SlideShare una empresa de Scribd logo
1 de 5
Descargar para leer sin conexión
Tech Notes



Why Data Warehouse Projects Fail
Using Schema Examination Tools to Ensure Information Quality,
Schema Compliance, and Project Success

Embarcadero Technologies

January 2008




Corporate Headquarters        EMEA Headquarters         Asia-Pacific Headquarters
100 California Street, 12th   York House                L7. 313 La Trobe Street
Floor                         18 York Road              Melbourne VIC 3000
San Francisco, California     Maidenhead, Berkshire     Australia
94111                         SL6 1SF, United Kingdom
Why Data Warehouse Projects Fail



According to a 2003 Gartner report, more than 50 percent of data warehouse projects
failed, and the ones that survived were delivered very late with extremely high costs. In
a 2007 study, Gartner predicted once more that 50 percent of data warehouse
projects would have limited acceptance or be outright failures as a result of lack of
attention to data quality issues.


DATA QUALITY IS NOT ENOUGH
Data Quality is one of the hottest topics in any IT shop. Although very important, Data
Quality is far from being enough because decisions are based on information, not on
data. Having quality data does not assure quality information. To have quality
information, it is necessary to have quality data, but this is not sufficient on its own. We
need more.


IT IS ALL ABOUT THE DATABASE SCHEMA
Information is produced by an application program that accesses data in a database,
usually a relational database such as Oracle, DB2, Sybase, SQL Server, etc. The core of
the database is the database schema, wherein are stored all the data definitions, the
relationships between the data, and the business rules.

The quality of the information depends on 3 things: (1) the quality of the data itself, (2)
the quality of the application programs and (3) the quality of the database schema.

Joe Celko (www.celko.com), a very well known expert and consultant in relational
technology, states that without a quality database schema, it is very difficult to:
• Achieve good program performance and
• Deliver quality information
When developing any database application, we must always ensure the database
schema has integrity and consistency – and no flaws. This must be done when the
schema is created, and every time it is changed. If the database schema has flaws, the
information will be flawed and the Data Warehouse projects will fail.


MODELING TOOLS ARE NOT ENOUGH
Database schemas are normally created using a modeling tool such as ERwin®,
ER/Studio®, or PowerDesigner®. These tools validate the data model for completeness of
the model, but they do not have the intelligence to “debug” the data model.


QUALITY OF FEEDER SYSTEMS
Data Warehouse projects depend on feeder systems. If the database schemas of the
feeder systems have flaws, the information produced by the data warehouse will not
have quality. This is the major reason why data warehouse projects fail. The database



Embarcadero Technologies                                                                -1-
Why Data Warehouse Projects Fail


schemas of the feeder systems must be validated for consistency, integrity and
compliance to the rules of the relational technology before a data warehouse project
is initiated. This is where Embarcadero® Schema Examiner™ comes in.


SCHEMA EXAMINER
Schema Examiner was created to fill this gap, providing a means to “debug” the
schema. Schema Examiner provides over 50 diagnostics to assure the schema adheres
to the relational model, is consistent and has integrity. Schema Examiner can validate
the data model, a set of SQL/DDL scripts or the database schema directly. Schema
Examiner can also compare schemas, indicating the differences.

Manual validation is impossible due to the size and complexity of today’s database
schemas.




A SUCCESSFUL DATA WAREHOUSE PROJECT
A corporation in the telecom business contracted with one of the major consulting
companies to develop a large data warehouse project. The cost of the project was $10


Embarcadero Technologies                                                          -2-
Why Data Warehouse Projects Fail


million. After the project was in production, they discovered that the quality of the
information was not good; many answers were inconsistent. They considered to re-do
the entire project or even scrap it.

Their committee suggested the hiring of a consultant. The consultant used Schema
Examiner and after a couple of weeks of analyzing the feeder systems, he made
several suggestions based upon the findings of Schema Examiner. Once the
recommendations were adopted, the results improved dramatically and the project
was a total success.

The client has stated that the success of the project was due to the use of Schema
Examiner.

They immediately purchased an enterprise license and made it mandatory to use
Schema Examiner in all their IT projects, internal or external.

The project used 4 Oracle feeder system that were “debugged” using Schema
Examiner; they were also compared to each other to discover inconsistencies. Below is
a graphical representation of the project.


SUMMARY
•   About half of all Data Warehouse projects fail due to poor data quality (Gartner
    Group)
•   Data Quality is not enough - decisions are based on information quality, not on data
    quality
•   A flawed schema impacts negatively on information quality
•   Database schemas must be validated for compliance with the rules of relational
    technology
•    Modeling tools validate data models / schemas for completeness, not for
    compliance
•   Data Warehouse feeder systems schemas must be validated for compliance
•   Schema Examiner validates schemas from feeder systems and compares them to
    verify inconsistencies




Embarcadero Technologies                                                            -3-
Embarcadero Technologies, Inc. is a leading provider of award-winning tools for
application developers and database professionals so they can design systems right,
build them faster and run them better, regardless of their platform or programming
language. Ninety of the Fortune 100 and an active community of more than three
million users worldwide rely on Embarcadero products to increase productivity, reduce
costs, simplify change management and compliance and accelerate innovation. The
company’s flagship tools include: Embarcadero® Change Manager™, CodeGear™
RAD Studio, DBArtisan®, Delphi®, ER/Studio®, JBuilder® and Rapid SQL®. Founded in 1993,
Embarcadero is headquartered in San Francisco, with offices located around the world.
Embarcadero is online at www.embarcadero.com.

