OVERALL USAGE
Purpose of Data Quality Policy Template
The purpose of this work-tool is to provide you all the possible headings and example text for a data quality policy which suits your function.
Who uses the work-tool?
The enterprise data steward should be the owner of this work-tool. He should be responsible for drafting this document and get it is approved by CIO, CFO, Head of Audit, Head of Internal Control, Head of Quality
CEO
The draft policy is circulated to the heads of business and other members of the management team, to ensure that they are aligned. The key decision makers on the policy are the list of approvers.
When this work-tool is used?
This work-tool will be used throughout the drafting and sign-off as it is review and changed till the final approval.
What this work-tool is not?
This work-tool is a management policy document, and does not contain the standards and control guidelines.
Linked Practice Tools-
---------------------------------------------------------------------
Help-Guide for Data Quality Policy Template
- State the Back-ground, on what has led to this policy: example:
- We have over 50 systems, 8 tera-byte of data.
- We have low audit rating in terms of data quality and internal control.
- We need to ensure the fundamental
- We are planning to introduce three more channels over next one year.
- We have just implemented a large core production system..
- We need to establish a data quality framework, which enables us a stable growth.
- Purpose and Objective: State the purpose of this document. For example:
- To establish a Management Policy for ensuring Data Quality.
- To establish guidelines for Data Quality Governance and Management.
- To provide high level guidelines for Data Quality.
- To ensure the adherence to the Data Quality Procedures.
Data Quality Policy
NOTE- Every organization has a different data quality policy, depending upon the kind of organization readiness. One does not need to have all the fundamental of Data Quality in place before issuing a data quality policy. For example, if the organization does not have universal Data Management standards, and does not have the readiness to do it over next one year, one may not include it in the policy. In other words, a data quality policy can start small and evolve.
NOTE- First few items are related to the Data Quality Roles. You can refer Data Quality Organization for subject matter.
- Business Owner Ship of Data Quality: example of Text
- Need for the business ownership of data
- Why business ownership is important.
- Data Quality is not only an IT subject
- Business Process and Business specifications are key factors for Data Quality.
- IT is an enabler and not a driver for Data quality
- Business Custodian-ship for Data Quality: example of Text
- Need for the business ownership of data
- Why Data Custodian?
- IT is the physical house-keeper of the data, but the custody is with business.
- Data does not reside only in IT Systems, but is also stored in manual documents, and in non-core applications (for example-excel files).
- Data custodian should be working with Data Steward and business owner to ensure data quality.
- Appointment of a Data Steward: Please refer Data Steward and data quality organization to understand the subject matter. example of Text
- We need a single point accountability for enterprise health of Data.
- Data steward will work with Business Owner, Data Custodians and the management team to drive data management initiatives, with Data Quality being a core component.
- Data Steward role will be empowered and made accountable to ensure that Data Quality initiatives and program have right level of support and sponsorship.
- Data Management Council: example of Text
- We need management ownership for Data Quality.
- Data Quality Council will be providing an oversight on the Data Quality efforts, take cross-functional decision.
- Data Quality Council will work resolving prioritization issues across the Data quality effort and other business needs.
- Data Quality Assurance Methods: example of Text
- There is a need for establish universal data quality assurance methods, as guidelines for all stakeholders to follow.
- The Data Quality Assurance methods to include the interface controls, Business partner interface controls, input controls and Business process controls,
- Data Management Universal Standards: example of Text
- There is a need for establish universal standards related to the data which we capture and store in our systems.
- We need to define the common standards for domains, data formats, data models, business rules related to the data entities and data elements.
- All the stakeholders will ensure adherence to these standards.
- The exceptions to these standards for new initiatives will be signed off by the data steward and the business owner.
- Data quality Assurance Procedures: example of Text
All project will have defined procedures to ensure the data quality. This will include a formal assessment at different phases for our adherence to data management standards and data quality assurance methods.
- Data Audit and Monitoring: example of Text
- We as an organization need to be pro-active to assess the health of the data. Every business owner of Data Quality, will plan and ensure a periodic monitoring of data based upon the criticality and perceived health.
- Data Audit will be part of the Internal Audit scope. At least once a year, all core and critical systems from Data Quality perspective, will undergo one audit on the health data.
- Data Correction : example of Text
- Data Correction needs to be handled carefully. As per this policy, the stakeholders should be correcting the data from the front-end of the systems.
- If due to an urgency, one needs to correct the data from the back-end of the system, one will need to follow the data correction procedure. This procedure will include
- Review of data correction scripts.
- Extensive testing of the Data correction scripts before executing them in production.
- Data Quality Measurement: example of Text- Data Quality Council will need to measure the Data Quality in our environment in terms of:
- Accuracy
- Completeness
- Auditability
- Consistency
- DQ Assurance Controls.
The Data Quality Dash-board to be published every six months.
- Data Quality Awareness: example of Text-
- Each business owner and IT owner will be responsible to generate awareness on Data Quality.
- Human Resources to include Data quality as part of standard training kit for all employees.
|
