Z



Loading


The Adobe Flash plugin is needed to view this content

Get the plugin now

View by Category
About This Presentation
Title:

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

Description:

The Current and Future Role of Data Warehousing in Corporate Application Architecture . . – PowerPoint PPT presentation

Number of Views:64
Avg rating:3.0/5.0
Slides: 43
Provided by: Gior227
Category:

less

Write a Comment
User Comments (0)
Transcript and Presenter's Notes

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


1
The Current and Future Role of Data Warehousing
in Corporate Application Architecture
???st?? ?pa?????. G?????? ?a?a?aµp?p?????.
Part 1
2
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.

3
Data Warehouse as a middleware layer
Decision support applications
Data Warehouse
Operational applications
4
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.

5
Foundation
  • An application architecture model.
  • Three dimensions.
  • Business process.
  • Business unit.
  • Business function.

6
3-D Model
Integration concepts can be visualized in this
3-D model.
Business function
Business unit
Business process
7
Business Process
  • Comprises of all the processes that are supported
    by applications.
  • Examples CRM,order processing, product
    development,risk management, corporate planning.

8
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.

9
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.

10
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
11
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.

12
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.

13
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.

14
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.

15
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.

16
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.

17
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.

18
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?

19
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
20
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.

21
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.

22
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.

23
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.

24
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.

25
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
26
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.

27
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.

28
Critical view of Data Warehousing
Part 2
29
Basic Roles
  • Utility.
  • Dependence.
  • Enabling.

30
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.

31
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.

32
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.

33
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.

34
(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.

35
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).

36
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).

37
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.

38
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.

39
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.

40
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.

41
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.

42
THE END
About PowerShow.com