Más contenido relacionado

La actualidad más candente

DATPROF Test data Management (data privacy & data subsetting) - English
DATPROF Test data Management (data privacy & data subsetting) - EnglishDATPROF Test data Management (data privacy & data subsetting) - English
DATPROF Test data Management (data privacy & data subsetting) - EnglishDATPROF
 
Etl developer job description
Etl developer  job descriptionEtl developer  job description
Etl developer job descriptionViswanath Syamlal
 
Transforming Business Intelligence Testing
Transforming Business Intelligence TestingTransforming Business Intelligence Testing
Transforming Business Intelligence TestingMethod360
 
Five Critical Elements for Successful Agile Data Management
Five Critical Elements for Successful Agile Data ManagementFive Critical Elements for Successful Agile Data Management
Five Critical Elements for Successful Agile Data ManagementTechWell
 
Resume z. o. ekumatalor
Resume    z. o. ekumatalorResume    z. o. ekumatalor
Resume z. o. ekumatalorozekuma
 
Sap information steward
Sap information stewardSap information steward
Sap information stewardytrhvk
 
Mdm for materials –positive impact of data quality improvement
Mdm for materials –positive impact of data quality improvementMdm for materials –positive impact of data quality improvement
Mdm for materials –positive impact of data quality improvementVerdantis Inc.
 
Ibm Optim Techical Overview 01282009
Ibm Optim Techical Overview 01282009Ibm Optim Techical Overview 01282009
Ibm Optim Techical Overview 01282009lucascibm
 
Production Support_ETL_Informatica Developer_raja_velpula_5yrs
Production Support_ETL_Informatica Developer_raja_velpula_5yrsProduction Support_ETL_Informatica Developer_raja_velpula_5yrs
Production Support_ETL_Informatica Developer_raja_velpula_5yrsrajasekhar velpula
 
Resume_David_Colbourn September 2016
Resume_David_Colbourn September 2016Resume_David_Colbourn September 2016
Resume_David_Colbourn September 2016David Colbourn
 
Resume - Abhishek Ray-Mar-2016 - Ind
Resume - Abhishek Ray-Mar-2016 - IndResume - Abhishek Ray-Mar-2016 - Ind
Resume - Abhishek Ray-Mar-2016 - IndAbhishek Ray
 

La actualidad más candente (20)

DATPROF Test data Management (data privacy & data subsetting) - English
DATPROF Test data Management (data privacy & data subsetting) - EnglishDATPROF Test data Management (data privacy & data subsetting) - English
DATPROF Test data Management (data privacy & data subsetting) - English
 
Ramachandran_ETL Developer
Ramachandran_ETL DeveloperRamachandran_ETL Developer
Ramachandran_ETL Developer
 
Etl developer job description
Etl developer  job descriptionEtl developer  job description
Etl developer job description
 
Transforming Business Intelligence Testing
Transforming Business Intelligence TestingTransforming Business Intelligence Testing
Transforming Business Intelligence Testing
 
Five Critical Elements for Successful Agile Data Management
Five Critical Elements for Successful Agile Data ManagementFive Critical Elements for Successful Agile Data Management
Five Critical Elements for Successful Agile Data Management
 
Prasad_Resume
Prasad_ResumePrasad_Resume
Prasad_Resume
 
Resume z. o. ekumatalor
Resume    z. o. ekumatalorResume    z. o. ekumatalor
Resume z. o. ekumatalor
 
Data Quality Everywhere
Data Quality EverywhereData Quality Everywhere
Data Quality Everywhere
 
Sap information steward
Sap information stewardSap information steward
Sap information steward
 
Nitin Paliwal
Nitin PaliwalNitin Paliwal
Nitin Paliwal
 
Mdm for materials –positive impact of data quality improvement
Mdm for materials –positive impact of data quality improvementMdm for materials –positive impact of data quality improvement
Mdm for materials –positive impact of data quality improvement
 
Geza Asboth Resume
Geza Asboth ResumeGeza Asboth Resume
Geza Asboth Resume
 
sandhya exp resume
sandhya exp resume sandhya exp resume
sandhya exp resume
 
Ibm Optim Techical Overview 01282009
Ibm Optim Techical Overview 01282009Ibm Optim Techical Overview 01282009
Ibm Optim Techical Overview 01282009
 
Production Support_ETL_Informatica Developer_raja_velpula_5yrs
Production Support_ETL_Informatica Developer_raja_velpula_5yrsProduction Support_ETL_Informatica Developer_raja_velpula_5yrs
Production Support_ETL_Informatica Developer_raja_velpula_5yrs
 
Siva Kanagaraj Resume
Siva Kanagaraj ResumeSiva Kanagaraj Resume
Siva Kanagaraj Resume
 
Kumarswamy_ETL
Kumarswamy_ETLKumarswamy_ETL
Kumarswamy_ETL
 
Resume_David_Colbourn September 2016
Resume_David_Colbourn September 2016Resume_David_Colbourn September 2016
Resume_David_Colbourn September 2016
 
Abdul ETL Resume
Abdul ETL ResumeAbdul ETL Resume
Abdul ETL Resume
 
Resume - Abhishek Ray-Mar-2016 - Ind
Resume - Abhishek Ray-Mar-2016 - IndResume - Abhishek Ray-Mar-2016 - Ind
Resume - Abhishek Ray-Mar-2016 - Ind
 

Similar a Database Management | Why Data Warehouse Projects Fail

Performance Optimization: Incorporating Database and Code Optimzitation Into ...
Performance Optimization: Incorporating Database and Code Optimzitation Into ...Performance Optimization: Incorporating Database and Code Optimzitation Into ...
Performance Optimization: Incorporating Database and Code Optimzitation Into ...Michael Findling
 
