data warehouse and business intelligence dr. minder chen professor of mis

Data Warehouse and

Business Intelligence

Dr. Minder Chen

Professor of MIS

Martin V. Smith School of Business and Economics

CSU Channel Islands

[email protected]

mailto:[email protected]

DW & BI - 2 © Minder Chen, 2004-2014

BI

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.

“The key in business is to know something that

nobody else knows.”-- Aristotle Onassis

PHOTO: HULTON-DEUTSCH COLL

“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

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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

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Business Valuation Models for BI

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Performance Dashboards for Information Delivery

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Scorecards for Information Delivery

Balanced Scorecard

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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

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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 vs. 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.

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Increasing potentialto supportbusiness decisions (MIS) End User

Business Analyst

DataAnalyst

DBA

MakingDecisions

Data Presentation

Visualization Techniques

Data MiningInformation Discovery

Data ExplorationOLAP, MDA,

Statistical Analysis, Querying and Reporting

Data Warehouses / Data Marts

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

Business Intelligence

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Where is Business Intelligence applied?

• ERP Reporting

• KPI Tracking

• Product Profitability

• Risk Management

• Balanced Scorecard

• Activity Based Costing

• Global Sourcing

• Logistics

• Sales Analysis

• Sales Forecasting

• Segmentation

• Cross-selling

• CRM Analytics

• Campaign Planning

• Customer Profitability

Operational Efficiency Customer Interaction

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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?

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The Data Warehouse/BI Architecture & Process

Data Marts

DataWarehouse

SourceSystems

Clients

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

3 4

Query ToolsReportingAnalysis

Data Mining

211

E T

L

ETL: Extract, Transform, and Load

E T

L E T

L

OLAP Cubes

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Normalized Database for OLTP

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OLTP vs. OLAP

Source: http://datawarehouse4u.info/OLTP-vs-OLAP.html

OLTP System Online Transaction Processing

(Operational System)

OLAP System Online Analytical Processing

(Data Warehouse)

Source of dataOperational data; OLTPs are the original

source of the data.Consolidation data; OLAP data comes from the

various OLTP Databases

Purpose of data

To control and run fundamental business tasks

To help with planning, problem solving, and decision support

What the dataReveals a snapshot of ongoing business

processesMulti-dimensional views of various kinds of business

activitiesInserts and

UpdatesShort and fast inserts and updates

initiated by end usersPeriodic long-running batch jobs refresh the data

QueriesRelatively standardized and simple

queries Returning relatively few recordsOften complex queries involving aggregations

Processing Speed

Typically very fastDepends on the amount of data involved; batch data

refreshes and complex queries may take many hours; query speed can be improved by creating indexes

Space Requirements

Can be relatively small if historical data is archived

Larger due to the existence of aggregation structures and history data; requires more indexes than OLTP

Database Design

Highly normalized with many tablesTypically de-normalized with fewer tables; use of star

and/or snowflake schemas

Backup and Recovery

Backup religiously; operational data is critical to run the business, data loss is likely to entail significant monetary loss

and legal liability

Instead of regular backups, some environments may consider simply reloading the OLTP data as a

recovery method

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Measuring Performance

• Real estate consumer services and analysis firm Trulia reports that Oct. 2013 saw only an 0.6% rise in home asking prices comparing to Sept. 2013.

• However, the average home asking price rose by 11.7% from Oct. 2012 to Oct. 2013.

• The year-over-year figure is the largest jump since the housing bubble popped back in 2007-08.

Source: http://www.thestreet.com/story/12100873/1/home-sellers-price-hikes-coming-unsustainably-fast.html

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compare with last period vs. year-on-year comparison

• A time series is a sequence of data points, measured typically at successive points in time spaced at uniform time intervals. Examples of time series are the daily closing value of the Dow Jones Industrial Average - Wikipedia “Time series”

• The year-over-year data compares a time period (e.g., a month or a quarter) against the same time period last year.

• You can compare a performance indicator with one from last period (quarter, month, week, day)

• One of the advantages of year-over-year comparisons is that it automatically negates the effect of seasonality (e.g., seasonal effect). It is a more effective way of looking at performance.

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Identifying Measures and Dimensions

The attribute (column) variescontinuously: •Unit Sold•Cost•Sales•Balance

The attribute is perceived asa constant or discrete value:

•Name/Description•Location•Color•Size

DimensionsMeasures

Performance Measures for KPI

Performance Drivers

Attribute Type?

What? Why?

