How to Choose Business Intelligence Tool

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Presentation Description

Get a FREE performance management kit and access to all of Victor's full videos at: www.lifecycle-performance-pros.com This presentation takes you through the steps of understanding your business intelligence needs and identifying the right tools for you. We discuss the different types of BI tools. We to discuss the criteria for selecting each type of tools. We to discuss popular Business Intelligence vendors and how to rate them.

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Presentation Transcript

Choosing the Right Business Intelligence Tools for Your Data Architecture : 

Choosing the Right Business Intelligence Tools for Your Data Architecture

Slide 2: 

Types of Business Intelligence Tools… ETL Tools OLAP Tools Reporting tools Metadata tools Data warehouse tools

Slide 3: 

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?

Slide 4: 

3 Criteria for Selecting Smaller, Less Known Vendors… Vendor Stability Technical Support Professional Services

Slide 5: 

Relational Database Tools & Hardware Evaluation Criteria Scalability Parallel Processing Support RDBMS/Hardware Combination

Slide 6: 

Popular Relational Database Tools and Platforms Popular Relational Databases Oracle Microsoft SQL Server IBM DB2 Teradata Sybase MySQL Popular OS Platforms Linux FreeBSD Microsoft

Slide 7: 

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.

Slide 8: 

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

Slide 9: 

ETL Illustration

Slide 10: 

ETL Buy vs Build Factors… Complexity of the data transformation Data cleansing needs Data volume

Slide 11: 

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?

Slide 12: 

Evaluation Criteria for ETL Tools Functionality capability Compatibility to your data source Metadata Support

Slide 13: 

Popular ETL Tools Popular ETL tools include:   Oracle Warehouse Builder (OWB) Business Objects Data Integrator SAS Data Integration Studio Informatica IBM Information Server

Slide 14: 

OLAP (On-Line Analytical Processing) Tools

Slide 15: 

OLAP Tools are use for… Business Performance Management Planning Budgeting Forecasting Financial Reporting Analysis Simulation Models Knowledge Discovery and Data Warehouse Reporting

Slide 16: 

There are 3 Types of OLAP Tools MOLAP (Multidimensional OLAP) ROLAP (Relational OLAP) HOLAP (Hybrid OLAP)

Slide 17: 

OLAP Evaluation Criteria Ability to leverage parallelism supplied by RDBMS and hardware Performance Customization efforts Security features Metadata support

Slide 18: 

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 What is the functional domain for analysis?

Slide 19: 

FASMI Method for OLAP Selection Fast Analysis Shared Multidimensional Information

Slide 20: 

Popular OLAP Tools Business Objects Cognos Hyperion DB2 MicroStrategy

Slide 21: 

Reporting Tools Evaluation Criteria Number of reports Desired Report Distribution Mode Ad hoc Report creation

Slide 22: 

Reporting Tools Evaluation Criteria Data source connection capabilities Scheduling and distribution capabilities Security features Export capabilities Integration with the Microsoft Office environment

Slide 23: 

Popular Reporting Tools Business Objects (Crystal Reports) Cognos Actuate DB2

Slide 24: 

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

Slide 25: 

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

Slide 26: 

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

Slide 27: 

The Metadata Lifecycle The Four Stages of Metadata Lifecycle Requirement Assessment Content Analysis System Requirement Specification Metadata System Service and Evaluation The Metadata Lifecycle Model consist of four stages covering ten steps, and several sub processes for developing a successful metadata strategy

Slide 28: 

Metadata Pros and Cons Pros Good for data requests that are unique every time Save time per search Provides analysis of data from multiple data sources Leverages intellectual infrastructure elements. Cons Metadata implementation is it is extremely expensive Difficult to implement and justify data Creates uncomfortable change among users (low user buy-in)

Slide 29: 

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

Slide 30: 

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

Slide 31: 

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

Slide 32: 

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