Data Con LA 2022 - Why Data Quality vigilance requires an End-to-End, Automat...
Data Con LA 2022 - Why Data Quality vigilance requires an End-to-End, Automat...Data Con LA 2022 - Why Data Quality vigilance requires an End-to-End, Automat...
Data Con LA 2022 - Why Data Quality vigilance requires an End-to-End, Automat...Data Con LA
 
Engineering Machine Learning Data Pipelines Series: Big Data Quality - Cleans...
Engineering Machine Learning Data Pipelines Series: Big Data Quality - Cleans...Engineering Machine Learning Data Pipelines Series: Big Data Quality - Cleans...
Engineering Machine Learning Data Pipelines Series: Big Data Quality - Cleans...Precisely
 
Engineering Machine Learning Data Pipelines Series: Tracking Data Lineage fro...
Engineering Machine Learning Data Pipelines Series: Tracking Data Lineage fro...Engineering Machine Learning Data Pipelines Series: Tracking Data Lineage fro...
Engineering Machine Learning Data Pipelines Series: Tracking Data Lineage fro...Precisely
 
Neoaug 2013 critical success factors for data quality management-chain-sys-co...
Neoaug 2013 critical success factors for data quality management-chain-sys-co...Neoaug 2013 critical success factors for data quality management-chain-sys-co...
Neoaug 2013 critical success factors for data quality management-chain-sys-co...Chain Sys Corporation
 
Unravel for azure databricks overview 4 28-20 final
Unravel for azure databricks overview 4 28-20 finalUnravel for azure databricks overview 4 28-20 final
Unravel for azure databricks overview 4 28-20 finalDevOps.com
 
Lecture 23
Lecture 23Lecture 23
Lecture 23Shani729
 
Os Mcgrattan
Os McgrattanOs Mcgrattan
Os Mcgrattanoscon2007
 
Reducing The Time And Costs Associated With Sarbanes Oxley Compliance
Reducing The Time And Costs Associated With Sarbanes Oxley ComplianceReducing The Time And Costs Associated With Sarbanes Oxley Compliance
Reducing The Time And Costs Associated With Sarbanes Oxley ComplianceMichael Findling
 
Optimizing Your Database Performance | Embarcadero Technologies
Optimizing Your Database Performance | Embarcadero TechnologiesOptimizing Your Database Performance | Embarcadero Technologies
Optimizing Your Database Performance | Embarcadero TechnologiesEmbarcadero Technologies
 
Optimizing Your Database Performance | Embarcadero Technologies
Optimizing Your Database Performance | Embarcadero TechnologiesOptimizing Your Database Performance | Embarcadero Technologies
Optimizing Your Database Performance | Embarcadero TechnologiesMichael Findling
 
Presentation application change management and data masking strategies for ...
Presentation   application change management and data masking strategies for ...Presentation   application change management and data masking strategies for ...
Presentation application change management and data masking strategies for ...xKinAnx
 
Mani_Sagar_ETL
Mani_Sagar_ETLMani_Sagar_ETL
Mani_Sagar_ETLMani Sagar
 
Keeping the Pulse of Your Data:  Why You Need Data Observability 
Keeping the Pulse of Your Data:  Why You Need Data Observability Keeping the Pulse of Your Data:  Why You Need Data Observability 
Keeping the Pulse of Your Data:  Why You Need Data Observability Precisely
 

Similar a Database Management | Why Data Warehouse Projects Fail (20)

Performance Optimization: Incorporating Database and Code Optimzitation Into ...
Performance Optimization: Incorporating Database and Code Optimzitation Into ...Performance Optimization: Incorporating Database and Code Optimzitation Into ...
Performance Optimization: Incorporating Database and Code Optimzitation Into ...
 
dq_fail.pdf
dq_fail.pdfdq_fail.pdf
dq_fail.pdf
 
Data Con LA 2022 - Why Data Quality vigilance requires an End-to-End, Automat...
Data Con LA 2022 - Why Data Quality vigilance requires an End-to-End, Automat...Data Con LA 2022 - Why Data Quality vigilance requires an End-to-End, Automat...
Data Con LA 2022 - Why Data Quality vigilance requires an End-to-End, Automat...
 
Engineering Machine Learning Data Pipelines Series: Big Data Quality - Cleans...
Engineering Machine Learning Data Pipelines Series: Big Data Quality - Cleans...Engineering Machine Learning Data Pipelines Series: Big Data Quality - Cleans...
Engineering Machine Learning Data Pipelines Series: Big Data Quality - Cleans...
 
Engineering Machine Learning Data Pipelines Series: Tracking Data Lineage fro...
Engineering Machine Learning Data Pipelines Series: Tracking Data Lineage fro...Engineering Machine Learning Data Pipelines Series: Tracking Data Lineage fro...
Engineering Machine Learning Data Pipelines Series: Tracking Data Lineage fro...
 
WHYDODQFAILS.pdf
WHYDODQFAILS.pdfWHYDODQFAILS.pdf
WHYDODQFAILS.pdf
 
Neoaug 2013 critical success factors for data quality management-chain-sys-co...
Neoaug 2013 critical success factors for data quality management-chain-sys-co...Neoaug 2013 critical success factors for data quality management-chain-sys-co...
Neoaug 2013 critical success factors for data quality management-chain-sys-co...
 
