Friday, 3 February 2012

Management of Database Systems

Discussions held at the Avon Information Management group meeting on 1 February.

My current working research questions were discussed.

To what extent are best practices and procedures utilised by the database community?
Is their adoption affected by the complex interactions that are an integral part of the management of Database Systems? 
Can the use of holistic methodologies contribute to improvement and innovation?


The Database System
The 4 main components in the system:- 
  • Technical Conponents
  • Cultural factors
  • Paradigms
  • Application Centric
Technical Layers 
The current technical system discussed broken down into sections including:-
  • Architecture
  • Access and Control
  • Maintenance
  • Resiliance and Conservation
  • Data Artifacts
  • Change
  • Forecasting
Cultural Factors
Items relating to cultural  arena:-
  • Ethics and Governance
  • Companies perception vs DBA’s
  • Organizational management structure
  • Customer requirements
  • Financial and IT progression
  • Proactive vs Reactive
  • Database community
  • Vendor  vs Supplier  vs Competitors
  • Change Managers vs Developers vs Service Managers
  • Attitudes and beliefs of DBAs
  • Market place changes from local to global
  • Environmental considerations
  • Government, Legal and Political issues
  • Business Owner, Changes to the business model, Risk
Paradigms
A few major methodologies that are in use today:-
  • Service Management
  • Data Management
  • Problem Management
  • Forensic Management
  • Agile Management
  • Information Lifecycle Management
  • Database as a Service
Additional paradigms to consider:-
  • Service Orientated Architecture
  • Capability Maturity Model
Application Centric
Taking the viewpoint that the application is at the centre of everything:-
  • Structured vs Unstructured vs Semi Structured
  • Transient vs Transactional vs Historical
  • Big Data vs Scalability, Sharding
  • Scale out vs Scale up
  • Quality of Data
  • Cloud vs On Premise
  • Data Sets for Public consumption
  • Database Engine changes vs Virtualization