Decision Support Systems

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Decision Support Systems
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Decision Support Trends

• The emerging class of applications focuses on
• Personalized decision support
• Modeling
• Information retrieval
• Data warehousing
• What-if scenarios
• Data visualization

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Business Intelligence

• Business intelligence refers to applications and
  technologies that are used to gather, provide
  access to, and analyze data and information about
  company operations. Business intelligence systems
  can help companies have a more comprehensive
  knowledge of the factors affecting their
  business, such as on sales, production, internal
  operations, and they can help companies to make
  better business decisions.

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Business Intelligence Applications
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DSS Components

• Data management function
• Data warehouse
• Data mart
• Model management function
• Analytical models
• Statistical model, management science model
• User interface
• Data visualization
• Web-based dashboards

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Data Warehouse

• A subject-oriented, integrated, time-variant,
  non-updatable collection of data used in support
  of management decision-making processes
• Subject-oriented e.g. customers, employees,
  locations, products, time periods, etc.
• Dimensions for data analysis
• Integrated Consistent naming conventions,
  formats, encoding structures from multiple data
  sources
• Time-variant Can study trends and changes
• Nonupdatable Read-only, periodically refreshed
• Data Mart
• A data warehouse that is limited in scope

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Need for Data Warehousing

• Integrated, company-wide view of high-quality
  information.
• Separation of operational and informational
  systems and data.

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The ETL Process
L
One, company-wide warehouse
T
E
Periodic extraction ? data is not completely
current in warehouse
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The ETL Process

• Capture/Extract
• Scrub or data cleansing
• Transform
• Load and Index

ETL Extract, transform, and load
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Data Warehouse Design- Star Schema -

• Fact table
• contain detailed business data
• Dimension tables
• contain descriptions about the subjects of the
  business such as customers, employees, locations,
  products, time periods, etc.

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Star schema example
Fact table provides statistics for sales broken
down by product, period and store dimensions
Dimension tables contain descriptions about the
subjects of the business
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Star schema with sample data
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ExampleOrder Processing System
City
OID
ODate
CID
Cname
Rating
SalesPerson
Order
Has
M
Customer
1
M
Qty
Has
M
Product
Price
PID
Pname
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Star Schema
Location Dimension LocationCode State City
CustomerRating Dimension Rating Description
FactTable LocationCode PeriodCode Rating PID Qty A
mount
Can group by State, City
Period Dimension PeriodCode Year Quarter
Product Category CategoryID Description
Product Dimension PID Pname CategoryID
(Snowflake model)
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From SalesDB to MyDataWarehouse

• Extract data from SalesDB
• Create query to get the data
• Download to MyDataWareHouse
• File/Import/Save as Table
• Data scrub/cleasing,and transform
• Transform City to Location
• Transform Odate to Period
• Load data to FactTable

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On-Line Analytical Processing (OLAP) Tools

• The use of a set of graphical tools that provides
  users with multidimensional views of their data
  and allows them to analyze the data using simple
  windowing techniques
• Relational OLAP (ROLAP)
• Traditional relational representation
• Multidimensional OLAP (MOLAP)
• Cube structure
• OLAP Operations
• Cube slicingcome up with 2-D view of data
• Drill-downgoing from summary to more detailed
  views
• Roll-up the opposite direction of drill-down
• Reaggregation rearrange the order of dimensions

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Slicing a data cube
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Summary report
Example of drill-down
Starting with summary data, users can obtain
details for particular cells
Drill-down with color added
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Access Pivot FormDrill Down
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Data Mining

• Knowledge discovery using a blend of statistical,
  Artificial Intelligence, and computer graphics
  techniques
• Goals
• Explain observed events or conditions
• Explore data for new or unexpected relationships
• Techniques
• Statistical regression
• Decision tree induction
• Clustering discover subgroups
• Affinity discover things with strong mutual
  relationships
• Sequence association discover cycles of evens
  and behaviors
• Rule discovery search for patterns and
  correlations
• Neural nets predictive models

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Typical Data Mining Applications

• Profiling populations
• High-value customers, credit risks, credit card
  fraud
• Analysis of business trends
• Target marketing
• Campaign effectiveness
• Product affinity
• Identifying products that are purchased
  concurrently
• Customer retention
• Up-selling
• Identifying new products and services to sell to
  a customer based on critical events

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Data Visualization

• Representing data in graphical/multimedia formats
  for analysis.
• Example
• http//www.corda.com/lpage/data_visualization_tool
  .html
• Click examples
• Map or demo

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Geological Information SystemGIS

• GIS is a computer-based tool for mapping and
  analyzing things that exist and events that
  happen on earth. GIS technology integrates common
  database operations such as query and statistical
  analysis with the unique visualization and
  geographic analysis benefits offered by maps.
• Typical application
• Site selection

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Data of GIS

• Geodatabase
• A geodatabase is a database that is in some way
  referenced to locations on the earth.
• Longitude, latitude
• Attribute data
• Attribute data generally defined as additional
  information, which can then be tied to spatial
  data.

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Chart
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Charting Decision Rules

• An Internet Service Provider charges customers
  based on hours used
• First 10 hours 15
• Each of the next 20 hours 2 per hour
• Hours over 30 hours 1 per hour

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Comparing Decision Rules

• Plan 2
• First 20 hours 20
• Hours over 20 1.5
• Plan 3
• 35 unlimited access.

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Charting Functions

• Demand function
• P 150 6Q2
• Supply function
• P 10 Q2 2Q
• Note
• Positive area
• Value axis maximum/minimum value
• Format Value Axis

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Frequency Distribution

• FREQUENCY(data_array,bins_array)
• Calculates how often values occur within a range
  of values, and then returns a vertical array of
  numbers. For example, use FREQUENCY to count the
  number of test scores that fall within ranges of
  scores. Because FREQUENCY returns an array, it
  must be entered as an array formula.
• Note  The formula in the example must be entered
  as an array formula. After copying the example to
  a blank worksheet, select the range A12A15,
  press F2, and then press CTRLSHIFTENTER.

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Example
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Chart Linear Regression Line

• Example The amount of additive x and the
  reduction in nitrogen oxides y are measured in
  some suitable units. Seven different levels of x
  are included in the experiment and some of these
  levels are repeated for more than one car. The
  data is given in the table. A glance at the data
  shows that y generally increase with x.

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Excel Regression Functions

• Regression line y mx b
• LINEST(known_y's,known_x's)
• An array function that calculates m and b
• TREND(known_y's,known_x's,new_x's)
• Returns values along a linear trend.
• FORECAST(x,known_y's,known_x's)
• Calculates, or predicts, a future value by using
  existing values.

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Chart Regression Line

• Calculate the data for the regression line
• LinEst or Trend
• Create a scatter chart to show the original data
  and the regression data.
• Change the regression data to a line
• Select the regression data
• Format/Selected data series
• Choose the line style

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Scenario

• A scenario is an assumption about input
  variables.
• Excels Scenarios is a what-if-analysis tool. A
  scenario is a set of values that Microsoft Excel
  saves and can substitute automatically in your
  worksheet.
• You can use scenarios to forecast the outcome of
  a worksheet model. You can create and save
  different groups of values on a worksheet and
  then switch to any of these new scenarios to view
  different results.

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Creating a Scenario

• Tools/Scenarios
• Add scenario
• Changing cells
• Resulting cells
• Demo benefit.xls