1
SoberITSoftware Business and Engineering Institute
HELSINKI UNIVERSITY OF TECHNOLOGY
Enterprise ArchitecturesData Warehousing
Business IntelligenceCustomer Relationship
Management
Timo Itälä, Paavo Kotinurmi, Matti Hämäläinen
2
SoberITSoftware Business and Engineering Institute
HELSINKI UNIVERSITY OF TECHNOLOGY
Timo Itälä
Course Map
13.9: Enterprise Architectures Overview
20.9: ERP and PDM systems
27.9: BI and Data Warehousing
4.10: BPM and SOA
11.10: Content Management Systems
18.10: Enterprise Architecture Summary
3
SoberITSoftware Business and Engineering Institute
HELSINKI UNIVERSITY OF TECHNOLOGY
Timo Itälä
Lesson 3: Data Warehousing
Enterprise Information Cycle
Data, Information, Decisions, Intelligence
Reporting with operational systems
Data Warehouse concept
Data Warehouse architectures
Customer focused retailing
Performance dashboards and scorecards
Customer Relationship Management
4
SoberITSoftware Business and Engineering Institute
HELSINKI UNIVERSITY OF TECHNOLOGY
Timo Itälä
Enterprise Information Cycle
Set Mission, Goals
Prepare Plans, Set Objcetives, Set Measures
Execute the plan
Follow-up
Analyse, Try to understand
Learn
Repeat the above
5
SoberITSoftware Business and Engineering Institute
HELSINKI UNIVERSITY OF TECHNOLOGY
Timo Itälä
Operational systems and reportingRoutine reports from operational systems
Predefined contentPredefined timesPredefined formatShort history
What makes good information?The right informationThe right timeThe right format
Example of an reporting system:53 different reports from an inventory control application
Processes
Data
Decision Support
Information
Dilemma with IT department:Process needs are different from decision support needsOne good answer leads to three new questions
6
SoberITSoftware Business and Engineering Institute
HELSINKI UNIVERSITY OF TECHNOLOGY
Timo Itälä
Multiple operational systems
Multiple departments and their operational and reporting systems
Different conceptual modelDifferent data modelDifferent coding schemesDifferent reporting intervalsDifferent versions of truth
Very difficult to get a holistic view of the enterprise
7
SoberITSoftware Business and Engineering Institute
HELSINKI UNIVERSITY OF TECHNOLOGY
Timo Itälä
Data Warehouse conceptUnbundling environments for operational systems and decision support systems
A Data Warehouse isIntegratedSubject-OrientedTime-VariantNonvolatileDatabase that provides support for decision making
Bill Inmon, ”the father of Data Warehouse”
Data Warehouse
8
SoberITSoftware Business and Engineering Institute
HELSINKI UNIVERSITY OF TECHNOLOGY
Timo Itälä
Early innovators
Industries with mass marketsRetail
Banking
Telecommunications
Airlines
Characteristics of enterprisesHeavy competition
Sufficient size
Marketing and sales orientation
Data Warehouse
9
SoberITSoftware Business and Engineering Institute
HELSINKI UNIVERSITY OF TECHNOLOGY
Timo Itälä
Early examples
RetailK-Mart Marketing: Balance between surplus and empty shelf
Wall-Mart Logistics: From push-to-store to pull-by-customers
TelecommunicationsAmeritech: Usage of the network, customer retention, competition follow-up
Banking and insuranceCustomer management
Risk management
Fraud detection
Data Warehouse
10
SoberITSoftware Business and Engineering Institute
HELSINKI UNIVERSITY OF TECHNOLOGY
Timo Itälä
“Atomic cube”Atomic data
sales
by date
by product
by store
Dimensions
Measures
Days
Pro
du
cts
Store
s
11
SoberITSoftware Business and Engineering Institute
HELSINKI UNIVERSITY OF TECHNOLOGY
Timo Itälä
Adding facts by dimensionsSummary
total sales
at store #4
today
Days
Pro
du
cts
Store
s
12
SoberITSoftware Business and Engineering Institute
HELSINKI UNIVERSITY OF TECHNOLOGY
Timo Itälä
Using summaries in caluculationsAverage
sum sales
of product #5
at store #1
last month
Days
Pro
du
cts
Store
s
13
SoberITSoftware Business and Engineering Institute
HELSINKI UNIVERSITY OF TECHNOLOGY
Timo Itälä
Star schema database
Dimensional model
Fact tableDimension keys
Measures
Dimension tablesDimension keys
Attributes
14
SoberITSoftware Business and Engineering Institute
HELSINKI UNIVERSITY OF TECHNOLOGY
Timo Itälä
Using dimensional model
Dimension attributes are used as column headings
Facts are summarized
Sort order can change when needed
by city, by product
Subtotals when dimension value changes
Grand total in the end
15
SoberITSoftware Business and Engineering Institute
HELSINKI UNIVERSITY OF TECHNOLOGY
Timo Itälä
OLAP On-Line Analytical ProcessingThe ”Cube paradigm”
Drill DownSlice and Dice
Why drop in Oulu?
