Business Intelligence

Topics

• Chart• Online Analytical Process, OLAP

– Excel’s Pivot table– Data visualization with dashboard

• Data warehousing• Data Mining• Scenario Management

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

Chart Stock Market Data

• Download Dow Jones Historical Data– Yahoo/Finance/Dow Jones/Historical Data

• To chart:– Insert/Chart/Other Charts/Stock chart

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

• 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

Excel’s Pivot Table

• Insert/Pivot Table or Pivot Chart– Drill down, rollup and reaggregation– Filter

• Pivot Chart– Filter– Drilldown, rollup, reaggregation

• Import queries from Access to perform analysis.– Sales related to: Customer’s location, Rating and

Products

Data Visualization

• Representing data in graphical/multimedia formats for analysis.– Web-based “dashboards”

• http://www.dundas.com/– Dashboard Samples

Data Warehouse

• Data warehouse is a repository of an organization's electronically stored data.

• A data warehouse houses a standardized, consistent, clean and integrated form of data that:– sourced from various operational systems in use

in the organization, – structured in a way to specifically address the

reporting and analytic requirements.

Example:Transaction Database

Customer Order

Product

Has

Has

1 M

M

M

CID Cname City OID ODate

PIDPname

Price

RatingSalesPerson

Qty

Analyze Sales DataDetailed Business Data

• Total sales:– by product:

• Qty*Price of each detail line• Sum (Qty*Price)• Detailed business data: qty*price

• Total quantity sold:– By product:

• Sum(Qty)• Detailed business data: Qty

Dimensions for Data Analysis:Factors relevant to the business data

• Analyze sales by Product• Analyze sales related to Customer:

– Location: Sales by City– Customer type: Sales by Rating

• Analyze sales related to Time:– Quarterly, monthly, yearly Sales

• Analyze sales related to Employee:– Sales by SalesPerson

Data Warehouse Design- Star Schema -

• Dimension tables– contain descriptions about the subjects of the

business such as customers, employees, locations, products, time periods, etc.

• Fact table– contain detailed business data with links to

dimension tables.

Star Schema

FactTableLocationCodePeriodCode

RatingPIDQty

Amount

LocationDimension

LocationCodeStateCity

CustomerRatingDimension

RatingDescription

ProductDimension

PIDPname

Category

PeriodDimensionPeriodCode

YearQuarter

Can group by State, City

Define Location Dimension

• Location:– In the transaction database: City– In the data warehouse we define Location to be

State, City• San Francisco -> California, San Francisco• Los Angeles -> California, Los Angeles

– Define Location Code: • California, San Francisco -> L1• California, Los Angeles -> L2

Define Period Dimension

• Period:– In the transaction database: Odate– In the data warehouse we define Period to be:

Year, Quarter• Odate: 11/2/2003 -> 2003, 4• Odate: 2/28/2003 -> 2003, 1

– Define Period Code:• 2003, 4 -> 20034• 2003, 1 -> 20031

The ETL Process

E

T

LOne, company-wide warehouse

Periodic extraction data is not completely current in warehouse

The ETL Process

• Capture/Extract• Transform

– Scrub(data cleansing),derive– Example:

• City -> LocationCode, State, City• OrderDate -> PeriodCode, Year, Quarter

• Load and Index

ETL = Extract, transform, and load

Performing Analysis

• Analyze sales:– by Location– By Location and Customer Type– By Location and Period– By Period and Product

• Pivot Table:– Drill down, roll up, reaggregation

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

Typical Data Mining 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

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• Up-selling

– Identifying new products and services to sell to a customer based on critical events

Affinity Analysis:Market Basket Analysis

• Market Basket Analysis is a modeling technique based upon the theory that if you buy a certain group of items, you are more (or less) likely to buy another group of items.

• The set of items a customer buys is referred to as an itemset, and market basket analysis seeks to find relationships between purchases.

• Typically the relationship will be in the form of a rule: Example:– IF {beer, no bar meal} THEN {chips}.

Basket Analysis and Cross- Selling

• For instance, customers are very likely to purchase shampoo and conditioner together, so a retailer would not put both items on promotion at the same time. The promotion of one would likely drive sales of the other.

• A widely used example of cross selling on the internet with market basket analysis is Amazon.com's use of suggestions of the type:– "Customers who bought book A also bought book B", e.g.

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.

• Data/What If analysis/Scenario

Creating a Scenario

– Add scenario• Changing cells

– Scenario Summary• Resulting cells

• Demo: benefit782.xls