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How do you manage source system data quality related risk in BI projects?

In a Business Intelligence project, source system data quality plays a crucial role for project success. However good is your data modeling and coding - all goes in vain unless the source system data quality supports your application. How will you manage the risks pertaining to source system data quality in BI project?
Posted by Santanu on May 21, 2011 06:52AM
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RE: How do you manage source system data quality related risk in BI projects?

Certainly. Terminology is another challenge. In a big corporation, a term means one thing in one department and another in another department. Like the term "balance", does it mean actual balance, book balance or available balance? Standardizing all the terms is a great effort. 
Janet Yu at 5/26/2011 5:54:50 AM
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(1 ratings)

RE: How do you manage source system data quality related risk in BI projects?

It depends on the scope of the BI Project. If it only focus on the development and implementation of a BI system, then there could be issues such as data quality issues from source systems.

The implementation of BI systems should be the result of a Information/Data Management project where the state of current data governance is assessed across all source systems and then to implement the tools and processes to improve the overall data governance going forward. One of the key deliverables from this Information/Data project is to deliver a BI/DW system which should provide the foundation for data governance. The data from source systems should therefore also be accurate and complete due to the fact that it complies with the overall data governance framework.
Kobus Bouman at 5/26/2011 7:42:32 PM
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(1 ratings)

RE: How do you manage source system data quality related risk in BI projects?

Data management / quality is one of the major challenges for organisations in any industry (and especially government) in the next decade, in view of the overwhelming petabytes of available information on zillions of platforms. 
For BI projects, it is crucial to tag the original source data so that one can always return to it when required.  It is similar to the CAATs concept (ACL, IDEA, etc.) where IT auditors load the original source data only once, and never touch it from an integrity point.   After loading, they can play with the data as much as they want, they can never change it, but still obtain impressive results.
So for any BI project, scrutinizing the original source data is essential for success.  Making sure that users can always find out which data was original source data, and which was already manipulated/played data, in an easy visual manner is an attention point.
In my current organization (IT unit working for the government), 6 people are researching and studying data quality on a constant basis with practical advice and results for the government. 
Believe me that they are still working hard on this topic.
Marc Vael at 6/4/2011 3:29:07 AM
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(1 ratings)

RE: How do you manage source system data quality related risk in BI projects?

Thanks Marc, Kobus and Janet for your excellent pointers for the discussions. So far what has come up are as follows:

  • Sourcing the data from original source is important
  • Scrutinizing original source data is crucial for project success
  • Researching and studying source data on continuous basis can be beneficial
  • Forming foundation of Data Governance is crucial for success of the project on a ongoing basis. Through proper data governance can churn out accurate and complete source system data - a must ingredient of BI project success
  • Standardizing terminology difference across departments is important step in managing source system data for BI project

Request all members to bring in their experience by contributing more pointers to take this discussion forward.

Santanu at 6/10/2011 2:04:22 AM
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(Unrated)

RE: How do you manage source system data quality related risk in BI projects?

Here I'd like to share a document from Computerworld Hong Kong - "BI & Analytics User Guide: Make Your Business Smarter" which can be found at http://www.cw.com.hk/content/cwhk-bi-analytics-user-guide-make-your-business-smarter.

There are several case studies from Hong Kong on BI adoption. It has also included  an article from CIO.com on Barriers to BI adoption. As with all projects, people and culture are the major barriers.
Janet Yu at 6/18/2011 2:43:47 AM
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(1 ratings)

RE: How do you manage source system data quality related risk in BI projects?

Thanks Janet for sharing the informative article on BI. Much appreciate your time in contributing to the discussions.
Santanu at 7/7/2011 7:32:02 AM
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(Unrated)

RE: How do you manage source system data quality related risk in BI projects?

Just found a podcast on BI project management tips for implementation success:

http://media.techtarget.com/audioCast/ENTERPRISE_APPS/sDM_BIPM_Newell_112309.mp3

In this 17-minute podcast, listeners will learn:

  • How BI project management requirements differ from those of other technologies (0:47)
  • The unique challenges that BI implementations create for project managers (2:11)
  • How to prevent "scope creep" from business end users (4:18)
  • Common mistakes made by project managers during BI implementations, and how to avoid the pitfalls (10:53)
  • Core BI project management skills that will still be important in five and 10 years from now (12:55)
Janet Yu at 8/20/2011 3:01:06 AM
You must sign in to rate content.
(Unrated)

RE: How do you manage source system data quality related risk in BI projects?

