Upload
elijah-stevenson
View
234
Download
0
Tags:
Embed Size (px)
Citation preview
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