Unravel for azure databricks overview 4 28-20 final
Unravel for azure databricks overview 4 28-20 finalUnravel for azure databricks overview 4 28-20 final
Unravel for azure databricks overview 4 28-20 final
 
Lecture 23
Lecture 23Lecture 23
Lecture 23
 
Data Architecture Success Stories
Data Architecture Success StoriesData Architecture Success Stories
Data Architecture Success Stories
 
Os Mcgrattan
Os McgrattanOs Mcgrattan
Os Mcgrattan
 
Mallikarjun_Konduri
Mallikarjun_KonduriMallikarjun_Konduri
Mallikarjun_Konduri
 
Reducing The Time And Costs Associated With Sarbanes Oxley Compliance
Reducing The Time And Costs Associated With Sarbanes Oxley ComplianceReducing The Time And Costs Associated With Sarbanes Oxley Compliance
Reducing The Time And Costs Associated With Sarbanes Oxley Compliance
 
Optimizing Your Database Performance | Embarcadero Technologies
Optimizing Your Database Performance | Embarcadero TechnologiesOptimizing Your Database Performance | Embarcadero Technologies
Optimizing Your Database Performance | Embarcadero Technologies
 
Optimizing Your Database Performance | Embarcadero Technologies
Optimizing Your Database Performance | Embarcadero TechnologiesOptimizing Your Database Performance | Embarcadero Technologies
Optimizing Your Database Performance | Embarcadero Technologies
 
Presentation application change management and data masking strategies for ...
Presentation   application change management and data masking strategies for ...Presentation   application change management and data masking strategies for ...
Presentation application change management and data masking strategies for ...
 
Jithender_3+Years_Exp_ETL Testing
Jithender_3+Years_Exp_ETL TestingJithender_3+Years_Exp_ETL Testing
Jithender_3+Years_Exp_ETL Testing
 
Mani_Sagar_ETL
Mani_Sagar_ETLMani_Sagar_ETL
Mani_Sagar_ETL
 
CV_Mike Yan
CV_Mike YanCV_Mike Yan
CV_Mike Yan
 
Keeping the Pulse of Your Data:  Why You Need Data Observability 
Keeping the Pulse of Your Data:  Why You Need Data Observability Keeping the Pulse of Your Data:  Why You Need Data Observability 
Keeping the Pulse of Your Data:  Why You Need Data Observability 
 

Más de Michael Findling

Senior Marketing Manager: Channels & Strategic accounts
Senior Marketing Manager:  Channels & Strategic accountsSenior Marketing Manager:  Channels & Strategic accounts
Senior Marketing Manager: Channels & Strategic accountsMichael Findling
 
Spark Job Description: Development Director
Spark Job Description: Development DirectorSpark Job Description: Development Director
Spark Job Description: Development DirectorMichael Findling
 
100 Things to Watch in 2011 from JWT
100 Things to Watch in 2011 from JWT100 Things to Watch in 2011 from JWT
100 Things to Watch in 2011 from JWTMichael Findling
 
Social Media Marketing Metrics That Matter
Social Media Marketing Metrics That MatterSocial Media Marketing Metrics That Matter
Social Media Marketing Metrics That MatterMichael Findling
 
Tutorpedia Foundation Silent Auction Item List – February 23, 2011
Tutorpedia Foundation Silent Auction Item List – February 23, 2011Tutorpedia Foundation Silent Auction Item List – February 23, 2011
Tutorpedia Foundation Silent Auction Item List – February 23, 2011Michael Findling
 
Website Marketing Seminar 2009
Website Marketing Seminar 2009Website Marketing Seminar 2009
Website Marketing Seminar 2009Michael Findling
 
Reducing Total Cost of Ownership for Database and Developer Software
Reducing Total Cost of Ownership for Database and Developer SoftwareReducing Total Cost of Ownership for Database and Developer Software
Reducing Total Cost of Ownership for Database and Developer SoftwareMichael Findling
 
Reasons to migrate from Delphi 7 to Delphi 2009
Reasons to migrate from Delphi 7  to Delphi 2009Reasons to migrate from Delphi 7  to Delphi 2009
Reasons to migrate from Delphi 7 to Delphi 2009Michael Findling
 
Migrating from CA AllFusionTM ERwin® Data Modeler to ER/Studio
Migrating from CA AllFusionTM ERwin® Data Modeler to ER/StudioMigrating from CA AllFusionTM ERwin® Data Modeler to ER/Studio
Migrating from CA AllFusionTM ERwin® Data Modeler to ER/StudioMichael Findling
 
Java Optimization For Faster Code & Better Results | J Optimizer
Java Optimization For Faster Code & Better Results | J OptimizerJava Optimization For Faster Code & Better Results | J Optimizer
Java Optimization For Faster Code & Better Results | J OptimizerMichael Findling
 
Migrating from CA AllFusionTM ERwin® Data Modeler to ER/Studio®
Migrating from CA AllFusionTM ERwin® Data Modeler to ER/Studio®Migrating from CA AllFusionTM ERwin® Data Modeler to ER/Studio®
Migrating from CA AllFusionTM ERwin® Data Modeler to ER/Studio®Michael Findling
 
Top Ten Reasons to Upgrade from Delphi 7
Top Ten Reasons to Upgrade from Delphi 7Top Ten Reasons to Upgrade from Delphi 7
Top Ten Reasons to Upgrade from Delphi 7Michael Findling
 
Database Tools and Developer Software Licence Management
Database Tools and Developer Software Licence ManagementDatabase Tools and Developer Software Licence Management
Database Tools and Developer Software Licence ManagementMichael Findling
 
Database Design and Data Modeling | PowerDesigner to All Access
Database Design and Data Modeling | PowerDesigner to All AccessDatabase Design and Data Modeling | PowerDesigner to All Access
Database Design and Data Modeling | PowerDesigner to All AccessMichael Findling
 