What caused dress department drop in Oulu?
Why drop on week 35?
16
SoberITSoftware Business and Engineering Institute
HELSINKI UNIVERSITY OF TECHNOLOGY
Timo Itälä
Atomic data; Granularity
Point-of-Sales:Customer purchase receipt row
POS data
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SoberITSoftware Business and Engineering Institute
HELSINKI UNIVERSITY OF TECHNOLOGY
Timo Itälä
Example: POS dataIndependent Data Marts
SalesPurchasesEmployees
…
Staging Area
Source systems
Data Warehouse
End user access and applications
Extract
Transform
Load
(ETL)
Dimensions:
Stores, Products, Customers, Time
Transaction Data
Summary Data
OLAP tools
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SoberITSoftware Business and Engineering Institute
HELSINKI UNIVERSITY OF TECHNOLOGY
Timo Itälä
Example: Market situation, ice cream manufacturer
Question of product marketing managerBest products?Best customers?Best regions?Best seasons?How are we doing against competition?How effective are our marketing campaigns?What is the effect of sunny weather?
Sales of ice creamby product, customer, region, time
from manufacturerInternal
Sales of ice creamby product, customer, region, time
from retail stores
Marketing of ice creamby product, media, region, time
from marketing department
External
Internal
Weather reports
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SoberITSoftware Business and Engineering Institute
HELSINKI UNIVERSITY OF TECHNOLOGY
Timo Itälä
Data Warehousing architectures
Independent data marts
Data mart bus architecture with linked dimensional data marts
Hub and spoke architecture
Centralized data warehouse
Federated architecture
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SoberITSoftware Business and Engineering Institute
HELSINKI UNIVERSITY OF TECHNOLOGY
Timo Itälä
Independent data martsIndependent Data Marts
SalesPurchasesEmployees…
Dimensional data model
Staging Area
Source systems
Data Mart
End user access and applications
21
SoberITSoftware Business and Engineering Institute
HELSINKI UNIVERSITY OF TECHNOLOGY
Timo Itälä
Data mart bus architecture with linked dimensional data marts (Kimball)
Process transactions
Raw material
Work in progress
Finished products
…l
Staging Area
Source systems
Data Marts with
confirmad dimensions
End user access and applications
Facts and dimensions
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SoberITSoftware Business and Engineering Institute
HELSINKI UNIVERSITY OF TECHNOLOGY
Timo Itälä
Hub and spoke (Bill Inmon) Enterprise wide DW
Internal dataExternal data
…
Dependent data marts for specific user groups
Staging Area
Source systems
Data Warehouse
(normalized)
End user access and applications
Dependent Data Marts
23
SoberITSoftware Business and Engineering Institute
HELSINKI UNIVERSITY OF TECHNOLOGY
Timo Itälä
Centralized Data Warehouse
Enterprise wide DW
Internal data
External data
…
Direct Access to DW
Staging Area
Source systems
Data Warehouse
(normalized)
End user access and applications
24
SoberITSoftware Business and Engineering Institute
HELSINKI UNIVERSITY OF TECHNOLOGY
Timo Itälä
Federated architecture
Staging Area
Source systems
Data Warehouse
(normalized)
End user access and applications
Dependent Data Marts
Logical/Physical
Integration
25
SoberITSoftware Business and Engineering Institute
HELSINKI UNIVERSITY OF TECHNOLOGY
Timo Itälä
Architecture and methodology
