As enterprises are growing, data need at all levels and all types is increasing and decision making is getting more complicated. What business users see and ask for more is reports that they can access aggregated and analysed data in the form of information. On the other hand, what IT developers need to do to meet this expectation is much more than creating reports.
Other than reporting purposes, data start to act as a commodity between different departments and processes in an enterprise and also between different enterprises. This extensive need for and use of data led the expansion of data management field and accelerated technological developments.
“What we’re seeing now is an absolute explosion in data management technology and it’s come about because of the complexity of data problems”
Mike Olson, the CEO of Hadoop company Cloudera.
Data Management is defined as "Administrative process by which
the required data is acquired, validated, stored, protected, and processed, and
by which its accessibility, reliability, and timeliness is ensured to satisfy
the needs of data users." in
http://www.businessdictionary.com.
- 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.
- 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.
- Data Development – The
data-focused activities within the system development lifecycle (SDLC),
including data modeling and data requirements analysis, design, implementation
and maintenance of databases data-related solution components.
- Database Operations Management –
Planning, control and support for structured data assets across the data
lifecycle, from creation and acquisition through archival and purge.
- Data Security Management –
Planning, implementation and control activities to ensure privacy and
confidentiality and to prevent unauthorized and inappropriate data access,
creation or change.
- Reference & Master Data
Management – Planning, implementation and control activities to ensure
consistency of contextual data values with a “golden version” of these data
values.
- Data Warehousing & Business
Intelligence Management – Planning, implementation and control processes to provide
decision support data and support knowledge workers engaged in reporting, query
and analysis.
- Document & 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, video)
- Meta Data Management – Planning,
implementation and control activities to enable easy access to high quality,
integrated meta data.
- 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.
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| Data Management Knowledge Areas, DAMA |
