slide1 n.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
Data Warehouse DSS Business Intelligence Minder.Chen@CSUCI.EDU PowerPoint Presentation
Download Presentation
Data Warehouse DSS Business Intelligence Minder.Chen@CSUCI.EDU

Loading in 2 Seconds...

play fullscreen
1 / 40

Data Warehouse DSS Business Intelligence Minder.Chen@CSUCI.EDU - PowerPoint PPT Presentation


  • 217 Views
  • Uploaded on

Data Warehouse DSS Business Intelligence Minder.Chen@CSUCI.EDU. BI Evolution. Source: META Group Inc. History. Legacy. Current. 2005+. MIS Reports. Decision Support Systems. Business Intelligence. Business Performance Management. Hand coded. Report writers. OLAP.

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Data Warehouse DSS Business Intelligence Minder.Chen@CSUCI.EDU' - gwydion


Download Now An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
slide1

Data Warehouse

DSS

Business Intelligence

Minder.Chen@CSUCI.EDU

bi evolution
BI Evolution

Source: META Group Inc.

History

Legacy

Current

2005+

MIS

Reports

Decision

Support

Systems

Business

Intelligence

Business

Performance

Management

  • Hand coded
  • Report writers
  • OLAP
  • Dashboard/mining
  • Single system data
  • Joined operating data
  • DW
  • Enterprise portals
  • Summary metrics
  • Statistical metrics
  • Predictive metrics
  • Recommendations
  • Extreme latency
  • Extreme cost
  • Extreme ‘infoglut’
  • Extreme integration

Moving beyond one-way info delivery to true BPM

bi questions
BI Questions
  • What happened?
    • What were our total sales this month?
  • What’s happening?
    • Are our sales going up or down, trend analysis
  • Why?
    • Why have sales gone down?
  • What will happen?
    • Forecasting & “What If” Analysis
  • What do I want to happen?
    • Planning & Targets

Source: Bill Baker, Microsoft

slide4
BI

“The key in business is to know something that

nobody else knows.” -- Aristotle Onassis

PHOTO: HULTON-DEUTSCH COLL

Business Intelligence (BI) is the process of gathering meaningful information to answer questions and identify significant trends or patterns, giving key stakeholders the ability to make better business decisions.

“To understand is to perceive patterns.”

— Sir Isaiah Berlin

"The manager asks how and when,

the leader asks what and why."

— “On Becoming a Leader” by Warren Bennis

bi definition
BI Definition
  • Business intelligence provides the ability to transform data into usable, actionable information for business purposes. BI requires:
    • Collections of quality data and metadata important to the business
    • The application of analytic tools, techniques, and processes
    • The knowledge and skills to use business analysis to identify/create business information
    • The organizational skills and motivation to develop a BI program and apply the results back into the business
business intelligence
Business Intelligence

Increasing potential

to support

business decisions (MIS)

End User

Making

Decisions

Business

Analyst

Data Presentation

Visualization Techniques

Data

Analyst

Data Mining

Information Discovery

Data Exploration

OLAP, MDA,

Statistical Analysis, Querying and Reporting

Data Warehouses / Data Marts

DBA

Data Sources

(Paper, Files, Information Providers, Database Systems, OLTP)

inmon s definition of data warehouse data view
Inmon's Definition of Data Warehouse – Data View
  • A warehouse is a
    • subject-oriented,
    • integrated,
    • time-variant and
    • non-volatile

collection of data in support of management's decision making process.

    • Bill Inmon in 1990

Source: http://www.intranetjournal.com/features/datawarehousing.html

inmon s definition explain
Inmon's Definition Explain
  • Subject-oriented: They are organized around major subjects such as customer, supplier, product, and sales. Data warehouses focus on modeling and analysis to support planning and management decisions v.s. operations and transaction processing.
  • Integrated: Data warehouses involve an integration of sources such as relational databases, flat files, and on-line transaction records. Processes such as data cleansing and data scrubbing achieve data consistency in naming conventions, encoding structures, and attribute measures.
  • Time-variant: Data contained in the warehouse provide information from an historical perspective.
  • Nonvolatile: Data contained in the warehouse are physically separate from data present in the operational environment.
the data warehouse process
The Data Warehouse Process

Data Marts and cubes

Source

Systems

Clients

Data

Warehouse

Query Tools

Reporting

Analysis

Data Mining

1

2

3

4

Design the Populate Create Query Data Warehouse Data Warehouse OLAP Cubes Data

bi architecture
BI Architecture

Source: http://www.rpi.edu/datawarehouse/docs/DW-Architecture.pdf

oltp versus business intelligence who asks what
OLTP Versus Business Intelligence: Who asks what?

OLTP Questions

When did that order ship?

How many units are in inventory?

Does this customer haveunpaid bills?

Are any of customer X’s line items on backorder?

Analysis Questions

What factors affect order processing time?

How did each product line (or product) contribute to profit last quarter?

Which products have the lowest Gross Margin?

What is the value of items on backorder, and is it trending up or downover time?

oltp versus olap
OLTP Versus OLAP

OLTP Questions

When did that order ship?

How many units are in inventory?

