Integrated Data Management

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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. It manages data over its lifetime, from requirements to retirement.

Contents

Overview

Data life cycle

Data retention Data governance Data quality Agile Data Method Data modeling

Purpose

Produce enterprise-ready applications faster

  • Improve data access, speed iterative testing
  • Empower collaboration between architects, developers & 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 & retirement

Facilitate alignment, consistency & governance

  • Define business policies and standards up front; share, extend, and apply throughout the lifecycle

Example

Integrated data management lets you grow your business without growing your infrastructure. How is this possible? Let’s say you’ve just completed an acquisition, and you need to bring three new manufacturing facilities on board. Application consolidation and retirement is common in this scenario. Data archiving capabilities help to minimize cost and accelerate completion of such scenarios:

  1. you can minimize the data that you migrate from the legacy system to the consolidated system accomplishing the consolidation faster and minimizing hardware and software requirements to support additional load.
  2. You retain archived information for as long as needed on lower cost stire, immutable if needed, while providing flexible access to it for e-discovery.

Time to market is a critical imperative in today’s environment. IDM helps you produce enterprise-ready applications faster. For example, our pureQuery technology is built to give developers the productivity they need, while helping them adopt best practices for data access. It provides a collaborative environment for developers and DBAs to work together to optimize data access performance, maximize database security, and improve manageability. It makes service enabling vetted database assets as simple as a drag-and-drop gesture. Our test data management capabilities let tester leverage production-like data while safeguarding client privacy and corporate confidentiality. And we help to jump-start development and testing efforts based on accelerators for packaged applications, industry models, and compliance initiatives.

For production systems, the challenge is to meet increasingly strict and challenging service level targets yet still free up staff time for value creation activities. We’re focused on learning more about the environment enabling the tools to automate and simplify operations. And aggregating and contextualizing information across the solution stack so that administrators have the information they need to identify emergent problems, view relevant information about the problem, isolate the problem to its source, and get expert advice on resolution.

And we want to help business and IT work together towards business objectives such that there is a common understanding and transparent and consistent execution across the business. We are building out a capability to define business policies and semantics early in the design cycle and then share them via models and other data artifacts across the lifecycle. We call this idea model-driven governance. Not only will this improve alignment and governance, but also organizational productivity and effectiveness by facilitating seamless collaborate: From analysts to architects to developers to administrators From design to delivery to management

Technologies

Metadata

Eclipse

Model driven development

Data Governance

See also

IBM Approach

Related Database Technologies

References

External links

Personal tools

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