Information for Decision Making

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Star Schema

Sales

Customers

Dates

Products

Channels

Promotions

Fact Tablewith

measures

Dimension Table

Dimension Table

Dimension Table

Dimension Table

Dimension Table

Multi-dimensional Data model

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Snowflake Schema

Sales

Customers

Dates

Products

Channels

Promotions

Brands

Star Schema

Fact Table

Dimension Table

Dimension Table

Dimension Table

Dimension Table

Dimension Table

Customer type

Normalized

Source: http://www.diffen.com/difference/Snowflake_Schema_vs_Star_Schema

Normalized

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Designing Data Warehouse: Dimensional Design Process

• Select the business process to model • Declare the grain of the business process/data in the fact

table (The grain represents the most atomic level by which the facts may be defined. The grain of a SALES fact table might be stated as "Sales volume by Day by Product by Store". )

• Identify the numeric facts/meaures that will populate each fact table row

• Choose the dimensions that apply to each fact table row

BusinessRequirements

Data Realities

Ref: http://en.wikipedia.org/wiki/Fact_table

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Select a business process to model

• Not business departments or business functions

• Cross-functional business processes

• Business events

• Examples: – Raw materials purchasing

– Order fulfillment process

– Shipments

– Invoicing

– Inventory

– General ledger

– Insurance claims

– Class enrollment

– Airline ticket sales

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Facts Table

DateID

ProductID

CustomerID

Units

Dollars

DimensionsDimensionsDimensionsDimensions

MeasuresMeasuresMeasuresMeasures

The Fact Table contains keys and units of The Fact Table contains keys and units of measuremeasure

Measurements of business events.

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Fact Tables

Fact tables have the following characteristics:• It contains numeric measures (metric) of the

business.• It may contain summarized (aggregated) data.• It almost always contains date-stamped data.• Measures 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.• Fact tables are narrow (few attributes) but many

records.

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A Dimensional Model for a Grocery Store’s Sales

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Creating Dimensional Model

• Identify fact tables• Translate business measures into fact tables

• Analyze information from source systems for additional measures

• Identify base and derived measures

• Document additivity of measures (e.g., non-additive[price], semi-additive [quantity-on-hand is not additive over time], or additive [quantity])

• Identify dimension tables

• Link fact tables to the dimension tables

• Create views for users

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Transaction Level Order Item Fact Table

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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 schema).

• Drilling down and rolling up along a dimension.

• One or more hierarchy within a dimension.

• Fewer number of records.

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OLAP Solutions

• Data Warehouse

• Data Mart

• Cubes

• Dimensions

• Measures

• CellsGadgets

Gizmos

Thingies

Widgets

Q1 Q2 Q3 Q4

US

EuropeAsia

130 135 140 142

205 390 350 475

175 230 190 250

310 340 410 450

OLAP Server (e.g., Oracle ESSBase & SQL Server’s Analysis Services)

A cube is a collection of data that’s been aggregated to allow queries to return data quickly.

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Hierarchy

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A Hierarchy in the Product Dimension

• SKU: Stock Keeping Unit

• Hierarchy: – Department Category Subcategory Brand Product

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Multidimensional Query Techniques

What?Why?

Why?

Why? Slicing

Dicing

Drillingdown

Product

Time

Geography

Aggregated data

Detail data

Drill d

ow

n Ro

ll u

p

Performance Measures

Performance Drivers

Hie

rarc

hy

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Roll-Up and Drill-Down

Source: http://www.tutorialspoint.com/dwh/dwh_olap.htm

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Slice and Dice

Source: http://www.tutorialspoint.com/dwh/dwh_olap.htm

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A Visual Operation: Pivot (Rotate)

10

47

30

12

Juice

Cola

Milk

CreamNY

LA

SF3/1 3/2 3/3 3/4 Date

Month

Re

gio

n

Product

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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

• Navigation to detailed data (drill-down)

– e.g., (sales - expense) by city, top 3% of cities by average income

• Selection (slice or dice) defines a subcube

– e.g., sales where city = Palo Alto and date = 1/15/96

• Visualization operations (e.g., Pivot)

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Pivot Table in Excel

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Date Dimension of the Retail Sales Model

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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.

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ETLETL = Extract, Transform, Load.

ETL cycle includes

• Build reference data (e.g., currency codes)

• Extract (from sources)

• Validate

• Transform (clean, apply business rules, check for data integrity, create aggregates)

• Stage (load into staging tables, if used)

• Audit reports on compliance with business rules.

