Data Management

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Data Management

• MBA 820
• Information Technology
• for Decision Making

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Case Sears Data Warehouses

• Problem
• Sears was caught by surprise in the 1980s as
  shoppers defected to specialty stores and
  discount mass merchandisers.
• Solution
• Sears constructed a single sales information data
  warehouse, replacing 18 old databases which were
  packed with redundant, conflicting obsolete
  data.
• By 2001, Sears made the following Web
  initiatives
• e-Commerce home improvement center
• B2B supply exchange for the retail industry
• Online Toy catalog and much more

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Case Sears Data Warehouses

• Results
• The ability to monitor sales by item per store
  enables Sears to create a sharp local market
  focus.
• Data monitoring of Web-based sales helps Sears
  marketing and Web advertisement plans.
• Response time to queries has dropped from days to
  minutes.
• The data warehouse offers Sears employees a tool
  for making better decisions.
• Sears retailing profits have climbed more than
  20 annually since the data warehouse was
  implemented.

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Difficulties of Managing Data

• The amount of data increases exponentially.
• Data are scattered throughout organizations and
  are collected by many individuals using several
  methods and devices.
• Only small portions of an organizations data are
  relevant for any specific decision.
• An ever-increasing amount of external data needs
  to be considered in making organizational
  decisions.
• Data are frequently stored in several servers and
  locations in an organization.

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Difficulties of Managing Data (cont.)

• Raw data may be stored in different computing
  systems, databases, formats, and human and
  computer languages.
• Legal requirements relating to data differ among
  countries and change frequently.
• Selecting data management tools can be a major
  problem because of the huge number of products
  available.
• Data security, quality, and integrity are
  critical yet are easily jeopardized.

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Traditional File Environment

• Data Hierarchy
• Bits, Bytes, Field, Records, Files, and Databases
• Entities, Attributes, and Key Fields
• Traditional Files - Flat File Structure

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Problems with Traditional Files

• Data Redundancy Inconsistency
• Program-Data Dependence
• Data Integrity
• Poor Security
• Lack of Data Sharing (isolation)

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Database Management Sys

• Database A collection of data organized to serve
  many applications efficiently by centralizing
  data and minimizing data redundancy
• DBMS Components
• Data Definition Language (DDL)
• Data Manipulation Language (DML)
• Data Dictionary

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Advantages of DBMS

• Reduced complexity
• Improved Security
• Diminished Data Redundancy
• Improved Data Integrity
• Reduced Program/Data Dependence
• Reduced Development/Maintenance Costs
• Supports End-User Queries

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Disadvantages of DBMS

• Can be Large Complex
• Overhead (time, space)
• Single Point of Failure

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Database Designs

• Database Models
• Hierarchical Data Models
• Network Data Models
• Relational Data Models
• Creating a DBMS
• Logical design is difficult
• Requires user participation

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Normalization

• Reduce Relational Database to most streamlined
  form for
• minimal data redundancy,
• maximum data integrity,
• and best processing performance

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Data Warehousing

• Establish a data repository that makes
  operational data accessible in a form readily
  acceptable for analytical processing activities
  (e.g. decision support, EIS)
• Benefits
• The ability to reach data quickly, as they are
  located in one place.
• The ability to reach data easily, frequently by
  end-users themselves, using Web browsers.

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Characteristics of Data Warehouses

• Organization. Data are organized by detailed
  subjects.
• Consistency. Data in different operational
  databases may be encoded differently. In the
  warehouse they will be coded in a consistent
  manner.
• Time variant. The data are kept for 5 to 10 years
  so they can be used for trends, forecasting, and
  comparisons over time.
• Non-volatile. Once entered into the warehouse,
  data are not updated.
• Relational. The data warehouse uses a relational
  structure.
• Client/server. The data warehouse uses the
  client/server to provide the end user an easy
  access to its data.

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Data Warehouse Suitability

• Large amounts of data need to be accessed by
  end-users.
• The operational data are stored in different
  systems.
• An information-based approach to management is in
  use.
• There is a large, diverse customer base.
• The same data are represented differently in
  different systems.
• Data are stored in highly technical formats that
  are difficult to decipher.
• Extensive end-user computing is performed.

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Data Marts

• Data Marts are an alternative used by many other
  firms is creation of a lower cost, scaled-down
  version of a data warehouse. They refer to small
  warehouses designed for a strategic business unit
  (SBU) or a department.
• Types of Data Marts
• 1) Replicated (dependent) Data Marts. In such
  cases one can replicate functional subsets of the
  data warehouse in smaller databases.
• 2) Stand-Alone Data Marts. A company can have
  one or more independent data marts without having
  a data warehouse.

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Data Mining

• Data mining - searching for valuable business
  information in a large database similar mining a
  mountain for valuable ore.
• Data mining technology can generate new business
  opportunities by providing these capabilities
• Automated prediction of trends and behaviors.
  Data mining automates the process of finding
  predictive information in large databases.
• Automated discovery of previously unknown
  patterns. Data mining tools identify previously
  hidden patterns in one step.

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Applications of Data Mining
Data Mining is currently being used in the
following areas

• Insurance
• Policework
• Government Defense
• Airlines
• Health care
• Broadcasting
• Marketing

• Retailing Sales
• Banking
• Manufacturing Production
• Brokerage Securities trading
• Computer hardware software

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Implementation Examples

• Alamo Rent-a-Car discovered that German tourists
  liked bigger cars. So now, when Alamo advertises
  its rental business in Germany, the ads include
  information about its larger models.
• Au Bon Pain Company discovered that they were not
  selling as much cream cheese as planned. When
  they analyzed point-of-sale data, they found that
  customers preferred small, one-serving packaging.
  
• ATT and MCI sift through terabytes of customer
  phone data to fine-tune marketing campaigns and
  determine new discount calling plans.

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CASE Walmart

• The systems house data on point of sale,
  inventory, products in transit, market
  statistics, customer demographics, finance,
  product returns, and supplier performance.
• The data are used for three broad areas of
  decision support
• analyzing trends
• managing inventory
• understanding customers
• The data warehouse is available over an extranet
  to store managers and suppliers.
• In 2001, 5,000 users made over 35,000 database
  queries each day.

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Management Challenges

• Organizational Obstacles to DB Environment
• Organizational change
• Legacy data problem
• Data Security Ethics
• Privacy
• Cost/Benefit Considerations
• Short-term Costs vs. Long-term Benefits
• How can we insure long-term benefits?