Just found a podcast on BI project management tips for implementation success:

http://media.techtarget.com/audioCast/ENTERPRISE_APPS/sDM_BIPM_Newell_112309.mp3

In this 17-minute podcast, listeners will learn:

  • How BI project management requirements differ from those of other technologies (0:47)
  • The unique challenges that BI implementations create for project managers (2:11)
  • How to prevent "scope creep" from business end users (4:18)
  • Common mistakes made by project managers during BI implementations, and how to avoid the pitfalls (10:53)
  • Core BI project management skills that will still be important in five and 10 years from now (12:55)
Janet Yu at 8/20/2011 3:01:06 AM
You must sign in to rate content.
(Unrated)

RE: How do you manage source system data quality related risk in BI projects?

Thanks Janet for sharing the informative article on BI. Much appreciate your time in contributing to the discussions.
Santanu at 7/7/2011 7:32:02 AM
You must sign in to rate content.
(Unrated)

RE: How do you manage source system data quality related risk in BI projects?

Here I'd like to share a document from Computerworld Hong Kong - "BI & Analytics User Guide: Make Your Business Smarter" which can be found at http://www.cw.com.hk/content/cwhk-bi-analytics-user-guide-make-your-business-smarter.

There are several case studies from Hong Kong on BI adoption. It has also included  an article from CIO.com on Barriers to BI adoption. As with all projects, people and culture are the major barriers.
Janet Yu at 6/18/2011 2:43:47 AM
You must sign in to rate content.
(1 ratings)

RE: How do you manage source system data quality related risk in BI projects?

Thanks Marc, Kobus and Janet for your excellent pointers for the discussions. So far what has come up are as follows:

  • Sourcing the data from original source is important
  • Scrutinizing original source data is crucial for project success
  • Researching and studying source data on continuous basis can be beneficial
  • Forming foundation of Data Governance is crucial for success of the project on a ongoing basis. Through proper data governance can churn out accurate and complete source system data - a must ingredient of BI project success
  • Standardizing terminology difference across departments is important step in managing source system data for BI project

Request all members to bring in their experience by contributing more pointers to take this discussion forward.

Santanu at 6/10/2011 2:04:22 AM
You must sign in to rate content.
(Unrated)

RE: How do you manage source system data quality related risk in BI projects?

Data management / quality is one of the major challenges for organisations in any industry (and especially government) in the next decade, in view of the overwhelming petabytes of available information on zillions of platforms. 
For BI projects, it is crucial to tag the original source data so that one can always return to it when required.  It is similar to the CAATs concept (ACL, IDEA, etc.) where IT auditors load the original source data only once, and never touch it from an integrity point.   After loading, they can play with the data as much as they want, they can never change it, but still obtain impressive results.
So for any BI project, scrutinizing the original source data is essential for success.  Making sure that users can always find out which data was original source data, and which was already manipulated/played data, in an easy visual manner is an attention point.
In my current organization (IT unit working for the government), 6 people are researching and studying data quality on a constant basis with practical advice and results for the government. 
Believe me that they are still working hard on this topic.
Marc Vael at 6/4/2011 3:29:07 AM
You must sign in to rate content.
(1 ratings)

RE: How do you manage source system data quality related risk in BI projects?

It depends on the scope of the BI Project. If it only focus on the development and implementation of a BI system, then there could be issues such as data quality issues from source systems.

The implementation of BI systems should be the result of a Information/Data Management project where the state of current data governance is assessed across all source systems and then to implement the tools and processes to improve the overall data governance going forward. One of the key deliverables from this Information/Data project is to deliver a BI/DW system which should provide the foundation for data governance. The data from source systems should therefore also be accurate and complete due to the fact that it complies with the overall data governance framework.
Kobus Bouman at 5/26/2011 7:42:32 PM
You must sign in to rate content.
(1 ratings)

RE: How do you manage source system data quality related risk in BI projects?

Certainly. Terminology is another challenge. In a big corporation, a term means one thing in one department and another in another department. Like the term "balance", does it mean actual balance, book balance or available balance? Standardizing all the terms is a great effort. 
Janet Yu at 5/26/2011 5:54:50 AM
You must sign in to rate content.
(1 ratings)

RE: How do you manage source system data quality related risk in BI projects?

