General Data Governance Framework
Data-governance is a journey, not a destination..
This blog will introduce data governance and how one can build an effective and holistic data governance framework.
Data governance is a culture set around data, a culture of people, processes and technology that ensure high quality and compliance through the complete lifecycle of your data.
5 Elements of Data Governance Framework
- Strategic Business Driver
- Governance Outcomes
- People
- Process
- Technology
1. Strategic Business Driver
The first part of the framework is “why” you want to implement data governance.
The drivers can be but are not limited to:
- Regulatory Compliance
- Merger & Acquisition
- Better Decision Making
- Enhanced Data Security
- Outsourcing
- Documentation
- Digital Transformation
2. Governance outcomes
This part is the end goal of your data governance implementation.
- Improved Data Quality
- Improved Data Management
- Increase Confidence in Data
- Quicker Data Discovery
- Cost Saving
- Risk Mitigation
- Improved Compliance
- Competitive Advantage
- Better Collaboration
3. People
Data governance is an enterprise-wide program and involves a large number of stakeholders. There should be a dedicated Executive Steering Committee consisting of the following roles.
- Project Sponsor
- Data Owner
- Compliance Officer
- Master Data Manager
- Data Stewards
- Data Quality Lead
- Metadata Lead
- Data Analyst
The committee reporting structure is given in the following chart.
The goal of people is to create a data governance organization structure, formalize the roles, create a program charter and train the employees.
4. Process
The data governance process component includes the following:
Principles: These are High-level statements of what an organization wants to achieve by implementing data governance.
Policies: A collection of statements of an organization’s intent or rules controlling several data-related activities and operations — creation, capture and acquisition, retrieval/requests, transformation, management, quality, protection, sharing, and data usage throughout its lifecycle.
Guidelines: Recommendations and best practices designed to achieve the policy’s objectives by providing a background to develop standards and implement processes.
Processes: Methods or sets of steps to support the various activities needed to govern data and implement the organization’s data policies. While policies define what to do, processes establish how to do it.
Rules and Standards: Encompass business rules, data quality rules, and data standards to ensure consistent results from people and processes using them.
5. Tools & Technology
The data problems that an enterprise faces are only sometimes new. There is no need to reinvent the wheel.
Good spreadsheet software like excel is enough for most tasks, but it’s better if you have the following tools in your enterprise.
- MDM (Master data management tool)
- BI (Business Intelligence tool)
- Meta Data Repository
- Policy Management Software
Putting it all together
The following is the 5 step framework to start your data governance transformation.
I hope this helps you set up data governance in your organization and if it does do drop me a note.
If you want me to help you build a model for your company reach out to me on my LinkedIn!
