Data management operating models

Data Management Concepts

Sharon Omwega

Data Scientist

Unpacking centralized models

• Tasks centralized under a specific authority or team
• Ensures uniformity and control
• Eliminates redundancy across departments
• Provides clear guidelines for data handling
• Possible limitations include bureaucracy and inflexibility adapting to specific needs
• Best suited to organizations prioritizing standardization, compliance, and unified approach

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Understanding decentralized models

• Dispersed data responsibilities
• Independence and local decision-making
• Flexible response to departmental needs and instills a sense of ownership
• Challenges include consistency hurdles, standardization issues
• Communication and alignment are paramount
• Suitable for organizations with varied units and distinct data requirements

Navigating hybrid models

• Blended approach combining elements from both centralized and decentralized models
• Aims to find equilibrium between flexibility, autonomy, control, and standardization
• May have central management for overall governance and strategic decisions
• Various units may have autonomy over specific data tasks
• Beneficial for organizations requiring both central oversight and decentralized flexibility

Considerations for implementation

1. Organizational culture
2. Scalability
3. Data sensitivity and security
4. Flexibility and responsiveness
5. Technology infrastructure compatibility
6. Integration requirements
7. Regulatory compliance
8. Cost implications

Let's practice!

Data Management Concepts