ArchitectureComponent parts, their characteristics and relationships among the other parts
Hub and spoke vs. confirmed Data marts
MethdologyActivities
Sequencing of activities
Top-down vs. Bottom-up
26
SoberITSoftware Business and Engineering Institute
HELSINKI UNIVERSITY OF TECHNOLOGY
Timo Itälä
Selection Factors
Information interdepence between organizationsUpper Management´s information needsUrgency of need for data warehouseNature of end user tasksConstraints on resourcesView of the data warehouse prior to implementationExpert influenceCompatibility with existing systemsThe perceived ability of the in-house IT staffSource of sponsorshipTechnical issues
27
SoberITSoftware Business and Engineering Institute
HELSINKI UNIVERSITY OF TECHNOLOGY
Timo Itälä
Popularity
Source: Data Warehuse Architectures: Factors in the Selection Decision and the Success of the Architectures.
Hugh J. Watson, Terry College of Business, University of Georgia, Athens, Georgia 30602
Thilini Ariyachandra, College of Business, University of Cincinnati, Cincinnati, Ohio 45221
39% Hub and spoke
27% Data mart bus
17% Centralized data warehouse
13% Independent data marts
4% Federated architecture
Number of respondents: 454
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SoberITSoftware Business and Engineering Institute
HELSINKI UNIVERSITY OF TECHNOLOGY
Timo Itälä
Operational Data Store
DW
Operational Data Store(up-to-date current data)
Decisions based on current situation
29
SoberITSoftware Business and Engineering Institute
HELSINKI UNIVERSITY OF TECHNOLOGY
Timo Itälä
Tools and products
Portal tools
ETL Tools
Data Base Management
System
Business Intelligence Tools:
Reporting
OLAP
Dashboards
Scorecards
ETL Tools
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SoberITSoftware Business and Engineering Institute
HELSINKI UNIVERSITY OF TECHNOLOGY
Timo Itälä
Extract-Transform-Load process
ETL Tools
Extract dataExtract from source databaseReceive messagesNew data after previous extract
TransformFrom source to target presentation
Load
ConsiderationsData Quality ProfilingData Quality CleansingScheduling
31
SoberITSoftware Business and Engineering Institute
HELSINKI UNIVERSITY OF TECHNOLOGY
Timo Itälä
Metadata
Business metadata (concepts, measures, calculations…)Technical metadata (structures, transformations…)Operational metadata (tasks, schedules, logs,…)
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SoberITSoftware Business and Engineering Institute
HELSINKI UNIVERSITY OF TECHNOLOGY
Timo Itälä
Example: Retailers vital data
Sales and Product Data
Merchandising & BuyingSupplyChain
TradingLocation
Our Store
OurStore
Our Store
MarketingCustomer
Data
33
SoberITSoftware Business and Engineering Institute
HELSINKI UNIVERSITY OF TECHNOLOGY
Timo Itälä
Customer Focused Marketing
Identify your customersWho they are Where they are What they are like
Capture shopping dataAnalyse basket dataIdentify common attributes
Plan targeted marketing and merchandising activities … … to change their behaviourMeasure the impact on the customers
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SoberITSoftware Business and Engineering Institute
HELSINKI UNIVERSITY OF TECHNOLOGY
Timo Itälä
The Vital Questions
Which customers come to the stores?
What are their shopping habits?
Are they right for the future?
How valuable are they today?
What is their lifetime value?
How do they react to marketing campaigns?
How can we target, attract and retain them?