Preventing Database Perfomance Issues | DB Optimizer
Preventing Database Perfomance Issues | DB OptimizerPreventing Database Perfomance Issues | DB Optimizer
Preventing Database Perfomance Issues | DB OptimizerMichael Findling
 
Build Windows Applications Fast | Delphi Prism
Build Windows Applications Fast | Delphi PrismBuild Windows Applications Fast | Delphi Prism
Build Windows Applications Fast | Delphi PrismMichael Findling
 
Develop Enterprise Java Applications | JBuilder
Develop Enterprise Java Applications | JBuilderDevelop Enterprise Java Applications | JBuilder
Develop Enterprise Java Applications | JBuilderMichael Findling
 
Develop Ruby Applications Fast | TubroRuby
Develop Ruby Applications Fast | TubroRubyDevelop Ruby Applications Fast | TubroRuby
Develop Ruby Applications Fast | TubroRubyMichael Findling
 
Develop Ruby Applications Fast | TubroRuby
Develop Ruby Applications Fast | TubroRubyDevelop Ruby Applications Fast | TubroRuby
Develop Ruby Applications Fast | TubroRubyMichael Findling
 
Business Process Modeling | Embarcadero Technologies EA/Studio
Business Process Modeling | Embarcadero Technologies EA/StudioBusiness Process Modeling | Embarcadero Technologies EA/Studio
Business Process Modeling | Embarcadero Technologies EA/StudioMichael Findling
 

Más de Michael Findling (20)

Senior Marketing Manager: Channels & Strategic accounts
Senior Marketing Manager:  Channels & Strategic accountsSenior Marketing Manager:  Channels & Strategic accounts
Senior Marketing Manager: Channels & Strategic accounts
 
Spark Job Description: Development Director
Spark Job Description: Development DirectorSpark Job Description: Development Director
Spark Job Description: Development Director
 
100 Things to Watch in 2011 from JWT
100 Things to Watch in 2011 from JWT100 Things to Watch in 2011 from JWT
100 Things to Watch in 2011 from JWT
 
Social Media Marketing Metrics That Matter
Social Media Marketing Metrics That MatterSocial Media Marketing Metrics That Matter
Social Media Marketing Metrics That Matter
 
Tutorpedia Foundation Silent Auction Item List – February 23, 2011
Tutorpedia Foundation Silent Auction Item List – February 23, 2011Tutorpedia Foundation Silent Auction Item List – February 23, 2011
Tutorpedia Foundation Silent Auction Item List – February 23, 2011
 
Website Marketing Seminar 2009
Website Marketing Seminar 2009Website Marketing Seminar 2009
Website Marketing Seminar 2009
 
Reducing Total Cost of Ownership for Database and Developer Software
Reducing Total Cost of Ownership for Database and Developer SoftwareReducing Total Cost of Ownership for Database and Developer Software
Reducing Total Cost of Ownership for Database and Developer Software
 
Reasons to migrate from Delphi 7 to Delphi 2009
Reasons to migrate from Delphi 7  to Delphi 2009Reasons to migrate from Delphi 7  to Delphi 2009
Reasons to migrate from Delphi 7 to Delphi 2009
 
Migrating from CA AllFusionTM ERwin® Data Modeler to ER/Studio
Migrating from CA AllFusionTM ERwin® Data Modeler to ER/StudioMigrating from CA AllFusionTM ERwin® Data Modeler to ER/Studio
Migrating from CA AllFusionTM ERwin® Data Modeler to ER/Studio
 
Java Optimization For Faster Code & Better Results | J Optimizer
Java Optimization For Faster Code & Better Results | J OptimizerJava Optimization For Faster Code & Better Results | J Optimizer
Java Optimization For Faster Code & Better Results | J Optimizer
 
Migrating from CA AllFusionTM ERwin® Data Modeler to ER/Studio®
Migrating from CA AllFusionTM ERwin® Data Modeler to ER/Studio®Migrating from CA AllFusionTM ERwin® Data Modeler to ER/Studio®
Migrating from CA AllFusionTM ERwin® Data Modeler to ER/Studio®
 
Top Ten Reasons to Upgrade from Delphi 7
Top Ten Reasons to Upgrade from Delphi 7Top Ten Reasons to Upgrade from Delphi 7
Top Ten Reasons to Upgrade from Delphi 7
 
Database Tools and Developer Software Licence Management
Database Tools and Developer Software Licence ManagementDatabase Tools and Developer Software Licence Management
Database Tools and Developer Software Licence Management
 
Database Design and Data Modeling | PowerDesigner to All Access
Database Design and Data Modeling | PowerDesigner to All AccessDatabase Design and Data Modeling | PowerDesigner to All Access
Database Design and Data Modeling | PowerDesigner to All Access
 
Preventing Database Perfomance Issues | DB Optimizer
Preventing Database Perfomance Issues | DB OptimizerPreventing Database Perfomance Issues | DB Optimizer
Preventing Database Perfomance Issues | DB Optimizer
 
Build Windows Applications Fast | Delphi Prism
Build Windows Applications Fast | Delphi PrismBuild Windows Applications Fast | Delphi Prism
Build Windows Applications Fast | Delphi Prism
 
Develop Enterprise Java Applications | JBuilder
Develop Enterprise Java Applications | JBuilderDevelop Enterprise Java Applications | JBuilder
Develop Enterprise Java Applications | JBuilder
 
Develop Ruby Applications Fast | TubroRuby
Develop Ruby Applications Fast | TubroRubyDevelop Ruby Applications Fast | TubroRuby
Develop Ruby Applications Fast | TubroRuby
 