It depends on the scope of the BI Project. If it only focus on the development and implementation of a BI system, then there could be issues such as data quality issues from source systems.

The implementation of BI systems should be the result of a Information/Data Management project where the state of current data governance is assessed across all source systems and then to implement the tools and processes to improve the overall data governance going forward. One of the key deliverables from this Information/Data project is to deliver a BI/DW system which should provide the foundation for data governance. The data from source systems should therefore also be accurate and complete due to the fact that it complies with the overall data governance framework.
Kobus Bouman at 5/26/2011 7:42:32 PM
You must sign in to rate content.
(1 ratings)

RE: How do you manage source system data quality related risk in BI projects?

Data management / quality is one of the major challenges for organisations in any industry (and especially government) in the next decade, in view of the overwhelming petabytes of available information on zillions of platforms. 
For BI projects, it is crucial to tag the original source data so that one can always return to it when required.  It is similar to the CAATs concept (ACL, IDEA, etc.) where IT auditors load the original source data only once, and never touch it from an integrity point.   After loading, they can play with the data as much as they want, they can never change it, but still obtain impressive results.
So for any BI project, scrutinizing the original source data is essential for success.  Making sure that users can always find out which data was original source data, and which was already manipulated/played data, in an easy visual manner is an attention point.
In my current organization (IT unit working for the government), 6 people are researching and studying data quality on a constant basis with practical advice and results for the government. 
Believe me that they are still working hard on this topic.
Marc Vael at 6/4/2011 3:29:07 AM
You must sign in to rate content.
(1 ratings)

RE: How do you manage source system data quality related risk in BI projects?

Here I'd like to share a document from Computerworld Hong Kong - "BI & Analytics User Guide: Make Your Business Smarter" which can be found at http://www.cw.com.hk/content/cwhk-bi-analytics-user-guide-make-your-business-smarter.

There are several case studies from Hong Kong on BI adoption. It has also included  an article from CIO.com on Barriers to BI adoption. As with all projects, people and culture are the major barriers.
Janet Yu at 6/18/2011 2:43:47 AM
You must sign in to rate content.
(1 ratings)

RE: How do you manage source system data quality related risk in BI projects?

Certainly. Terminology is another challenge. In a big corporation, a term means one thing in one department and another in another department. Like the term "balance", does it mean actual balance, book balance or available balance? Standardizing all the terms is a great effort. 
Janet Yu at 5/26/2011 5:54:50 AM
You must sign in to rate content.
(1 ratings)

RE: How do you manage source system data quality related risk in BI projects?

Thanks Marc, Kobus and Janet for your excellent pointers for the discussions. So far what has come up are as follows:

  • Sourcing the data from original source is important
  • Scrutinizing original source data is crucial for project success
  • Researching and studying source data on continuous basis can be beneficial
  • Forming foundation of Data Governance is crucial for success of the project on a ongoing basis. Through proper data governance can churn out accurate and complete source system data - a must ingredient of BI project success
  • Standardizing terminology difference across departments is important step in managing source system data for BI project

Request all members to bring in their experience by contributing more pointers to take this discussion forward.

Santanu at 6/10/2011 2:04:22 AM
You must sign in to rate content.
(Unrated)

RE: How do you manage source system data quality related risk in BI projects?

Thanks Janet for sharing the informative article on BI. Much appreciate your time in contributing to the discussions.
Santanu at 7/7/2011 7:32:02 AM
You must sign in to rate content.
(Unrated)

RE: How do you manage source system data quality related risk in BI projects?

Just found a podcast on BI project management tips for implementation success:

http://media.techtarget.com/audioCast/ENTERPRISE_APPS/sDM_BIPM_Newell_112309.mp3

In this 17-minute podcast, listeners will learn:

  • How BI project management requirements differ from those of other technologies (0:47)
  • The unique challenges that BI implementations create for project managers (2:11)
  • How to prevent "scope creep" from business end users (4:18)
  • Common mistakes made by project managers during BI implementations, and how to avoid the pitfalls (10:53)
  • Core BI project management skills that will still be important in five and 10 years from now (12:55)
Janet Yu at 8/20/2011 3:01:06 AM
You must sign in to rate content.
(Unrated)

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