Maturity models contain a set of levels or phases, each with their own characteristics, usually about the status of processes and structures, people and culture, tools technology. As an organization advances in a level/ phase, it is considered more mature in its treatment, usage, and understanding towards data and information.
Organizations often use maturity models as a comparison to where their program lies and where it should move towards. In this article I will cover the Oracle maturity model, but please feel free to also go through these other ones I’ve covered:
- Stanford Data Governance Maturity Model
- IBM Data Governance Maturity Model
- DataFlux Data Governance Maturity Model
- Gartner EIM Maturity Model
Oracle Data Governance Maturity Model
Overview: Oracle states that data governance “does not come together all at once” and an iterative approach is needed. To guide organizations in their approaches, Oracle developed its own maturity model to assess the current state maturity of the data governance capability. Its model is comprised of 6 levels, or milestones:
1. Milestone one: None (level 0)
- No formal governance processes, policies, standards, etc. are in place
- Data is a by-product of their applications
2. Milestone two: Initial (level 1)
- IT has some authority over the data, but has limited influence on business processes which don’t consider the benefits of data governance
- There is some business and IT collaboration, but it is inconsistent across the enterprise as it is more project based
- Data champions are present in different business areas
3. Milestone three: Managed (level 2)
- A few business areas/ departments/ units have data owners and data stewards
- Some processes are defined at a high level around key systems
- Data problems are dealt with reactively, without addressing their root cause
- Standards are starting to be put together at the department or system level
4. Milestone four: Standardized (level 3)
- The roles of data stewards are explicitly defines and appointed
- Cross-functional teams are formed to tackle data governance
- Processes and standards are consistently established across departments
- A centralized repository of data policies is established
- Data quality measures are defined, monitored, and improved
Avoid losing track of data quality issues. Here is a free data quality issues log.
5. Milestone five: Advanced (level 4)
- Data governance organizational structure is enterprise-wide
- Data governance is viewed as critical to business across all functions
- Quantitative goals for processes and data quality are set and met
- Ownership of data quality and metadata as well as data policy making, lies with the business
6. Milestone six: Optimized (level 5)
- Data governance is core to the business process and projects
- Decisions are informed by data which provide quantifiable benefit/ cost/ risk analysis
- Processes and policies are firmly established and adopted and continually revised to reflect business goals and objectives
Take away: As other models, Oracle’s pushes for the adoption of an on-going program and a continuous improvement process, with their milestones to guide the way. To increase maturity, they recommend a three phase approach in governance activity, which I find even more valuable:
1. Explore: Build a solid data governance foundation and create data governance leaders. Key activities:
- Understand and prioritize data governance needs
- Assess where business improvement can bring the most benefit
- Create a planning document for implementation
- Create or select a framework to ensure the confidentiality, quality, integrity of the data
- Define the mission and vision for the program
- Establish and define goals, metrics, success measures, and funding strategies
- Define data standards, policies, process, etc.
- Establish a data governance council
- Create a data governance communication plan
2. Expand: Include extending data governance coverage from local project implementation to department/ division level. Think globally and act collaboratively cross-division. Key activities:
- Establish a centralized data repository
- Enforce data quality evaluation and automation
- Deploy tools for more complex data quality improvements
- Adopt more sophisticated and comprehensive data security tools, processes, and policies
- Create the process for dealing with data modeling and data architecture changes
3. Transform: Enterprise-wide data governance is established. Key activities:
- Develop automated data quality dashboards
- Optimize data governance processes
- Evaluate and communicate the data asset valuation results to the enterprise
- The business intelligence landscape has data governance at its foundation
- Manage new data service consumer agreements
- Create service level agreements (SLAs) around data sharing and data usage
More information:
- Enterprise Information Management: Best Practices in Data Governance (White paper from May 2011)
Checkout this online course on Data Governance Maturity Model(s) to learn all there is to know about them, the best practices and pitfalls on selecting and using them, and so much more.
Next I’ll go over Open Universiteit Nederland Data Governance Maturity Model.