Develop Ruby Applications Fast | TubroRuby
Develop Ruby Applications Fast | TubroRubyDevelop Ruby Applications Fast | TubroRuby
Develop Ruby Applications Fast | TubroRuby
 
Business Process Modeling | Embarcadero Technologies EA/Studio
Business Process Modeling | Embarcadero Technologies EA/StudioBusiness Process Modeling | Embarcadero Technologies EA/Studio
Business Process Modeling | Embarcadero Technologies EA/Studio
 

Último

VoIP Service and Marketing using Odoo and Asterisk PBX
VoIP Service and Marketing using Odoo and Asterisk PBXVoIP Service and Marketing using Odoo and Asterisk PBX
VoIP Service and Marketing using Odoo and Asterisk PBXTarek Kalaji
 
Computer 10: Lesson 10 - Online Crimes and Hazards
Computer 10: Lesson 10 - Online Crimes and HazardsComputer 10: Lesson 10 - Online Crimes and Hazards
Computer 10: Lesson 10 - Online Crimes and HazardsSeth Reyes
 
Igniting Next Level Productivity with AI-Infused Data Integration Workflows
Igniting Next Level Productivity with AI-Infused Data Integration WorkflowsIgniting Next Level Productivity with AI-Infused Data Integration Workflows
Igniting Next Level Productivity with AI-Infused Data Integration WorkflowsSafe Software
 
Introduction to Matsuo Laboratory (ENG).pptx
Introduction to Matsuo Laboratory (ENG).pptxIntroduction to Matsuo Laboratory (ENG).pptx
Introduction to Matsuo Laboratory (ENG).pptxMatsuo Lab
 
IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019
IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019
IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019IES VE
 
All in AI: LLM Landscape & RAG in 2024 with Mark Ryan (Google) & Jerry Liu (L...
All in AI: LLM Landscape & RAG in 2024 with Mark Ryan (Google) & Jerry Liu (L...All in AI: LLM Landscape & RAG in 2024 with Mark Ryan (Google) & Jerry Liu (L...
All in AI: LLM Landscape & RAG in 2024 with Mark Ryan (Google) & Jerry Liu (L...Daniel Zivkovic
 
UiPath Solutions Management Preview - Northern CA Chapter - March 22.pdf
UiPath Solutions Management Preview - Northern CA Chapter - March 22.pdfUiPath Solutions Management Preview - Northern CA Chapter - March 22.pdf
UiPath Solutions Management Preview - Northern CA Chapter - March 22.pdfDianaGray10
 
UiPath Studio Web workshop series - Day 6
UiPath Studio Web workshop series - Day 6UiPath Studio Web workshop series - Day 6
UiPath Studio Web workshop series - Day 6DianaGray10
 
UiPath Studio Web workshop series - Day 8
UiPath Studio Web workshop series - Day 8UiPath Studio Web workshop series - Day 8
UiPath Studio Web workshop series - Day 8DianaGray10
 
Comparing Sidecar-less Service Mesh from Cilium and Istio
Comparing Sidecar-less Service Mesh from Cilium and IstioComparing Sidecar-less Service Mesh from Cilium and Istio
Comparing Sidecar-less Service Mesh from Cilium and IstioChristian Posta
 
OpenShift Commons Paris - Choose Your Own Observability Adventure
OpenShift Commons Paris - Choose Your Own Observability AdventureOpenShift Commons Paris - Choose Your Own Observability Adventure
OpenShift Commons Paris - Choose Your Own Observability AdventureEric D. Schabell
 
Using IESVE for Loads, Sizing and Heat Pump Modeling to Achieve Decarbonization
Using IESVE for Loads, Sizing and Heat Pump Modeling to Achieve DecarbonizationUsing IESVE for Loads, Sizing and Heat Pump Modeling to Achieve Decarbonization
Using IESVE for Loads, Sizing and Heat Pump Modeling to Achieve DecarbonizationIES VE
 
UiPath Platform: The Backend Engine Powering Your Automation - Session 1
UiPath Platform: The Backend Engine Powering Your Automation - Session 1UiPath Platform: The Backend Engine Powering Your Automation - Session 1
UiPath Platform: The Backend Engine Powering Your Automation - Session 1DianaGray10
 
Crea il tuo assistente AI con lo Stregatto (open source python framework)
Crea il tuo assistente AI con lo Stregatto (open source python framework)Crea il tuo assistente AI con lo Stregatto (open source python framework)
Crea il tuo assistente AI con lo Stregatto (open source python framework)Commit University
 
COMPUTER 10 Lesson 8 - Building a Website
COMPUTER 10 Lesson 8 - Building a WebsiteCOMPUTER 10 Lesson 8 - Building a Website
COMPUTER 10 Lesson 8 - Building a Websitedgelyza
 
Meet the new FSP 3000 M-Flex800™
Meet the new FSP 3000 M-Flex800™Meet the new FSP 3000 M-Flex800™
Meet the new FSP 3000 M-Flex800™Adtran
 
Apres-Cyber - The Data Dilemma: Bridging Offensive Operations and Machine Lea...
Apres-Cyber - The Data Dilemma: Bridging Offensive Operations and Machine Lea...Apres-Cyber - The Data Dilemma: Bridging Offensive Operations and Machine Lea...
Apres-Cyber - The Data Dilemma: Bridging Offensive Operations and Machine Lea...Will Schroeder
 
How Accurate are Carbon Emissions Projections?
How Accurate are Carbon Emissions Projections?How Accurate are Carbon Emissions Projections?
How Accurate are Carbon Emissions Projections?IES VE
 
