Published on December 30th, 2021 | by Bibhuranjan


Data Management and Information Management Systems

Data management systems are defined as quantitative or qualitative attributes of a set of variables or a variable. Data provides information in the form of an abstract setting. This includes:

  • The class to which the assigned variable belongs
  • The object which is a member of a specific class
  • Some ideas regarding object operations and relationships to other classes and objects
  • Data alone and in the abstract form does not provide information

Data translates the information into a form that is efficient for processing. Data is information that has been translated into binary digital form. You can use data as a plural or singular subject. Raw data describes data in a standard digital format.

Information management systems answer questions. Therefore, it relates to data and knowledge with the data representing values assigned to parameters and the knowledge indicating the understanding of abstract concepts.

When data is in its most standard digital format, it does not provide information. When you combine it with other data or manipulate it somehow, then organizations can utilize to derive value from the information, which leads them to obtain knowledge.

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How Is Data Management Different from Information Management

An information management system is an organizational program responsible for managing people, technology, and processes that provide control over the processing, structure, delivery, and utilization of the information that businesses require for management and intelligence purposes.

The two modes of information include electronic and physical information. Businesses need to have the capability to manage the information throughout their lifecycle despite the format or source—electronic documents, data, paper documents, visual, audio, and more— for delivery via various channels such as online and mobile phones.

A data management system is a subset of an information management system. It includes all disciplines that relate to the management of data as a valuable and critical organization source. It is described as the process of developing, acquiring, converting, sharing, securing, documenting, and storing data.

DAMA International defines data management as the development, implementation, and management of plans, policies, programs, and practices that regulate, secure, and enhance the value of information and data assets during their lifecycle.

Data management systems include policies, file-naming conventions, and practices on developing documentation and metadata for the long-term. Data management ensures the availability, accuracy, completeness, and security of data that underlies a business. Data also addresses the development and implementation of policies, architectures, procedures, policies, and practices that manage the data lifecycle.

Why Do Businesses Need to Know the Difference between the Two?

Knowing the difference between data management systems and information systems can help businesses identify the gaps in the approaches they take. It can lead them to develop a foundation that drives high-quality data, which they can use to make more informed business decisions.

Businesses Need to Use Information and Data Management (IDM) Systems

Information and data management systems create practices, policies, and procedures to ensure that data is understandable, visible, trusted, accessible, optimized for usage, and interoperable. IDM comprises processes for strategy, modelling, planning, access control, security, data analytics, conception, and quality.

The outcomes improve assurance and data quality, enable sharing information, and fostering data reuse by reducing data redundancy. Data is a crucial aspect of businesses because it flows between various databases, systems, departments, and processes. Without data, businesses will struggle to make smarter and effective decisions regarding their operations.

The Importance of Utilizing Data for Organizational Success

Businesses with a high success rate refer to the data asset when defining, designing, and building databases and systems. They utilize data to make educated decisions that guide and measure the effectiveness of their organizational strategy.

Example: Businesses may analyze data to determine the best enforcement actions they can take to enforce compliant practices and behaviour.

Example: Since data is the core part of the business processes, businesses may improve a process to monitor and identify fraudulent activities using historical data related to risk.

Using data to improve processes can result in material savings. Implementing a single business process can lead a business to gain sustainable benefits, such as utilizing data patterns to filter cyber attacks.

When businesses have access to quality data, they need to know how to use and manage it properly to bring success to the business. Appropriate and quality data allows businesses to perform processes and identify which processes will impact the business if utilized correctly.

Businesses Need to Make the Data Assets Available to the Processes and the Staff

To succeed in meeting their objectives, they need to leverage data by converting it into useful and valuable information. It is their responsibility to make the data assets available to the different processes and people who need it.

Additionally, they need to ensure the data assets are of sufficient quality and appropriateness and that protections are in place to prevent the data assets from being abused and misused. Businesses can successfully leverage data and information assets through proactive data management.

Proactive data management involves applying definite policies, competencies, and disciplines through the data’s lifecycle. The diagram below explains the data lifecycle:

Businesses need to maintain effective data management throughout the data lifecycle, as it is the basis for reliable information. Businesses may use data differently at various times, for which they may need different management handling in the stages of the data lifecycle.

Example: Businesses may consider using critical data for discovery during an important event, but when the event ends, the information no longer holds value.

In other cases, the data’s lifecycle may be longer than the project that develops it. However, the resultant data may be accessible for several years, given businesses manage and preserve it properly. If they do, they can use the data in the future, which will increase the investment they made to generate it by increasing its efficacy and visibility.


Businesses need to incorporate data management and information management systems into their organizational structure to produce valuable data and use it towards accomplishing their short-term and long-term goals by developing solutions and minimizing risks.

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Editorial Officer, I'm an avid tech enthusiast at heart. I like to mug up on new and exciting developments on science and tech and have a deep love for PC gaming. Other hobbies include writing blog posts, music and DIY projects.

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