Does this customer haveunpaid bills?

Are any of customer X’s line items on backorder?

OLAP Questions

What factors affect order processing time?

How did each product line (or product) contribute to profit last quarter?

Which products have the lowest Gross Margin?

What is the value of items on backorder, and is it trending up or down over time?

dimensional design process
Dimensional Design Process
  • Select the business process to model
  • Declare the grain of the business process/data in the fact table
  • Choose the dimensions that apply to each fact table row
  • Identify the numeric facts that will populate each fact table row

Business

Requirements

Data

Realities

star schema
Star Schema

Source: Moody and Kortink, "From ER Models to Dimensional Models: Bridging the Gap between OLTP and OLAP Design, Part I," Business Intelligence Journal, Summer 2003, pp. 7-24.

identifying measures and dimensions
Identifying Measures and Dimensions

Performance Measures for KPI

Performance Drivers

Measures

Dimensions

  • The attribute varies
  • continuously:
  • Balance
  • Unit Sold
  • Cost
  • Sales
  • The attribute is perceived as
  • a constant or discrete value:
  • Description
  • Location
  • Color
  • Size
facts table
Facts Table

Measurements of business events.

Dimensions

Measures

The Fact Table contains keys and units of measure

fact tables
Fact Tables

Fact tables have the following characteristics:

  • Contain numeric measures (metric) of the business
  • May contain summarized (aggregated) data
  • May contain date-stamped data
  • Are typically additive
  • Have key value that is typically a concatenated key composed of the primary keys of the dimensions
  • Joined to dimension tables through foreign keys that reference primary keys in the dimension tables
store dimension
Store Dimension
  • It is not uncommon to represent multiple hierarchies in a dimension table. Ideally, the attribute names and values should be unique across the multiple hierarchies.
inside a dimension table
Inside a Dimension Table
  • Dimension table key: Uniquely identify each row. Use surrogate key (integer).
  • Table is wide: A table may have many attributes (columns).
  • Textual attributes. Descriptive attributes in string format. No numerical values for calculation.
  • Attributes not directly related: E.g., product color and product package size. No transitive dependency.
  • Not normalized (star schemar).
  • Drilling down and rolling up along a dimension.
  • One or more hierarchy within a dimension.
  • Fewer number of records.
product dimension
Product Dimension
  • SKU: Stock Keeping Unit
  • Hierarchy:
    • Department  Category  Subcategory  Brand  Product
operations in multidimensional data model
Operations in Multidimensional Data Model
  • Aggregation (roll-up)
    • dimension reduction: e.g., total sales by city
    • summarization over aggregate hierarchy: e.g., total sales by city and year -> total sales by region and by year
  • Selection (slice) defines a subcube
    • e.g., sales where city = Palo Alto and date = 1/15/96
  • Navigation to detailed data (drill-down)
    • e.g., (sales - expense) by city, top 3% of cities by average income
  • Visualization Operations (e.g., Pivot)
slide31

Drilling down in a data mart is nothing more than adding row headers from the dimension tables. Drilling up is removing row headers.

We can drill down or up on attributes from more than one explicit hierarchy and with attributes that are part of no hierarchy.

avoid null key in the fact table
Avoid Null Key in the Fact Table
  • Include a row in the corresponding dimension table to identify that the dimension is not applicable to the measurent.

11: No Promotion

Sales Fact Table

slide35
ETL

ETL = Extract, Transform, Load

  • Moving data from production systems to DW
  • Checking data integrity
  • Assigning surrogate key values
  • Collecting data from disparate systems
  • Reorganizing data
building the warehouse
Building The Warehouse

Transforming Data

use of data mining
Use of Data Mining
  • Customer profiling
  • Market segmentation
  • Buying pattern affinities
  • Database marketing
  • Credit scoring and risk analysis
olap and data mining address different types of questions
OLAP and Data Mining Address Different Types of Questions

While reporting and OLAP are informative about past facts, only data mining can help you predict the future of your business.

Source: http://www.dmreview.com/editorial/dmreview/print_action.cfm?articleId=2367

slide40

UA’s Existing Data Warehouse

Balanced Scorecard

Improve Stakeholder Value

Financial Perspective

Stakeholder Value

ROCE

Revenue Growth Strategy

Productivity Strategy

Build the Franchise

Increase Customer Value

Improve Cost Structure

Improve Asset Utilization

  • New Revenue Services
  • Profitability
  • Cost per Unit
  • Asset Utilization
  • Customer Retention
  • Customer Profitability
  • Customer Acquisition

Product Leadership

Customer Intimacy

Customer Perspective

Customer Value Proposition

Operational Excellence

Product/Service Attributes

Relationship

Image

Price

Quality

Time

Function

Service

Relations

Brand

  • Customer Satisfaction

“Build the Franchise”

“Increase Customer Value”

“Achieve Operational Excellence”

“Become a

Good Neighbor”

Internal Perspective

(Customer Management Processes)

(Innovation Processes)

(Operations & Logistics Processes)

(Regulatory & Environmental Processes)

Learning & Growth Perspective

A Motivated and Prepared Workforce

Strategic Competencies

Strategic Technologies

Climate for Action