This document discusses the benefits of using a model-driven approach for business intelligence (BI) projects. It begins by noting that BI is a top priority for chief information officers. It then shows that many BI projects fail to meet their targets. The document advocates using a model-driven architecture (MDA) to improve BI project success. MDA defines system functionality using platform-independent and platform-specific models, with model transformations between them. When applied to BI, MDA starts with business requirements and progresses through dimensional data models, logical models, and physical implementations. Integrating metadata across these models improves the information loop between business users and BI platforms. The document presents an example of applying MDA to a web analytics use case and

1. Model Driven
Business Intelligence
Stuttgart, 27/11/2013
Stefano Cazzella @StefanoCazzella
http://caccio.blogdns.net
stefano.cazzella{at}gmail.com
Model Driven Business Intelligence - Stuttgart, 27/11/2013 - Stefano Cazzella
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2. BI is in the top 10 priorities of CIOs
Top 10 Technology Priorities
Top 10 Business Priorities
①
②
③
④
⑤
⑥
⑦
⑧
⑨
⑩
①
②
③
④
Analytics and BI
Mobile technologies
Cloud computing
Collaboration technologies
Legacy modernization
IT management
CRM
Virtualization
Security
ERP applications
⑤
⑥
⑦
⑧
⑨
⑩
Increasing enterprise growth
Delivering operational results
Reducing enterprise costs
Attracting and retaining new
customers
Improving IT applications and
infrastructure
Creating new products and services
Improving efficiency
Attracting and retaining the workforce
Implementing analytics and big data
Improving business processes
Top 10 CIO Business and Technology Priorities in 2013 - Gartner survey involving 2.053 CIOs
Model Driven Business Intelligence - Stuttgart, 27/11/2013 - Stefano Cazzella
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3. 25% of BI projects miss the target
How successful is your organization’s use of
BI in supporting improved business
performance?
56%
Mostly a failure
Less successful than
expected
Somewhat successful
23%
Very successful
2%
19%
Information Week – Business Intelligence Survey in 2008 involving 385 professionals
Model Driven Business Intelligence - Stuttgart, 27/11/2013 - Stefano Cazzella
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4. BI Project success factors
Success factors:
Management:
• Engineering method
(methodology)
• Project & Quality plan
• Delivery control
• Resource management
BI Project
Business:
• Sponsorship & Commitment
• Well defined Business Objectives
(Business User Requirements)
• Focus on Business Value (ROI?)
To drive a (BI) Project to
the success three main
areas must be mastered:
• Management
• Business
• Technology
Technology:
• SW/HW platforms
• Technical architecture
• Technical skill / Best practices
Model Driven Business Intelligence - Stuttgart, 27/11/2013 - Stefano Cazzella
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5. Model-driven BI Method
Management
Engineering method (MDA)
Business
Business user-requirements
Technology
Tech. architecture & best practices
The model-driven approach
• is largely adopted in
industrialized software
development projects
• is either business and
technical (functional /
non functional)
requirements driven
• may be applied in
several application
lifecycle models:
RUP, Agile, waterfall, e
tc.
• is based on metadata
integration
Model Driven Business Intelligence - Stuttgart, 27/11/2013 - Stefano Cazzella
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6. Model-Driven Architecture (MDA)
Definition
• Model Driven Architecture (MDA) is a software design approach
for the development of software systems.
• The Model-Driven Architecture approach defines system
functionality using a platform-independent model (PIM) using an
appropriate domain-specific language (DSL).
• Then, given a platform model […] the PIM is translated to one or
more platform-specific models (PSMs) that computers can run.
• Model transformation is the process of converting one model to
another model of the same system
PIM
PSM
Model
transformation
Code
Model
transformation
Model Driven Business Intelligence - Stuttgart, 27/11/2013 - Stefano Cazzella
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7. MDA applied to BI & Analytics
Business (functional) requirements
•
•
PIM
Business-centric
No technical details
Non-functional requirements
Technical specifications (platform)
PSM
•
•
Technical design
System architecture
Implementation best practices
Code
•
•
Source code
Software modules
Fact, measures, dimensions, …
Dimensional Fact Model
Star-schema / snow-flake
Surrogate key
Slow changing dimension
Relational Logical Model
Indexes, partitions, …
Phisical model / DDL
Model Driven Business Intelligence - Stuttgart, 27/11/2013 - Stefano Cazzella
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8. PIM – From requisite to DFM
• Context: weblog analytics - the
analysis of the visits of several
web sites belonging to different
domains (eg. Google Analytics)
• Requisite: monitoring and
analyzing the number of visits
and their monthly and daily
average duration for each page
of the websites, or each
domain, distributed by the
geographic region of the IP of the
visitors.
