IT Management

Frameworks can be wonderful things. They help us organize how we think and communicate about complicated or ambiguous concepts. For many nontechnical people, data management seems both complicated and ambiguous. The differences between various data management functions seem unclear, and this leads to unclear accountabilities for compliance requirements and activities.

Finally, a framework is available that you can use to bring clarity to your work. It’s called the DAMA DMBOK, or Data Management Body of Knowledge, developed and published by DAMA International (The Data Management Association International).

The DMBOK (pronounced dim-bock) organizes data management activities into 10 different functions, each with its own specific mission and goals, guiding principles, functions and activities, deliverables, roles, and technologies. It provides a standardized way of looking at those functions while also identifying widely adopted methods, techniques, and good practices. It also looks at some common organizational and cultural issues and the ways environmental conditions affect data management approaches. As a data management professional, you may want to download the entire 400-plus page framework, available for a fee at www.dama.org. At the very least, download the free white paper describing the framework. A simple list of data management functions and their key activities could go a long way toward setting reasonable expectations for your next compliance project.

The 10 DMBOK data management functions are:

1. Data governance: The exercise of authority, control, and shared decision-making (planning, monitoring, and enforcement) over the management of data assets. Data governance is high-level planning and control over data management.

2. Data architecture management: The development and maintenance of enterprise data architecture within the context of all enterprise architecture, and its connection with the application system solutions and projects that implement enterprise architecture.

3. Data development: The data-focused activities within the System Development Lifecycle (SDLC), including data modeling and data requirements analysis, design, implementation, and maintenance of database data-related solution components.

4. Database operations management: Planning, control, and support for structured data assets across the data lifecycle, from creation and acquisition through archival and purge.

5. Data security management: Planning, implementation and control activities to ensure privacy and confidentiality and to prevent unauthorized and inappropriate data access, creation, or change.

6. Reference and master data management: Planning, implementation, and control activities to ensure consistency of contextual data values with a “golden version” of these data values.

7. Data warehousing and business intelligence management: Planning, implementation, and control processes to provide decision support data and support knowledge workers engaged in reporting, query, and analysis.

8. Document and content management: Planning, implementation, and control activities to store, protect, and access data found within electronic files and physical records (including text, graphics, image, audio, and video).

9. Metadata management: Planning, implementation, and control activities to enable easy access to high-quality, integrated metadata.

10. Data quality management: Planning, implementation, and control activities that apply quality management techniques to measure, assess, improve, and ensure the fitness of data for use.