UWB Technology for Enhanced Indoor and Outdoor Positioning in Physiological M...
UWB Technology for Enhanced Indoor and Outdoor Positioning in Physiological M...UWB Technology for Enhanced Indoor and Outdoor Positioning in Physiological M...
UWB Technology for Enhanced Indoor and Outdoor Positioning in Physiological M...UbiTrack UK
 
Videogame localization & technology_ how to enhance the power of translation.pdf
Videogame localization & technology_ how to enhance the power of translation.pdfVideogame localization & technology_ how to enhance the power of translation.pdf
Videogame localization & technology_ how to enhance the power of translation.pdfinfogdgmi
 

Último (20)

VoIP Service and Marketing using Odoo and Asterisk PBX
VoIP Service and Marketing using Odoo and Asterisk PBXVoIP Service and Marketing using Odoo and Asterisk PBX
VoIP Service and Marketing using Odoo and Asterisk PBX
 
Computer 10: Lesson 10 - Online Crimes and Hazards
Computer 10: Lesson 10 - Online Crimes and HazardsComputer 10: Lesson 10 - Online Crimes and Hazards
Computer 10: Lesson 10 - Online Crimes and Hazards
 
Igniting Next Level Productivity with AI-Infused Data Integration Workflows
Igniting Next Level Productivity with AI-Infused Data Integration WorkflowsIgniting Next Level Productivity with AI-Infused Data Integration Workflows
Igniting Next Level Productivity with AI-Infused Data Integration Workflows
 
Introduction to Matsuo Laboratory (ENG).pptx
Introduction to Matsuo Laboratory (ENG).pptxIntroduction to Matsuo Laboratory (ENG).pptx
Introduction to Matsuo Laboratory (ENG).pptx
 
IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019
IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019
IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019
 
All in AI: LLM Landscape & RAG in 2024 with Mark Ryan (Google) & Jerry Liu (L...
All in AI: LLM Landscape & RAG in 2024 with Mark Ryan (Google) & Jerry Liu (L...All in AI: LLM Landscape & RAG in 2024 with Mark Ryan (Google) & Jerry Liu (L...
All in AI: LLM Landscape & RAG in 2024 with Mark Ryan (Google) & Jerry Liu (L...
 
UiPath Solutions Management Preview - Northern CA Chapter - March 22.pdf
UiPath Solutions Management Preview - Northern CA Chapter - March 22.pdfUiPath Solutions Management Preview - Northern CA Chapter - March 22.pdf
UiPath Solutions Management Preview - Northern CA Chapter - March 22.pdf
 
UiPath Studio Web workshop series - Day 6
UiPath Studio Web workshop series - Day 6UiPath Studio Web workshop series - Day 6
UiPath Studio Web workshop series - Day 6
 
UiPath Studio Web workshop series - Day 8
UiPath Studio Web workshop series - Day 8UiPath Studio Web workshop series - Day 8
UiPath Studio Web workshop series - Day 8
 
Comparing Sidecar-less Service Mesh from Cilium and Istio
Comparing Sidecar-less Service Mesh from Cilium and IstioComparing Sidecar-less Service Mesh from Cilium and Istio
Comparing Sidecar-less Service Mesh from Cilium and Istio
 
OpenShift Commons Paris - Choose Your Own Observability Adventure
OpenShift Commons Paris - Choose Your Own Observability AdventureOpenShift Commons Paris - Choose Your Own Observability Adventure
OpenShift Commons Paris - Choose Your Own Observability Adventure
 
Using IESVE for Loads, Sizing and Heat Pump Modeling to Achieve Decarbonization
Using IESVE for Loads, Sizing and Heat Pump Modeling to Achieve DecarbonizationUsing IESVE for Loads, Sizing and Heat Pump Modeling to Achieve Decarbonization
Using IESVE for Loads, Sizing and Heat Pump Modeling to Achieve Decarbonization
 
UiPath Platform: The Backend Engine Powering Your Automation - Session 1
UiPath Platform: The Backend Engine Powering Your Automation - Session 1UiPath Platform: The Backend Engine Powering Your Automation - Session 1
UiPath Platform: The Backend Engine Powering Your Automation - Session 1
 
Crea il tuo assistente AI con lo Stregatto (open source python framework)
Crea il tuo assistente AI con lo Stregatto (open source python framework)Crea il tuo assistente AI con lo Stregatto (open source python framework)
Crea il tuo assistente AI con lo Stregatto (open source python framework)
 
COMPUTER 10 Lesson 8 - Building a Website
COMPUTER 10 Lesson 8 - Building a WebsiteCOMPUTER 10 Lesson 8 - Building a Website
COMPUTER 10 Lesson 8 - Building a Website
 
Meet the new FSP 3000 M-Flex800™
Meet the new FSP 3000 M-Flex800™Meet the new FSP 3000 M-Flex800™
Meet the new FSP 3000 M-Flex800™
 
Apres-Cyber - The Data Dilemma: Bridging Offensive Operations and Machine Lea...
Apres-Cyber - The Data Dilemma: Bridging Offensive Operations and Machine Lea...Apres-Cyber - The Data Dilemma: Bridging Offensive Operations and Machine Lea...
Apres-Cyber - The Data Dilemma: Bridging Offensive Operations and Machine Lea...
 
How Accurate are Carbon Emissions Projections?
How Accurate are Carbon Emissions Projections?How Accurate are Carbon Emissions Projections?
How Accurate are Carbon Emissions Projections?
 
UWB Technology for Enhanced Indoor and Outdoor Positioning in Physiological M...
UWB Technology for Enhanced Indoor and Outdoor Positioning in Physiological M...UWB Technology for Enhanced Indoor and Outdoor Positioning in Physiological M...
UWB Technology for Enhanced Indoor and Outdoor Positioning in Physiological M...
 
