Choosing the Right Business Intelligence Tools for Your Data and Architectural Needs

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?

3 Criteria for Selecting Smaller, Less
Known Vendors…

• Vendor Stability

• Technical Support

• Professional Services

Relational Database Tools & Hardware
Evaluation Criteria

• Scalability

•Parallel Processing Support

•RDBMS/Hardware Combination


Popular Relational Database Tools and
Platforms

Popular Relational Databases Popular OS Platforms

• Oracle • Linux
• Microsoft SQL Server • FreeBSD
• IBM DB2 • Microsoft
• Teradata
• Sybase
• MySQL


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.

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

ETL Illustration


ETL Buy vs Build Factors…

• Complexity of the data transformation

• Data cleansing needs

• Data volume

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?

Evaluation Criteria for ETL Tools

• Functionality capability

•Compatibility to your data
source

•Metadata Support

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


OLAP Tools are use for…

• Business Performance Management
• Planning
• Budgeting
• Forecasting
• Financial Reporting
• Analysis
• Simulation Models
• Knowledge Discovery
• and Data Warehouse Reporting

There are 3 Types of OLAP Tools

• MOLAP (Multidimensional
OLAP)

•ROLAP (Relational OLAP)

•HOLAP (Hybrid OLAP)

OLAP Evaluation Criteria

• Ability to leverage parallelism supplied by
RDBMS and hardware

• Performance

• Customization efforts

• Security features

• Metadata support

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

Popular OLAP Tools

• Business Objects
• Cognos
• Hyperion
• DB2
• MicroStrategy

Reporting Tools
Evaluation Criteria

• Number of reports

• Desired Report Distribution Mode

• Ad hoc Report creation

Reporting Tools
Evaluation Criteria

• Data source connection capabilities
• Scheduling and distribution capabilities
• Security features
• Export capabilities
• Integration with the Microsoft Office
environment


Popular Reporting Tools

• Business Objects (Crystal Reports)
• Cognos
• Actuate
• DB2


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

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


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

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

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.


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

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


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

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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