Download Decision Support Systems

Decision Support Systems
Decision Support Trends
• The emerging class of applications
focuses on
– Personalized decision support
– Modeling
– Information retrieval
– Data warehousing
– What-if scenarios
– Data visualization
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.
Business Intelligence
Applications
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”
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
Need for Data Warehousing
• Integrated, company-wide view of high-quality
information.
• Separation of operational and informational systems
and data.
The ETL Process
L
T
One,
companywide
warehouse
E
Periodic extraction  data is not completely current in warehouse
The ETL Process
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•
Capture/Extract
Scrub or data cleansing
Transform
Load and Index
ETL = Extract, transform, and load
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.
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
Star schema with sample data
Example:
Order Processing System
CID
Cname
City
OID
ODate
Rating
SalesPerson
Customer
1
Has
M
Order
M
Qty
Has
M
Product
Price
PID
Pname
Star Schema
Location
Dimension
LocationCode
State
City
Can group by State, City
Product
Category
CategoryID
Description
(Snowflake model)
FactTable
LocationCode
PeriodCode
Rating
PID
Qty
Amount
Product
Dimension
PID
Pname
CategoryID
CustomerRating
Dimension
Rating
Description
Period
Dimension
PeriodCode
Year
Quarter
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
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 slicing–come up with 2-D view of data
– Drill-down–going from summary to more detailed
views
– Roll-up – the opposite direction of drill-down
– Reaggregation – rearrange the order of dimensions
Slicing a data cube
Summary report
Example of drill-down
Starting with summary
data, users can obtain
details for particular
cells
Drill-down with
color added
Access Pivot Form
Drill Down
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
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–
–
–
–
–
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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
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
Data Visualization
• Representing data in
graphical/multimedia formats for
analysis.
• Example:
– http://www.corda.com/lpage/data_visualizatio
n_tool.html
• Click examples
– Map or demo
Geological Information System
GIS
• 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
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.
Chart
Charting Decision Rules
• An Internet Service Provider charges
customers based on hours used:
– First 10 hours
– Each of the next 20 hours
– Hours over 30 hours
$15
$2 per hour
$1 per hour
Comparing Decision Rules
• Plan 2:
– First 20 hours:
– Hours over 20
$20
$1.5
• Plan 3:
– $35 unlimited access.
Charting Functions
• Demand function:
– P = 150 – 6*Q^2
• Supply function:
– P = 10* Q^2 + 2*Q
• Note:
– Positive area
– Value axis maximum/minimum value:
• Format Value Axis
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
A12:A15, press F2, and then press
CTRL+SHIFT+ENTER.
Example
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.
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.
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
Scenario
• A scenario is an assumption about input
variables.
• Excel’s 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.
Creating a Scenario
• Tools/Scenarios
– Add scenario
• Changing cells
• Resulting cells
• Demo: benefit.xls