The data excellence framework describes the methodology, processes and roles required to generate business value while improving business processes using data quality and business rules. The framework supports the creation of a new cultural shift focused on data excellence, motivating the broader team and supporting collaboration between the stakeholders. The framework takes into consideration that the solution, although simple to articulate, is complex with many dimensions.
Therefore, it is critical to focus on culture as a key to a sustainable solution. A key difference about the data excellence framework is that it is focused on generating value in comparison to most initiatives relating to data which are only focused on reducing costs.
- Business Value Generation Pillars
- Data Maturity Model
- Data Quality
- Data Governance via Data Quality Dimensions
- Business Rules
- Key Value Indicator (KVI)
- Data Governance Model
- Data Excellence Process
- Data Excellence Principles
- Approach to achieving Data Excellence
COMPONENT
- Business Value generation can be expressed in terms of four pillars - trust, transparency, intelligence and agility.
- Describes the characteristics of each stage from ad-hoc departmental projects to enterprise data governance initiatives.
- High quality data results in increased business process efficiency. Low quality data results in errors, additional costs and inefficiencies in processes.
- Characteristics of data that enable data quality to be measured e.g. uniqueness, completeness, accuracy, non-obsolescence and consistency.
- Business rules that data should comply with in order for business processes to execute properly.
- A KVI is a measurement of the percentage of records which successfully executed the business rules plus a list of the records which have violated the business rules.
- Data Governance executed through Data Stewardship roles.
- A step by step process for implementing data excellence.
- Principles necessary to change an organization’s culture.
- A high level methodology linking all the framework components as part of an implementation strategy.
DESCRIPTION
- Links data quality to business value generation.
- Data Excellence can be achieved progressively (non invasive approach).
- Business Case and ROI are based on multiple wins across all departmental processes.
- Metrics based approach.
- Focus on critical data attributes only.
- Linking data quality metrics and business value.
- Implement data governance model with existing roles.
- Logical process based on data quality audit and root cause analysis.
- Multi-dimensional problem solving approach.
- Proven methodology across global organizations.