From Data Objects to Governance
Start with the Data Objects and the various tasks, roles and processes orbiting them. Then define the roles, responsibilities and processes so that every level becomes structured measurable, and controllable.
Without control and consistency at the base, processes utilising multiple data objects will fail. When maintenance and control start from the object level, domain quality is possible.
Roles require structure, policies & procedures so that governance can be embedded and risk mitigated.
- Profiling of the master data object tables & preparation of the results
- Rules workshops with subject matter experts
- Analysis of data against rules
- Interpretation of business impact
An assessment document outlining:
- Critical fields identified in the object
- Business impacts for these fields quantified as far as possible
- Rules identified for these fields
- Interpretation of data quality and recommendations for future improvements
- Rules defined for critical fields
- Rules can later be used to cleanse data
- Future data can be verified at point of entry so poor quality data cannot be captured (see SimpleData Management for further details)
- Business Drivers for data governance
- Current data governance organizational structure
- Technology enablers
- Profiling of the master data object
A gap analysis outlining:
- Findings from the assessment and survey results
- Recommendations on managing, improving, and monitoring organizational data
- Practical quick wins, and medium to long term data governance goals
- Identify the degree of explicit, formalised Master Data Governance structures in place
- Assess supplied documents and Data
- Define current issues and pain points with Master Data
BUSINESS IMPACTS: Financial, Legal, Lost opportunity / Unrealised revenue. Operational inefficiencies.