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Data Administration

Digitization and the data obtained from it facilitate a great many company-specific processes. However, the corresponding data must also be structured in such a way that it can be used in a meaningful way. This is where data administration plays a major role.

In today's world, the flood of information and data in companies is constantly increasing, which is why it is becoming more and more difficult to maintain an overview of the meaningfulness of the data generated. This rapidly increasing GROWTH OF DATA can quickly lead to internal overload if data management is not yet anchored in the company. If the processes for data classification are missing, the company cannot evaluate the data either.

However, with a selected infrastructure and the necessary processes, internal company data can quickly become valuable capital that can now be used.

The terms "data administration" or "data resource management" can simply be described as "data management". But what processes are hidden behind them?

What does Data Administration / Data Resource Management mean?

"Data Administration" or also "Data Resource Management" refers to a process in which data is monitored, managed and maintained by an administrator or an organization. This process is necessary in the corporate environment in order to be able to control data and its processing with various applications. Data Administration, then, is the basis for managing all of an organization's data resources. These data resources include, for example:

  • Data definitions, policies, procedures and standards
  • Solutions for data conflicts
  • Database planning, analysis, design, implementation and maintenance
  • Data protection
  • Data performance assurance
  • Training and consulting

For this reason, "Data Administration" is often also referred to as "Data Resource Management".

Tasks of Data Administration / Data Resource Management

The administration of data includes the management of data and information, in which the data flow should be analyzed, data models should be created and the relationships between them should be defined. Likewise, the administration defines the security and access control elements of data, because in a company not all data and contents of the system should be accessible to everyone and therefore certain restrictions are necessary.

The main goal is to prepare the existing company data in a structured way in order to build analyses on it. If there is no overview of the flood of data, companies have no chance of relating this data to one another and thus using it meaningfully for analyses.

A simple example will clearly explain Data Administration: A company wants to organize its unstructured data and assign it to specific areas so that at some point each department can contribute to it.

  • In the first step, it is necessary to discover and compile all data. A complete data pool can be compiled through a targeted search as well as specific filtering of the data into sub-areas.
  • In the next step, the data is enriched with metadata. In addition to a clear name and data types, metadata contains additional context such as annotations, descriptions, affiliation with specific areas or tags. This metadata is used in the further course for simplified findability and classification.
  • The third step requires understanding the data and its application and being able to classify it correctly. Here it is important to know which data can be analyzed and processed in what way.
  • In the fourth step, the data assigned to the respective departments is opened up to all employees. These can and should now contribute valuable information.

In this way, the data benefits from the wealth of knowledge of all employees, and possible BUSINESS INTELLIGENCE analyses become more meaningful. Data Administration provides the framework in which such data preparation can take place and the logical step to DAM and PIM can be taken.

The difference from database management

Since data administration is often confused with database administration, it is necessary to be aware of the differences:

The difference from database administration is that the focus of Data Administration / Data Resource Management is not on the technical details associated with data management as in database administration, but on defining the data management processes to be used as organizational assets.

By directing the collection and modeling functions, information and metadata management strategies should be coordinated. Data modeling supports the development of applications using diverse tools and methodologies. This results in the possibility of integrating and bridging different application and software packages into the overall structure of the data architecture.

Data administration thus defines the architecture of the data to be collected, access rights, analysis structures, uses and procedures with which the data can subsequently be used within a company. It is thus virtually the prerequisite for the correct use of PIM and DAM systems.

The entirety of the data architecture forms the enterprise data model and is crucial for the company's ability to analyze and evaluate business risk and various effects of business changes.


Companies that have their data under control, can analyze it and draw the right conclusions from it, are equipped for current and future challenges. Data administration is the first step in gaining control over internal data. This makes it all the more important to work with great care.

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Sebastian Dietrich

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