Videogame localization & technology_ how to enhance the power of translation.pdf
Videogame localization & technology_ how to enhance the power of translation.pdfVideogame localization & technology_ how to enhance the power of translation.pdf
Videogame localization & technology_ how to enhance the power of translation.pdf
 

Database Management | Why Data Warehouse Projects Fail

  • 1. Tech Notes Why Data Warehouse Projects Fail Using Schema Examination Tools to Ensure Information Quality, Schema Compliance, and Project Success Embarcadero Technologies January 2008 Corporate Headquarters EMEA Headquarters Asia-Pacific Headquarters 100 California Street, 12th York House L7. 313 La Trobe Street Floor 18 York Road Melbourne VIC 3000 San Francisco, California Maidenhead, Berkshire Australia 94111 SL6 1SF, United Kingdom
  • 2. Why Data Warehouse Projects Fail According to a 2003 Gartner report, more than 50 percent of data warehouse projects failed, and the ones that survived were delivered very late with extremely high costs. In a 2007 study, Gartner predicted once more that 50 percent of data warehouse projects would have limited acceptance or be outright failures as a result of lack of attention to data quality issues. DATA QUALITY IS NOT ENOUGH Data Quality is one of the hottest topics in any IT shop. Although very important, Data Quality is far from being enough because decisions are based on information, not on data. Having quality data does not assure quality information. To have quality information, it is necessary to have quality data, but this is not sufficient on its own. We need more. IT IS ALL ABOUT THE DATABASE SCHEMA Information is produced by an application program that accesses data in a database, usually a relational database such as Oracle, DB2, Sybase, SQL Server, etc. The core of the database is the database schema, wherein are stored all the data definitions, the relationships between the data, and the business rules. The quality of the information depends on 3 things: (1) the quality of the data itself, (2) the quality of the application programs and (3) the quality of the database schema. Joe Celko (www.celko.com), a very well known expert and consultant in relational technology, states that without a quality database schema, it is very difficult to: • Achieve good program performance and • Deliver quality information When developing any database application, we must always ensure the database schema has integrity and consistency – and no flaws. This must be done when the schema is created, and every time it is changed. If the database schema has flaws, the information will be flawed and the Data Warehouse projects will fail. MODELING TOOLS ARE NOT ENOUGH Database schemas are normally created using a modeling tool such as ERwin®, ER/Studio®, or PowerDesigner®. These tools validate the data model for completeness of the model, but they do not have the intelligence to “debug” the data model. QUALITY OF FEEDER SYSTEMS Data Warehouse projects depend on feeder systems. If the database schemas of the feeder systems have flaws, the information produced by the data warehouse will not have quality. This is the major reason why data warehouse projects fail. The database Embarcadero Technologies -1-
  • 3. Why Data Warehouse Projects Fail schemas of the feeder systems must be validated for consistency, integrity and compliance to the rules of the relational technology before a data warehouse project is initiated. This is where Embarcadero® Schema Examiner™ comes in. SCHEMA EXAMINER Schema Examiner was created to fill this gap, providing a means to “debug” the schema. Schema Examiner provides over 50 diagnostics to assure the schema adheres to the relational model, is consistent and has integrity. Schema Examiner can validate the data model, a set of SQL/DDL scripts or the database schema directly. Schema Examiner can also compare schemas, indicating the differences. Manual validation is impossible due to the size and complexity of today’s database schemas. A SUCCESSFUL DATA WAREHOUSE PROJECT A corporation in the telecom business contracted with one of the major consulting companies to develop a large data warehouse project. The cost of the project was $10 Embarcadero Technologies -2-
  • 4. Why Data Warehouse Projects Fail million. After the project was in production, they discovered that the quality of the information was not good; many answers were inconsistent. They considered to re-do the entire project or even scrap it. Their committee suggested the hiring of a consultant. The consultant used Schema Examiner and after a couple of weeks of analyzing the feeder systems, he made several suggestions based upon the findings of Schema Examiner. Once the recommendations were adopted, the results improved dramatically and the project was a total success. The client has stated that the success of the project was due to the use of Schema Examiner. They immediately purchased an enterprise license and made it mandatory to use Schema Examiner in all their IT projects, internal or external. The project used 4 Oracle feeder system that were “debugged” using Schema Examiner; they were also compared to each other to discover inconsistencies. Below is a graphical representation of the project. SUMMARY • About half of all Data Warehouse projects fail due to poor data quality (Gartner Group) • Data Quality is not enough - decisions are based on information quality, not on data quality • A flawed schema impacts negatively on information quality • Database schemas must be validated for compliance with the rules of relational technology • Modeling tools validate data models / schemas for completeness, not for compliance • Data Warehouse feeder systems schemas must be validated for compliance • Schema Examiner validates schemas from feeder systems and compares them to verify inconsistencies Embarcadero Technologies -3-
  • 5. Embarcadero Technologies, Inc. is a leading provider of award-winning tools for application developers and database professionals so they can design systems right, build them faster and run them better, regardless of their platform or programming language. Ninety of the Fortune 100 and an active community of more than three million users worldwide rely on Embarcadero products to increase productivity, reduce costs, simplify change management and compliance and accelerate innovation. The company’s flagship tools include: Embarcadero® Change Manager™, CodeGear™ RAD Studio, DBArtisan®, Delphi®, ER/Studio®, JBuilder® and Rapid SQL®. Founded in 1993, Embarcadero is headquartered in San Francisco, with offices located around the world. Embarcadero is online at www.embarcadero.com.