Choosing the Right Business Intelligence
Tools for Your Data Architecture

Types of Business Intelligence Tools…
• ETL Tools
• OLAP Tools
• Reporting tools
• Metadata tools
• Data warehouse tools
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5 Criteria for Identifying Business
Intelligence Tools…
• Do we intend to buy these products or build them in-
house?
• What are the requirements for our business
intelligence tools?
• What technical skills does my staff possess?
• What is our timeline for implementation?
• What is our available budget for the task?
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3 Criteria for Selecting Smaller, Less
Known Vendors…
• Vendor Stability
• Technical Support
• Professional Services
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Relational Database Tools & Hardware
Evaluation Criteria
• Scalability
•Parallel Processing Support
•RDBMS/Hardware Combination
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Popular Relational Database Tools and
Platforms
Popular Relational Databases Popular OS Platforms
• Oracle • Linux
• Microsoft SQL Server • FreeBSD
• IBM DB2 • Microsoft
• Teradata
• Sybase
• MySQL
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ETL (Extract, Transform, & Load) Tools
ETL data can come from many sources:
•mainframe applications
•ERP applications
•CRM tools
•flat files
•Excel spreadsheets,
•or even a message queue.
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ETL Tools apply rules to convert
content from multiple databases
For Example, a Telephone number can be
recorded several ways
• (123) 456-7890
• 1234567890
• 123-456-7890
• 123 456 7890
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ETL Illustration
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ETL Buy vs Build Factors…
• Complexity of the data transformation
• Data cleansing needs
• Data volume
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ETL Implementation Questions
• How many sources will users extract the information
from?
• What are your types of data sources?
• What are the expected sizes of these input files?
• Is there a need to join information from different
sources?
• What are the growth expectations
• What is the current environment
• What is the budget for purchasing your ETL tool?
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Evaluation Criteria for ETL Tools
• Functionality capability
•Compatibility to your data
source
•Metadata Support
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Popular ETL Tools
Popular ETL tools include:
•Oracle Warehouse Builder (OWB)
•Business Objects Data Integrator
•SAS Data Integration Studio
•Informatica
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IBM Information Server

OLAP (On-Line Analytical Processing)
Tools
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OLAP Tools are use for…
• Business Performance Management
• Planning
• Budgeting
• Forecasting
• Financial Reporting
• Analysis
• Simulation Models
• Knowledge Discovery
• and Data Warehouse Reporting
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There are 3 Types of OLAP Tools
• MOLAP (Multidimensional
OLAP)
•ROLAP (Relational OLAP)
•HOLAP (Hybrid OLAP)
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OLAP Evaluation Criteria
• Ability to leverage parallelism supplied by
RDBMS and hardware
• Performance
• Customization efforts
• Security features
• Metadata support
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OLAP Implementation Questions
• How many developers need to access the
tool?
• Is there any security to be implemented for
access?
• What is the level of end users knowledge
• What is the budget
• What is the report requirement
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What is the functional domain for analysis?

FASMI Method for OLAP Selection
• Fast
• Analysis
• Shared
• Multidimensional
• Information
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Popular OLAP Tools
• Business Objects
• Cognos
• Hyperion
• DB2
• MicroStrategy
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Reporting Tools
Evaluation Criteria
• Number of reports
• Desired Report Distribution Mode
• Ad hoc Report creation
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Reporting Tools
Evaluation Criteria
• Data source connection capabilities
• Scheduling and distribution capabilities
• Security features
• Export capabilities
• Integration with the Microsoft Office
environment
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Popular Reporting Tools
• Business Objects (Crystal Reports)
• Cognos
• Actuate
• DB2
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Metadata Tools
Metadata refers to the collection of information
about the data stored in an application or database.
Information about data can include:
•Definition of the data
•Type of data
•Size of data
•Systems abbreviation
•Business unit
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3 Criteria For Developing a Metadata
Strategy
• The definition of a common metamodel
• The configuration of products which will
be used to acquire, manage, and
distribute metadata
• Defining and enforcing a methodology
for the use and maintenance of the above
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Steps for Ensuring a Successful Metadata
Implementation
• Establish data owners, custodians and
users
• Formalize data stewardship committee
• Define corporate data and process
standards
• Define edit philosophy and user
education
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The Metadata Lifecycle
The Metadata Lifecycle Model consist of four stages
covering ten steps, and several sub processes for
developing a successful metadata strategy
The Four Stages of Metadata Lifecycle
•Requirement Assessment Content Analysis
•System Requirement Specification
•Metadata System
•Service and Evaluation
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Metadata Pros and Cons
Pros Cons
• Good for data requests • Metadata implementation
that are unique every time is it is extremely
expensive
• Save time per search
• Difficult to implement and
• Provides analysis of data
justify data
from multiple data
sources • Creates uncomfortable
change among users (low
• Leverages intellectual
user buy-in)
infrastructure elements.
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Data Warehousing Pros
• Simplifies the technical aspects of database technology
• Performs server or disk bound requests that require
querying and reporting
• Queries and reports data from multiple transaction
processing systems
• Utilizes data models or server technologies that speed
up querying and reporting
• Provides query and reporting capabilities while limiting
access to the transaction processing system
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Data Warehousing Pros
• Provides customers with access to data from a vast
amount of transactions each day delivered via
management reports, scorecards and dashboards
• Provides a repository of “cleaned up” data without
having to change the transaction processing systems
• Provides a repository or “snapshot” of data to archive
“as was” information
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Data Warehousing Cons
• Stored, historical data has limited value in many
organizations
• Data warehousing systems can complicate business
processes
• Not necessary if reporting needs are simple and there
is not a requirement for end user ad hoc querying and
reporting
• There is a learning curve that may not be suitable for
time constraints or budget
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Data Warehousing Cons
• Not useful if there is not a clearly determined process
or guideline for adding data and data sources to a
warehouse.
• Data warehousing requires a great deal of
maintenance
• The cost to capture, clean and load data in a special
format may be out of budget
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