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SoberITSoftware Business and Engineering Institute
HELSINKI UNIVERSITY OF TECHNOLOGY
Timo Itälä
The Future
Customer-centric data warehouses will become an essential tool for many retail organisations
Gradual move from analysis to modelling and predicting future customer purchasing behaviour
The winners will understand their customers
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SoberITSoftware Business and Engineering Institute
HELSINKI UNIVERSITY OF TECHNOLOGY
Timo Itälä
Evolution of a Solution
The search for the perfect “business insight system”:
1980s Executive information systems (EIS)
Decision support systems (DSS)
1990sData warehousing (DW)
Business intelligence (BI)
2000sCorporate performance management (CPM)
Performance dashboards
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SoberITSoftware Business and Engineering Institute
HELSINKI UNIVERSITY OF TECHNOLOGY
Performance Dashboards
Source: The Data Warehousing Institute (TDWI)
38
SoberITSoftware Business and Engineering Institute
HELSINKI UNIVERSITY OF TECHNOLOGY
Timo Itälä
Two Metaphors
Dashboard Performance Chart
Performance Dashboard
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SoberITSoftware Business and Engineering Institute
HELSINKI UNIVERSITY OF TECHNOLOGY
Timo Itälä
Two Disciplines
+Business Intelligence
Performance Management
=
Performance Dashboards
Information
Knowledge
Plans
Act
Data Warehouses
Analytical Tools
Rules and Models
Review, Measure, Refine
Data
Events
Data
Wisdom
DATA REFINERY
STRATEGY
EXECUTION
1. Strategize 2. Plan
3. Monitor/Analyze
4. Act/Adjust
IntegratedData
Miss
ion,
Value
s, G
oals
Objec
tives
, Inc
entiv
es
Stra
tegy
Map
s
Budgets, Plans,
Forecasts,
Models
Initiatives,
Targets
BI/D
W
Perfo
rman
ce
Dashb
oard
sActions,
Decisions,
Revisions
40
SoberITSoftware Business and Engineering Institute
HELSINKI UNIVERSITY OF TECHNOLOGY
Timo Itälä
Why Performance Dashboards?Resonate with users
Monitor status of several areas on one screenGraphical view of key metricsAlerts users to exception conditionsClick to analyze and drill to detailCustomized views based on rolePersonalized views based on interestNo training required
Rich data Blends data from multiple sourcesBoth detailed and aggregated Both historical and real-time
41
SoberITSoftware Business and Engineering Institute
HELSINKI UNIVERSITY OF TECHNOLOGY
Timo Itälä
Why Performance Dashboards?
Aligns the businessEveryone uses the same data
Everyone uses the same metrics
Everyone works off the same objectives
Optimizes performance and complianceCloses gap between strategy and execution
Greater visibility into business
Makes processes more efficient
Makes workers more effective
42
SoberITSoftware Business and Engineering Institute
HELSINKI UNIVERSITY OF TECHNOLOGY
Timo Itälä
Performance Dashboard
Three Layers of Information
Detailed, Operational DataDW queries, Operational reports
Summarized Dimensional DataDimensions, hierarchies, “slice/dice”
Graphical Abstracted DataGraphs, Symbols, Charts
Pla
nnin
gP
lans
, mod
els,
fore
cast
s, u
pdat
es
Monitoring
Analysis
Reporting
Collaboration
43
SoberITSoftware Business and Engineering Institute
HELSINKI UNIVERSITY OF TECHNOLOGY
Timo Itälä
Dashboards vs Scorecards
SymbolsCharts Display
SummariesEventsData
Periodic snapshots“Right time” feedsUpdates
Executives, managers, staff Executives, managers, staffUsers
Charts progressMeasures current activityPurpose
ScorecardDashboard
Dashboards and scorecards are visual interfaces for monitoring business performance
44
SoberITSoftware Business and Engineering Institute
HELSINKI UNIVERSITY OF TECHNOLOGY
Timo Itälä
Three Types
CollaborationAnalysisMonitoringEmphasis
“Dashboard”
Intra-day
Detailed
Operational
Supervisors+
Monitor operations
Operational
“Scorecard”
