Decision Support Systems 1201311 Data Warehousing

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Decision Support Systems1201311Data Warehousing

• Chattrakul Sombattheera

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Agenda

• Definitions and Concepts
• Process Overview
• Architecture
• Data Integration and the ETL Processes
• Development
• Real-Time Data Warehousing
• Administration and Security Issues

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Definitions and Concepts

• A data warehouse (DW) is a pool of data produced
  to support decision making it is also a
  repository of current and historical data of
  potential interest to managers throughout the
  organization.
• Data are usually structured to be available in
  form ready for analytical processing activities
  (e.g., online analytical processing OLAP, data
  mining, querying, reporting, other decision
  support applications).
• A data warehouse is a subject-oriented,
  integrated, time-variant, nonvolatile collection
  of data in support of managements
  decision-making process.

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Characteristics of DW

• Subject-oriented. Data are organized by detailed
  subject, such as sales, products, or customers,
  containing only information relevant for decision
  support that allows decision makers to both know
  how their business performs and why.
• A DW differs from typical DBs that they are more
  product-oriented and are tuned to handle
  transactions of DBs. Subject orientation allows a
  more comprehensive view of organization
• Integrated. Data are from different sources and
  must be in a consistent form. DWs must deal with
  naming conflicts and discrepancies among units of
  measure.
• Time variant (time series). A DW usually contains
  historical data (containing multiple time points,
  e.g. daily, weekly, monthly), except in real-time
  systems. They detect trends, deviations,
  long-term relationships for forecasting and
  comparisons, leading to decision making.
• Nonvolatile. Users cannot change data in DWs.
  Obsolete data are discarded, and changes are
  recorded as new data.

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Additional DW Characteristics

• Web-based. DWs are typically web-based
  application.
• Relational/Multidimensional. DWs use either a
  relational structure or multidimensional
  structure.
• Client/server. DWs use client/server architecture
  to provide easy access to end users.
• Real-time. Newer DWs provide real-time or active,
  data access and analysis capabilities.
• Include metadata. DWs contain metadata (data
  about data) about how data organized and how to
  effectively use them.

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Parts of DWs.

• Data Marts. A data mart is a subset of a DW,
  typically consisting of a single subject area
  (e.g. marketing, operations).
• Operational Data Stores (ODS). An ODS provides a
  fairly recent form of customer information file
  (CIF) and is used for short-term decision
  involving mission-critical applications. The
  contents of an ODS are updated through the course
  of business operations.
• Enterprise Data Warehouses (EDW). An EDW is a
  large scale DW that is used across the enterprise
  for decision support. EDW are used to provide
  data for many types of DSS, including customer
  relation management (CRM), supply chain
  management (SCM), business performance management
  (BPM), business activity monitoring (BAM),
  product lifecycle management (PLM), revenue
  management, knowledge management systems (KMS),
  etc.

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DW Process Overview

• Data sources. Data come from various sources
  including legacy systems, external data
  providers, online transaction processing (OLTP),
  enterprise resource planning (ERP) system, Web
  logs, etc.
• Data extractions. Data are extracted using
  custom-written or commercial software (ETL).
• Data loading. Data are loaded into staging area,
  where they are transformed and cleansed.
• Comprehensive database. EDWs support all decision
  analysis by providing relevant summarised and
  detailed information originating from many
  different sources.
• Metadata. Medata are maintained so that they can
  be accessed by IT personnel and users. Metadata
  include software programs about data and rules
  for organizing data summaries that are easy to
  index and search.
• Middleware tools. Middleware tools enable access
  to the data warehouse. Middleware tools include
  SQL, Business Object, applications (data mining,
  OLAP, reporting tools, data visualize tools),
  etc.

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DW Process Overview
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DW Architectures
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DW Architectures
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DW Architectures
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DW Architectures
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DW Architecture
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DW Architectures
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DW Architectures
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DW Architectures
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DW Architectures
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10 Factors for choosing DW architecture

• Information interdependence between
  organizational units
• Upper managements information needs
• Urgency of need for a data warehouse
• Nature of end-user tasks
• Constraints on resources
• Strategic view of the data warehouse prior to
  implementation
• Compatibility with existing systems
• Perceived ability of the in-house IT staff
• Technical issues
• Social/Political factors

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Data Integration

• Data integration comprises three major processes
  that, when correctly implemented, permit data to
  be accessed and made accessible to an array of
  ETL and analysis tools and data warehousing
  environment
• Data access (i.e., the ability to access and
  extract data from any data source),
• Data federation (i.e., the integration of
  business views across multiple data stores), and
• Change capture (i.e., based on the
  indentification, cpature, and delivery of the
  changes made to enterprise data sources).

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Data Integration Techniques

• Enterprise application integration (EAI) provides
  a vehicle for pushing data from source systems
  into the data warehouse.
• EAI involves integrating application
  functionality and is focused on sharing
  functionality (rather than data) across systems,
  thereby enabling flexibility and reuse.
• Traditional EAP focuses on enabling application
  reuse at the programming level, whereas modern
  EAP uses services-oriented architecture (SOA).
• Enterprise information integration (EII) proposes
  real-time data integration from a variety of
  sources, such as relational databases, Web
  services, and multidimensional databases.
• EII tools use predefined metadata to populate
  views that make integrated data appear relational
  to end users. XML seems to be the most
  appropriate tool to define metadata.

