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MIS 430 - Data Mining > Quiz 4 > Flashcards

Quiz 4 Flashcards

1
Q

business applications

A
  • Function orientation
  • Process-centric
  • Goal: Efficient execution of business operations while maintaining data integrity
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2
Q

business applications examples

A
  • Order processing
  • Payroll processing
  • Maintaining inventory
  • Accounts receivable
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3
Q

operational databases

A
  • support day-to-day business activities
  • optimized for transaction processing
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4
Q

transaction

A
  • well-defined process that occurs in exactly the same way and is routine (i.e. repeats)
  • produce one set of data (i.e. record(s))
  • characterize one single operation – i.e. is indivisible (Atomic)
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5
Q

transaction processing

A
  • chronological processing of transactions
  • aims to data changes (Insert, Update, Delete) immediately upon completion of transaction
  • aims to maintain data integrity
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6
Q

business decision making:

What kinds of decisions are made?

A
  • How to boost sales levels
    • Revenues
    • Sales Price
    • Number of Customers
  • How to reduce costs
  • Effect of marketing campaigns on product sales
  • etc.
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7
Q

business decision making:

What level are the decisions being made?

A
  • Different individual products
    • (ipod, ipad, ipad-2, samsung-tv1, samsung tv-2)
  • Different groups of products
    • (tablets, apple products, samsung products, televisions)
  • Different levels of groupings
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8
Q

business decision making:

What type of information is needed for such decisions?

A
  • Individual sale transaction is insufficient
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9
Q

business decision making:

Decision making in support of Business Objectives

A

Example business objectives:

  • Over next year, increase customer based by 20%
  • Improve customer retention by 25%
  • Increase sales by 15%
  • Increase profit by 20%
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10
Q

business decision making:

Information Requirements

A
  • More than business function needs Enterprise-wide Integrated View
  • Timely information delivery
  • Data consistency
  • Historical Information
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11
Q

business decision making:

What would be the characteristics of the queries?

A
  • Large number of records
  • Historical
  • Information across business functions and domains
    • (Sales, Marketing, Financials, etc.)
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12
Q

information hierarchy

A

[insert graphic]

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13
Q

From Silo to Enterprise Wide View graphic

A
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14
Q

Data Warehouse Architecture graphic

A
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15
Q

How is Data Warehouse different from Operational Database?

A
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16
Q

ODB vs. DW:

Data Content

A

ODB:

  • current value

DW:

  • archived
  • historic
  • summarized
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17
Q

ODB vs. DW:

Data Structure

A

ODB:

  • optimized for transaction processing

DW:

  • optimized for complex querying
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18
Q

ODB vs. DW:

Access Frequency

A

ODB:

  • high

DW:

  • low
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19
Q

ODB vs. DW:

Access Type

A

ODB:

  • read
  • update
  • delete

DW:

  • read only
20
Q

ODB vs. DW:

Usage

A

ODB:

  • predictable
  • routine

DW:

  • ad-hoc
  • random
  • context dependent on decision-making
21
Q

ODB vs. DW:

Response Time

A

ODB:

  • sub-seconds

DW:

  • several seconds to minutes
22
Q

ODB vs. DW:

Users

A

ODB:

  • many

DW:

  • few
23
Q

What is ETL?

A
  1. Extraction
  2. Transformation
  3. Loading
24
Q

ETL sources of data

A
  • Production Data:
    • data from operational systems
  • Internal Data:
    • data gathered and utilized typical systems
    • example: spreadsheets, departmental databases, etc.
  • External Data:
    • data purchased from outside sources
  • Archived Data:
    • old data from within the company
    • (typically one-time addition)
25
Q

data staging

A
  • Characterized by ETL
  • Extraction:
    • Data from diverse systems and data models
  • Transformation:
    • Cleaning
    • Standardization:
      • Data type
      • Measurement
      • Interpretation (Synonym vs. Homonym)
    • Summarization
  • Loading
26
Q