Model Driven Business Intelligence - Stuttgart, 27/11/2013 - Stefano Cazzella
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9. PSM – Relational Logical Data Model
Surrogate key
SCD-2
Start date
End date
Model
transformation
Technical design choices:
Fact grain
• Reference ROLAP model  star-schema
• Hierarchy Viewer use surrogate key
• Hierarchy Page  SCD – Type 2
Model Driven Business Intelligence - Stuttgart, 27/11/2013 - Stefano Cazzella
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10. Phisical model and DDL (1)
Implementation choices & best practice:
•
•
•
•
DBMS  SQL Server
Fact F_VISITS partitioned by year
Column-store index on day and duration
2 distinct file groups for tables and indexes
Partition scheme and functions
File groups
Columnstore index
Model Driven Business Intelligence - Stuttgart, 27/11/2013 - Stefano Cazzella
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11. Phisical model and DDL (2)
Implementation choices & best practice:
•
•
•
•
DBMS  Oracle
Fact F_VISITS partitioned by year
Bitmap index on viewer dimension
2 distinct table spaces for tables and
indexes
Table spaces
Table partitions
Bitmap index
Model Driven Business Intelligence - Stuttgart, 27/11/2013 - Stefano Cazzella
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12. The information-making loop
Business User
BI & Analytics Platform
Information
Data
Requirements definition
Model
transformation
Multidimensional
data model
Data Mart
Deployment
Model
transformation
Logical data model
Model Driven Business Intelligence - Stuttgart, 27/11/2013 - Stefano Cazzella
Phisical data model
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13. “Classic” DW Architecture
Data Mart 1
Operational systems
Source 1
ETL
BI
ETL
EDW
Read
Source
Tables
Elaborate
data
Write
Target
Tables
Maps tables and
columns to
business-domain
concepts and
terms
Extract-Transform-Load processes
Model Driven Business Intelligence - Stuttgart, 27/11/2013 - Stefano Cazzella
Semantic
layer
Data Mart n
Source n
Reports
Dashboards
Analytics
BI Platform
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14. Model driven / Metadata integration
Metadata Integration
Model-driven architecture
Source mapping
Relational
Source
Loading strategy
ETL
E/R
Business rules
DFM
Relational
Loading strategy
Relational
Semantic layer
EDW
ETL
Data Mart
BI
Model Driven Business Intelligence - Stuttgart, 27/11/2013 - Stefano Cazzella
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15. The Project Blueprint
Metadata Integration
Model-driven architecture
PIM - Platform Independent Model
Source mapping
E/R
Business rules
DFM
Project Blueprint
PSM - Platform Specific Model
Relational
Loading strategy
Relational
Loading strategy
Relational
Semantic layer
Project Deliverables
Code – Data Warehouse System Components
Source
ETL
EDW
ETL
Model Driven Business Intelligence - Stuttgart, 27/11/2013 - Stefano Cazzella
Data Mart
BI
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16. Essential Blueprint Models
E/R
DFM
Source 1
Relational
Data Model
EDW
Loading
Strategy
Source 2
Relational
Data Model
EDW
Relational
Data Model
Data Mart
Loading
Strategy
Data Mart
Relational
Data Model
Business Intelligence Modeler
Metadata Integration
Model Driven Business Intelligence - Stuttgart, 27/11/2013 - Stefano Cazzella
Model transformation
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17. BI Modeler Roadmap
Entity Relationship
Semantic layer
Hadoop Hive support
(phisical data model)
Self-service custom
documentation
Blueprint & Loading
strategy
Model Driven Business Intelligence - Stuttgart, 27/11/2013 - Stefano Cazzella
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