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The data lifecycle Data Management comprises all disciplines related to managing data as a valuable resource.
The concept of data management arose in the 1980s as technology moved from sequential processing (first cards, then tape) to random access storage. Since it was now possible to store a discreet fact and quickly access it using random access disk technology, those suggesting that data management was more important than business process management used arguments such as “a customer’s home address is stored in 75 (or some other large number) places in our computer systems.” However, during this period, random access processing was not competitively fast, so those suggesting “process management” was more important than “data management” used batch processing time as their primary argument. As software applications evolved into real-time, interactive usage, it became obvious that both management processes were important. If the data was not well defined, the data would be mis-used in applications. If the process wasn’t well defined, it was impossible to meet user needs.
In modern management usage, the term data is increasingly replaced by information or even knowledge in a non-technical context. Thus data management has become information management or knowledge management. This trend obscures the raw data processing and renders interpretation implicit. The distinction between data and derived value is illustrated by the information ladder. However, data has staged a comeback with the popularisation of the term Big data, which refers to the collection and analyses of massive sets of data.
Several organisations have established data management centers (DMC) for their operations.
Integrated data management
Integrated data management (IDM) is a tools approach to facilitate data management and improve performance. IDM consists of an integrated, modular environment to manage enterprise application data, and optimize data-driven applications over its lifetime. IDM’s purpose is to:
- Produce enterprise-ready applications faster
- Improve data access, speed iterative testing
- Empower collaboration between architects, developers and DBAs
- Consistently achieve service level targets
- Automate and simplify operations
- Provide contextual intelligence across the solution stack
- Support business growth
- Accommodate new initiatives without expanding infrastructure
- Simplify application upgrades, consolidation and retirement
- Facilitate alignment, consistency and governance
- Define business policies and standards up front; share, extend, and apply throughout the lifecycle
Data Management Frameworks
A Data Management Framework (DMF) is a system of thinking, terminology, documentation, resources and insights which allows users to view data related concepts and information in their own context, and in the broader context of the framework, thereby enabling them to integrate their conversations and work.
There are a number of DMFs available.
William Richard Evans, of South Africa, has developed three Fully Integrated Data Management Frameworks: The Data Atom Data Management Framework version 1.0 was developed between 2010 and 2014. Version 2.0 was developed between 2014 and 2017. With the advent of artificial intelligence, the Internet of Things and data lakes, version 2.0 was replaced with the more comprehensive Multi Dimensional Data Management Framework V3.0. It covers 20 data management disciplines and 7 data environments. On 20 October 2018 He released The Multi Dimensional Data Management Framework V4.0, which includes 8 Data Management Considerations at the core. Four relate to the impact time has on Data and another four provide insight on the current trajectory towards Managed Technological Singularity using Artificial intelligence.
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