• Publish/load (to target tables in the data warehouse)

• Clean up

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Data Quality Issues

• No common time basis

• Different calculation algorithms

• Different levels of extraction

• Different levels of granularity

• Different data field names

• Different data field meanings

• Missing information

• No data correction rules

• No drill-down capability

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CUST #CUST # NAMENAME ADDRESSADDRESS TYPETYPE

90238475

90233479

90233489

90234889

90345672

90328575

90328575

Digital Equipment

Digital

Digital Corp

Digital Consulting

Digital Info Service

Digital Integration

DEC

187 N. PARK St. Salem NH 01458187 N. Pk. St. Salem NH 01458

187 N. Park St Salem NH 01458

187 N. Park Ave. Salem NH 01458

15 Main Street Andover MA 02341PO Box 9 Boston MA 02210

Park Blvd. Boston MA 04106

OEM

OEM

$#%

Comp

Consult

Mail List

SYS INT

No Unique KeyNoise in

Blank FieldsSpellingNo StandardizationAnomalies

How does one correctly identify and consolidate anomalies from millions of records?

The Anomalies Nightmare

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Data Mining & Knowledge Discovery in Database (KDD) Process

Data mining is the analysis step of the "Knowledge Discovery in Databases" process (KDD) involving methods such as artificial intelligence, machine learning, statistics, and database systems.

Data Mining is the practice of searching through large amounts of computerized data to find useful patterns or trends

Source: http://www2.cs.uregina.ca/~dbd/cs831/notes/kdd/1_kdd.html

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Knowledge Discovery• Knowledge discovery in databases is the non-trivial process of

identifying valid, novel, potentially useful, and ultimately understandable patterns in data.Data A set of facts.

PatternAn association, dependence, clusters, etc. among facts (items) in the data set.

ProcessKDD is a multi-step process involving data preparation, pattern searching, knowledge evaluation, and refinement with iteration after modification.

ValidDiscovered patterns should be true on new data with some degree of certainty. Generalize to the future (other data).

Novel Patterns must be novel (should not be previously known).

UsefulActionable; patterns should potentially lead to some useful actions.

Under-standable

The process should lead to human insight. Patterns must be made understandable in order to facilitate a better understanding of the underlying data.

http://www2.cs.uregina.ca/~dbd/cs831/notes/kdd/1_kdd.html

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Cross Industry Standard Process for Data Mining

Source: http://en.wikipedia.org/wiki/Cross_Industry_Standard_Process_for_Data_Mining

Decision

Action

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Data Mining Tasks and Examples• Classification - Customer profiling into predefined

categories via supervised learning using Decision Tree or Neural Network

• Clustering - grouping a set of objects in such a way that objects in the same group (cluster) are more similar to each other than to those in other groups (clusters) Market segmentation , e.g.,

• Summarization - Credit scoring and risk analysis using Bayesian inference. It is considered a Structured prediction technique.

• Association - What is the likelihood that a customer will buy a product next month, if he buys a related item today? (sequence association)

http://www2.cs.uregina.ca/~dbd/cs831/notes/kdd/2_tasks.html

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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.

OLAP Data Mining

What was the response rate to our mailing? What is the profile of people who are likely to respond to future mailings?

How many units of our new product did we sell to our existing customers?

Which existing customers are likely to buy our next new product?

Who were my 10 best customers last year?Which 10 customers offer me the greatest profit potential?

Which customers didn't renew their policies last month?

Which customers are likely to switch to the competition in the next six months?

Which customers defaulted on their loans?Is this customer likely to be a good credit risk?

What were sales by region last quarter?What are expected sales by region next year?

What percentage of the parts we produced yesterday are defective?

What can I do to improve throughput and reduce scrap?

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

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Shopping Basket Analysis

• Which items are purchased in a retail store at the same time?

• Amazon use collaborative filtering that use shopping basket (sales) data to make recommendations when you select an item.

Ref: http://en.wikipedia.org/wiki/Collaborative_filtering

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Issues on Interpreting Modeling Results

• Housing price: Use factors, such as location, number of bedrooms, and square footage, to determine the market value of a property.

• Beer and Diaper

Source: http://dssresources.com/newsletters/66.php

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Source: •http://www.ibmbigdatahub.com/infographic/four-vs-big-data•http://whatsthebigdata.com/2013/07/25/big-data-3-vs-volume-variety-velocity-infographic/

Scale of Data Analysis of Streaming Data

Different forms of data

Uncertainty of data

Veracity

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CRISP-DM Methodology

Source: http://lyle.smu.edu/~mhd/8331f03/crisp.pdf & (link)

Cross Industry Standard Process for Data Mining Methodology

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OLTP Normalized Design

Ordering Ordering ProcessProcess

Ware- Ware- househouse

POS POS ProcessProcess

Chain Chain RetailerRetailer

Retailer Retailer ReturnsReturns

Retailer Retailer PaymentsPayments

StoreStore

ProductProduct

BrandBrandGLGL AccountAccount

ClerkClerk

Retail Retail CustCust

Cash Cash RegisterRegister

Retail Retail PromoPromo

data warehouse and business intelligence dr. minder chen professor of mis

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