Defining features of data warehouse

A
  • Subject Orientation
    • (vs. Functional Orientation of Operational DB)
  • Enterprise-wide Integration
  • Time-Variant Data / Historical
  • Non-volatile / read-only
  • Granularity
27
Q

Defining Features of DW:

Subject-Orientation

A
28
Q

Defining Features of DW:

Time-Variant

A
  • Operational database store current values.
    • Examples:
      • What is the balance owed by customer?
      • What is the list price of the car?
  • Data warehouse helps analyze changes in outcomes because of changes in dimensions.
    • Example:
      • to help answer questions like what was the cause of drop in sales.
29
Q

Defining Features of DW:

Time-Variant allows…

A
  • Historical analysis
  • Analysis of patterns for predictive use
  • Impact of decisions in different operational areas
    • e.g., marketing on sales, customer retention, profits.
  • Measure outcome influence
    • e.g., impact of decision to drop prices on revenues, number of customers, …
30
Q

Defining Features of DW:

Non-Volatile Data

A
  • Data is moved from operational into data warehouse at intervals of time
  • Captured snapshot does not change
    • For example:
      • balance owed by customer at the time is captured
      • any change in balance is loaded next extraction.
    • (time variant and nonvolatile)
31
Q

Defining Features of DW:

Data Granularity

A
  • defines level of detail
  • multiple levels of detail are usually present
  • example:
    • grocery store may store data in warehouse on:
      • hourly intervals
      • daily intervals
      • weekly …
    • customer behavior may be tracked on:
      • Individual customer
      • Zip code
      • Customer type, etc.
32
Q

metadata

A
  • metadata is basically Data Dictionary
  • often defined as “data about data”
33
Q

types of metadata

A
  • Operational
  • ETL
  • End-User
34
Q

operational metadata

A
  • information about source systems
  • where the data is coming from
  • details of fields used, etc.
35
Q

ETL metadata

A
  • extraction methods
  • business rules for transformation
  • when was data last loaded
  • percentage errors, …
36
Q

end-user metadata

A
  • navigational map for end-users
  • what information is located where
  • what does it mean
  • what is the measurement unit, etc.
37
Q

Operational DB vs. Data Warehouse

A
38
Q

Defining Business Requirements:

Identifying Focus of Decision Support

A
  • Overall Goal:
    • What information is needed for decision making?
  • Identifying the Subject:
    • What is the outcome being focused on?
    • Example: Sales, Inventory
  • Analyzing decision making:
    • What about the outcome is being analyzed? (measures)
    • Measures of success/failure of strategy (decision)
39
Q

examples of measures

A

Decisions involving sales may focus on:

  • market share
  • number of unique customers
  • number of customer visits
  • average sales to each customer
  • total sales
  • profits
  • profit margins, …
40
Q

Defining Business Requirements:

Identifying Business Dimensions of Decisions

A
  • What are the components of the decision?
    • Example:
      • What can the decision maker do to accomplish the outcomes identified?
  • Identify various types decisions that are made to influence the outcome
  • Identify the Business Dimensions of these decisions
41
Q

example business dimensions

A

To increase total sales (subject), decision may be taken to launch a promotion campaign offering discounts on…

  • PRODUCTS,
  • at different STORES,
  • on certain DATES.

Note: besides the decision, the outcome measures are also analyzed on the dimensions. Monthly Sales for Product at different stores

42
Q

Defining Business Requirements:

Hierarchies in Business Dimensions

A

Decisions are defined by:

  • BUSINESS DIMENSIONS
  • LEVEL
  • CATEGORY
43
Q

level

A
  • level corresponds to different levels of hierarchy within a dimension
  • example: Day—>Month—>Quarter—>Year
  • the finest level of hierarchy identifies the GRANULARITY of the data
  • both decisions taken and outcomes measured can be analyzed up to this level of detail
44
Q

category

A
  • categories are dimensions characterized by groupings.
  • for example: ethnicity, college education, etc.
45
Q

multi-dimensional data

A

MIS 430 - Data Mining (5 decks)

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