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Extraction, Transformation, and Load (ETL)

• ETL is the heart of DW.
• ETL is composed of
• Extraction reading data from one or more
  databases,
• Transformation converting the extracted data
  from its previous form into the form in which it
  needs to be so that it can be placed into a data
  warehouse or simply another database, and
• Load putting the data into datawarehouse.
• Transformation occurs by using rules or lookup
  tables or by combining the data with other data.
• The three database functions are integrated into
  one tool to pull data out of one or more
  databases and place them into another,
  consolidated database or a data warehouse.
• ETL tools transport data between sources and
  targets, document how data elements (e.g.
  metadata) change as the move between source and
  target, exchange metadata with other application
  as needed, and administer all runtime processes
  and operations (e.g., scheduling, error
  management, audit logs, statistics).

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ETL Process
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DW Development Approaches

• The inmon Model The EDW Approach
• The Kimbell Model The Data Mart Approach

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DW Structure Star Schema
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DW Implementation Issues

• The following is a list of major tasks that could
  be performed
• Establishment of service-level agreements and
  data-refresh requirements
• Identification of data sources and their
  governance policies
• Data quality planning
• Data model design
• ETL tool selection
• Relational database software and platform
  selection
• Data transport
• Data conversion
• Reconciliation process
• Purge and archive planning
• End-user support

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DW Implementation Issues

• Some of the best practices for implementing a
  data warehouse
• The project must fit with corporate strategy and
  business objectives.
• There must be complete buy-in to the project by
  executives, managers, and users.
• It is important to manage user expectation about
  the completed project.
• The DW must be built incrementally.
• Build in adaptability.
• The project must be managed by both IT and
  business professionals.
• Develop a business/supplier relationship.
• Only load data that have been cleansed and are of
  a quality understood by the organization.
• Do not overlook training requirements.
• Be politically aware.

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DW Implementation issues

• Risks in implementing DW
• No mission or objective
• Quality of source data unknown
• Skills not in place
• Inadequate budget
• Lack of supporting software
• Source data not understood
• Weak sponsor
• Users not computer literate
• Political problems or turf wars
• Unrealistic user expectations
• Architectural and design risks
• Scope creep and changing requirements
• Vendors out of control
• Multiple platforms
• Key people leaving the project
• Loss of the sponsor
• Too much new technology
• Having to fix an operating system

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DW Implementation Issues

• When developing a DW, the followings are not to
  things
• Starting with the wrong sponsorship chain
• Setting expectations that you cannot meet and
  frustrating executives at the moment of truth
• Engaging in politically naïve behavior
• Loading the warehouse with information just
  because it is available
• Believing that data warehousing database design
  is the same as transactional database design
• Choosing a data warehouse manager who is
  technology oriented rather than user oriented.
• Focusing on traditional internal record-oriented
  data and ignoring the value of external data and
  of text, images, and, perhaps, sound and video.
• Delivering data with overlapping and confusing
  definitions.
• Believing promises of performance, capacity, and
  scalability.
• Believing that your problems are over when the
  data warehouse is up and running.
• Focusing on ad hoc data mining and periodic
  reporting instead of alerts.

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Massive DW and Scalability

• Scalability is the ability of a system to
  maintain its level of performance when the size
  of input grows.
• When the size of input in DWs grows, DWs must be
  able to handle the number of users and the
  complexity of their queries.
• Wal-Mart use hundreds of terabytes of data to
  study sales trends and other tasks.
• US Defence use 5 petabyte DW to maintain the
  medical records of its 9 million personnel.
• Good scalability of DW means that queries and
  other data access functions will grow (ideally)
  linearly with the size of the DW.

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Real-Time Data Warehousing

• Traditionally, DWs work mainly on historical data
  to support strategic and tactical decision
  making.
• For many business, making fast and consistent
  decisions across the enterprise requires
  real-time data warehousing. Decision support has
  become operational.
• Real-time data warehouse (RDW), also known as
  active data warehouse (ADW), is the process of
  loading and providing data via the data warehouse
  as they become available.
• At a basic level, ADW simply report what
  happened. At the next level, some analysis
  occurs. As the system evolves, it provides
  prediction capabilities, which lead to the next
  level of operationalization.
• At its highest evolution, ADW is capable of
  making events happen (e.g. activities such as
  creating sales and marketing compaigns or
  idnetifying and exploiting opportunities).

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Traditional vs Active DW

• Strategic, tactic and Operational decisions
• Results measured with operations
• Only comprehensive detailed data available within
  minutes is acceptable
• High number (1000 or more) of users accessing and
  querying the system simultaneously
• Flexible ad hoc reporting, as well as
  machine-assisted modeling (e.g., data mining) to
  discover new hypotheses and relationships
• Operational staffs, call centers, external users

• Strategic decision only
• Results sometimes hard to measure
• Daily, weekly, monthly data currency acceptable
  summaries often appropriate
• Moderate user concurrency
• Highly restrictive reporting used to confirm or
  check existing processes and patterns often uses
  predeveloped summary tables or data marts
• Power users, knowledge workers, internal users

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DW Administration and Security Issues

• Establishing effective corporate security
  policies and procedures. An effective security
  policy should start at the top, with executive
  management, and should be communicated to all
  individuals within the organization.
• Implementing logical security procedures and
  techniques to restrict access. This includes user
  authentication, access controls, and encryption
  technology.
• Limiting physical access to the data center
  environment.
• Establishing an effective internal control review
  process with an emphasis on security and privacy.