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Sponsorship for Data Quality.

We had some issues in the past on data quality. This resulted in some customer impact, but not a company wide impact and got us a sponsorship on a limited data quality program. However the challenge for sponsorship for Data Quality Program remains. How can we enhance the level of sponsorhip for a data quality initiative?
 
This page of 'Ask a question' is linked to:  Data Quality, Metadata Management, Core Data Management Tools,

Data Quality initiatives always face the challenge. There are few myths one has to clear on this. A data quality program does not take much effort, to establish a business case. You can go into the root causes of the issues you faced earlier, and also run some queries on the database to bring about the issues. Surely your conviction on the need of a data quality program must have come from your sensing of the situation. Secondly, you should try to quantify the benefit. Lastly, you can make the a business unit or CFO as the mentor or sponsor of this initiative. If he/she takes the ownership it will be easier for you, as corporate audit has a direct access to CEO.

A data quality program has to be sponsored and owned by business. One of the reasons for lesser reception to business case is that business may feel it to be an IT subject. With ownership being given to business, they become more engaged on this subject.

For quantification, there is not better source than business itself. Once you have enough basis (by running queries and checks, and also by gathering the list of business issues linked to Data Quality), you can engage business on positive impacts if the data quality issues are resolved.

There are two stages of business case:

  • Initial business case to get a go-ahead on data quality analysis (as that also may need some investment). This is just like getting a go ahead for initial project scoping.
  • Detailed business case to get a go-ahead for Data Quality program (in other words- to get the cheque signed by the sponsor). This is going to be a more extensive exercise.

Few other points to Note:

  • You need to be careful that business not only understands the cost of quality program from IT perspective, but also all the change management as well as the effort they have to put on the business side (like changing processes, additional manual controls..).
  • One can engage a third party consultant or specialist. This may be given a greater level credibility to the business-case. The external view will also share on how the competition and other companies are managing their data quality program.

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Relevant Links to this page
TOPIC - Data Quality Organization Roles → Expert's Answers → Entry criteria to start DW project → Expert's Answers → Prime Purpose of a Data Warehouse → Expert's Answers → Online data feed to Data Warehouse. → Expert's Answers → Data Warehouse- Big Bang or Incremental → Expert's Answers → Dimensional model vs Relational model → Expert's Answers → Interview sequence for DW business requirements → Expert's Answers → Pre-configured reports- Do they work? → Expert's Answers → Business Intelligence vs Business Performance → Expert's Answers → Data Warehouse vs Business Intelligence → Expert's Answers → Refresh frequency of a Data Warehouse → Expert's Answers → EAI vs Data Warehouse → Expert's Answers → Ownership of Data Quality Initiative → Expert's Answers → Excel export to Data Warehouse → Expert's Answers → Captive ERP reporting capability → Expert's Answers → CRM and Data Warehouse → Expert's Answers → Starting A Data Quality Program → Expert's Answers → Source system re-writing in parallel to Data Warehouse → Expert's Answers → Data Profiling tool for Data Quality → Expert's Answers → Statistical sampling for Data Quality. → Expert's Answers → Simultaneous launch of source system and Data Warehouse → Expert's Answers → Security Matrix of a Data Warehouse → Expert's Answers → Data Quality program prioritization. → Expert's Answers → ETL phase taking too long → Expert's Answers → Should Data Warehouse wait for Meta-Data initiative → Expert's Answers → Mismatch in Source vs Data Warehouse reporting. → Expert's Answers → Data Warehouse vs Data Mart vs Data Mining → Expert's Answers → Data Quality Assurance vs. Risk Assessment → Expert's Answers → Data Quality Business Ownership in high-transition environment → Expert's Answers → Including informal and small systems in your Data Quality scope → Expert's Answers → When to use what level of detail for DQ assurance tracking? → Expert's Answers → Level of usage of Data Quality Practice Tool-Kit → Expert's Answers → Evolution path for Data Quality Practice Tool-Kit → Expert's Answers → Data Management Standards for Data Quality → Expert's Answers → Data Quality Practice Kit in work-flow and collaboration → Expert's Answers → Data Quality Policy- Level of Coverage → 
 
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Relevant links to this page
Data Quality Organization Roles
Entry criteria to start DW project
Prime Purpose of a Data Warehouse
Online data feed to Data Warehouse.
Data Warehouse- Big Bang or Incremental
Dimensional model vs Relational model
Interview sequence for DW business requirements
Pre-configured reports- Do they work?
Business Intelligence vs Business Performance
Data Warehouse vs Business Intelligence
Refresh frequency of a Data Warehouse
EAI vs Data Warehouse
Ownership of Data Quality Initiative
Excel export to Data Warehouse
Captive ERP reporting capability
CRM and Data Warehouse
Starting A Data Quality Program
Source system re-writing in parallel to Data Warehouse
Data Profiling tool for Data Quality
Statistical sampling for Data Quality.
Simultaneous launch of source system and Data Warehouse
Security Matrix of a Data Warehouse
Data Quality program prioritization.
ETL phase taking too long
Should Data Warehouse wait for Meta-Data initiative
Mismatch in Source vs Data Warehouse reporting.
Data Warehouse vs Data Mart vs Data Mining
Data Quality Assurance vs. Risk Assessment
Data Quality Business Ownership in high-transition environment
Including informal and small systems in your Data Quality scope
When to use what level of detail for DQ assurance tracking?
Level of usage of Data Quality Practice Tool-Kit
Evolution path for Data Quality Practice Tool-Kit
Data Management Standards for Data Quality
Data Quality Practice Kit in work-flow and collaboration
Data Quality Policy- Level of Coverage
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Principles & Rules
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