This technical article is edited by one of the professional data management companiesMiniTool. It defines various common data management-related terms like product data management, data management services, big data management, data risk management, data center infrastructure management, etc.

What Is Data Management?

Data Management contains all disciplines involving in managing data as a valuable resource. Arose in the 1980s, data management refers to the technology moved from sequential processing (first punched cards, then magnetic tape) to random access storage.

Now, it is possible to store a discrete fact and quickly access it using random access disk tech. thus, many people think that data management is more important than business process management. yet, since software programs evolve into interactive and real-time use, it is obvious that both management processes are crucial. If the process is not well defined, it’s impossible to meet users’ requirements. Also, if the data isn’t well defined, it will be misused in apps.

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  1. Data architecture
  • Data architecture
  • Data flow
  1. Database and storage management
  • Data maintenance
  • Database administration
  • Database management system
  • Business continuity planning
  • Data sub-setting
  1. Data security
  1. Reference and master data
  • Master data management (MDM)
  • Reference data
  1. Data integration and interoperability
  • Data integration
  • Data interoperability
  • Data movement (extract, transform, load)
  1. Documents and content
  • Document management system
  • Records management
  1. Data quality
  • Data discovery
  • Data cleansing
  • Data integrity
  • Data enrichment
  • Data quality
  • Data quality assurance
  • Secondary data
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Development of Data Management

In modern management usage, the word “data” is increasingly replaced by “knowledge” or “information” in non-technical contents. Therefore, data management has become knowledge management or information management.

This trend obscures the raw data processing and renders interpretation implicit. The distinction between data and derived value illustrated by the information ladder. Yet, data has re-gain its popularity with the rising of big data, which means the collection and analysis of massive sets of data. Several organizations have established data management centers (DMC) for big data management.

1. Master Data Management

Master data management (MDM) is a tech-enabled discipline in which business and information technology cooperate to ensure the uniformity, consistency, accuracy, semantic, stewardship, and accountability of the enterprise’s official shared master data assets.

2. Data Management Platform

A data management platform (DMP) is a software platform used to collecting and managing data. it enables businesses to identify audience segments, which can be applied to target specific users and contexts in online advertising campaigns.

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3. Enterprise Data Management

Enterprise data management (EDM) is the ability of an organization to precisely define, easily integrate, and effectively retrieve data for both internal applications and external communication. It focuses on the creation of accurate, consistent, as well as transparent content.

4. Data Lifecycle Management

Data lifecycle management (DLM) is a policy-driven structure or model through which data flows to optimize its useful life. It is represented by a series of steps or stages that typically begin with data collection or creation and end with data destruction or recycling.

The three main goals of data lifecycle management are data security and confidentiality, all-time availability, and long-term structural integrity. The six phases of the lifecycle of data are data capture, data maintenance or storage, data usage, data publication or release, data archiving, and data cleaning.  

5. Data Management Software

Data management software (DMS) is a kind of tool to help users collect, store, edit, transfer, copy, as well as delete or remove data. generally, a single data management program can’t do all the mentioned operations; most data management Services can complete one or more tasks. For example, MiniTool ShadowMaker is one of the data management tools that mainly creates a backup of data to ensure data security. This is also helpful for data risk management and data security management.

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6. Data Center Management

Datacenter management is the collection of tasks performed by those responsible for managing the ongoing operation of a data center. This contains business service management and planning for the future.

In history, data center management was regarded as something performed by employees with the help of tools collectively called data center infrastructure management (DCIM) tools.

7. Customer Data Management

Customer data management (CDM) refers to how businesses keep track of their customer info and survey their customer base for obtaining feedback. CDM embraces a variety of software or cloud computing apps developed to give large organizations fast and efficient access to customer data.

8. Product Data Management

Product data management (PDM), also called product information management (PIM), is the business feature usually included in product lifecycle management. It is responsible for the management and publication of product data. in software engineering, PDM is known as version control.

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9. Data Management Plan

A data management plan (DMP) can mean a formal document that outlines how data are handled during a research project and after the project is completed. The goal of DMP is to consider the many aspects of data management, metadata generation, data preservation, as well as analysis before the project starts.

10. Data Management Strategy

A mobile data management strategy, for example, is a structure imposed on a complex data model that’s to be navigated by a user on a mobile device. It is a relatively new process that rises from the popularity of mobile apps requiring a flexible and in-depth navigation structure.

11. Integrated Data Management

Integrated data management (IDM) is a process to facilitate data management and improve performance. IDM consists of an integrated, modular environment to manage enterprise program data and optimize data-driven apps over its lifetime. Below are the purposes of IDM.

  • Improve data access, speed iterative testing.
  • Simplify app upgrades, consolidation, and retirement.
  • Produce enterprise-ready software faster.
  • Simplify and automate processes.
  • Offer contextual intelligence across the solution stack.
  • Accommodate new initiatives without expanding infrastructure.
  • Facilitate alignment, consistency, and governance.
  • Empower collaboration between architects, developers, and database administrators (DBAs).
  • Achieve service level targets consistently.
  • Support business growth.
  • Pre-define business standards and policies; extend, share, and apply throughout the lifecycle.

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