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

E

T

LOne, company-wide warehouse

Periodic extraction data is not completely current in warehouse

The ETL Process

• 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

Customer Order

Product

Has

Has

1 M

M

M

CID Cname City OID ODate

PIDPname

Price

RatingSalesPerson

Qty

Star Schema

FactTableLocationCodePeriodCode

RatingPIDQty

Amount

LocationDimension

LocationCodeStateCity

CustomerRatingDimension

RatingDescription

ProductDimension

PIDPname

CategoryID

ProductCategory

CategoryIDDescription

PeriodDimensionPeriodCode

YearQuarter

Can group by State, City

(Snowflake model)

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

Example of drill-down

Summary report

Drill-down with color added

Starting with summary data, users can obtain details for particular cells

Access Pivot FormDrill 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– 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 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

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 $15– Each of the next 20 hours $2 per hour– Hours over 30 hours $1 per hour

Comparing Decision Rules

• Plan 2:– First 20 hours: $20– Hours over 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