Monthly/Quarterly
Summary
Enterprise
Executives+
Execute strategy
Strategic
“BI Portal”“Looks like a…”
Daily/WeeklyUpdates
Detailed/SummaryInformation
DepartmentalScope
Managers+Users
Optimize processApplication Emphasis
Tactical
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SoberITSoftware Business and Engineering Institute
HELSINKI UNIVERSITY OF TECHNOLOGY
Timo Itälä
Tactical Dashboard CaseInternational Truck and Engine
$9.7 billion manufacturer of trucks, buses, diesel engines, and parts based in Illinois
Key business issues: Market reality: Global competition, new regulations, emerging markets
Goals: 1) $15b in revenues 2) reduced costs, 3) improved quality, 4) reduced risk
46
SoberITSoftware Business and Engineering Institute
HELSINKI UNIVERSITY OF TECHNOLOGY
Timo Itälä
KBI Portal
Purpose Deliver actionable information to financial analysts
ScopeSpans 32 source systems across five divisions
130 key business indicators, updated daily
Supports 500 financial executives, managers, and analysts
UpshotBridges gulf between finance and operations
Replaces hodge-podge of paper reports
Saves analysts time creating custom reports
Shuts down dozens of reporting systems
47
SoberITSoftware Business and Engineering Institute
HELSINKI UNIVERSITY OF TECHNOLOGY
Timo Itälä
Architecture
Source Systems
Star Schema Database
OLAP Cubes
Reporting Portal
Purpose
Staging Area Database
TransactionalRun the business
Gather all data in one place. Keep a copy for future reuse.
Integrate data for easy loading into OLAP cubes.
Store data dimensionally to support fast queries and easy navigation.
Data
Display key metrics so they can be viewed at a glance.
Transactional
Transactional & lightly summarized
Moderately summarized
Highly summarized
ETLTools
ETLTools
Web Server
Data Warehouse
48
SoberITSoftware Business and Engineering Institute
HELSINKI UNIVERSITY OF TECHNOLOGY
Timo Itälä
Monitoring Layer
49
SoberITSoftware Business and Engineering Institute
HELSINKI UNIVERSITY OF TECHNOLOGY
Timo Itälä
Detail Transaction Layer
‘Click’ any VIN to expandfull page order/build report
‘Click’ any VIN to expandfull page order/build report
50
SoberITSoftware Business and Engineering Institute
HELSINKI UNIVERSITY OF TECHNOLOGY
Timo Itälä
Analysis Layer
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SoberITSoftware Business and Engineering Institute
HELSINKI UNIVERSITY OF TECHNOLOGY
Timo Itälä
BI Maturity Model
1. Prenatal 2. Infant 3. Child 4. Teenager 5. Adult 6. Sage
“Production Reporting”
“Spreadmarts”
“Data Marts”
“Data Warehouses
”
“Enterprise DW”
“Analytic Services”
GULF CHASM
Business Value Semantic Integration Data Consolidation
52
SoberITSoftware Business and Engineering Institute
HELSINKI UNIVERSITY OF TECHNOLOGY
Timo Itälä
Customer relationship management
Customer relationship management (CRM) encompasses the capabilities, methodologies, and technologies that support an enterprise in managing customer relationships.(Source:Wikipedia)
CRM is a holistic change in an organisation's philosophy which places emphasis on the customer.
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SoberITSoftware Business and Engineering Institute
HELSINKI UNIVERSITY OF TECHNOLOGY
Timo Itälä
Application architecture of CRM
Operational - automation to the basic business processes (marketing, sales, service)Analytical - support to analyze customer behavior, implements business intelligence alike technologyCollaborative - ensures the contact with customers (phone, email, fax, web, sms, post, in person)Most successful analytical CRM projects take advantage of a data warehouse to provide suitable data.
54
SoberITSoftware Business and Engineering Institute
HELSINKI UNIVERSITY OF TECHNOLOGY
Timo Itälä
Enterprise Architecture
And the journey continues..Business
Data
Applications
Technology
Questions?