What is Data Management and Why Is it Important?

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Written By Haisam Abdel Malak

About: Haissam is a digital software product manager with 15 years of expertise in developing enterprise content management solutions. His core capabilities encompass digital transformation, document management, records management, business process automation, and collaboration.

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Businesses are dealing with a barrage of data which requires a lot of their time and attention. Data management becomes more difficult as the volume increases, and it can be quite costly to keep up with increasing growth.

Data management refers to the process of organizing, storing, and protecting data throughout its lifecycle. It involves the implementation of policies and procedures to ensure data accuracy, accessibility, and security. Effective data management can help businesses make informed decisions and gain valuable insights from their data.

Integrated data management necessitates an enterprise strategy as well as dependable techniques for accessing, integrating, governing, storing, and preparing collected data for analytics. In addition, businesses should always follow up with the latest DM trends to get the most out of their data.

The importance of data management is that it can assure the accuracy, accessibility, and availability of data to be processed and analyzed in order to make better, more informed business decisions and get deep insights into customer behavior, trends, and opportunities.

It compensates for the creation, execution, and management of plans that govern, safeguard, deliver, and enhance the value of data and information assets.

DM can be done by different people in different ways. For example, data scientists use it to collect and analyze information from large datasets. In contrast, business analysts use it to collect and organize information from various sources.

Proper data management is important because it helps decision-makers understand the information that they have gathered. It also helps them understand how the information relates to other pieces of data that they have obtained.

Data management examples could be building databases, uploading data to different data stores, creating backup and historical copies of files, and providing permissions to others in the organization to access certain data files.

In short, it is the procedure companies use to manage, control, protect, access, store, and maintain data sources. It starts with complete control of the data lifecycle, from data capture to storage in addition to understanding its components.

Data management strategy
data management model

I strongly recommend reading the below article discussing the challenges of DM

Data management challenges

Why Data Management is Important?

There are two types of data management: cloud data management and traditional. In this section, we will discuss the benefits of the traditional method.

Data management benefits can be seen across different areas such as marketing, IT infrastructure, business intelligence, and analytics.

Some of the benefits of effective data management include better decision making, better customer relationship management, increased productivity, and better data analytics.

The benefits of effective data management are:

1- Visibility

One of the most important data management benefits is the ability to have complete visibility over your data storage.

Effective data management will raise the visibility of your organization’s digital data assets. Employees may be certain that data will be at their fingertips when they need it.

2- Improves decision making

The importance of data management is that it can improve decision-making in a number of ways. First, it can provide accurate and up-to-date information about the current state of a system or data management process. This information can be used to identify patterns that may not be immediately apparent.

Second, It can help to identify and track key performance indicators. These indicators can be used to measure progress and identify areas where improvements need to be made.

Finally, it can help to create a historical record of decisions that have been made in the past. This record can be used to help inform future decision-making.

3- Eliminates redundancy

There are a number of ways that master data management can eliminate redundancy, such as:

1. Implementing a data governance framework: data governance will help ensure that only one version of the truth exists by defining roles and responsibilities, setting up data processing and procedures, and establishing controls. Check more for differences.

2. Cleaning up: This includes deduplicating, standardizing, and classifying master data to ensure that there is only one version of each record.

3. Enforcing data quality: This ensures that data is accurate and consistent by setting up validation rules and monitoring data for errors.

4. Managing metadata: This helps to ensure that data is properly organized and easy to find by creating a metadata repository and defining standards for naming and classifying data.

5. Automation: This can help to further reduce redundancy by automating tasks such as data cleansing, data quality checking, and data transformation.

4- Minimizes data loss

This is considered among the top data management benefits that organizations can leverage.

An effective approach is ensuring that all data sources are available when needed and that backups are performed on a regular basis in order to restore it in the event of an emergency.

Data management tools help reduce data loss by allowing users to keep an accurate record of their data and its status. This ensures that important information is not lost.

5- Improves compliance with regulatory requirements

Managing data can improve compliance with regulatory requirements by ensuring that it is accurate, complete, and timely. It can also improve compliance by providing visibility into data and by providing apps to help identify and correct errors.

6- Improves organizational efficiency

Improving business efficiency is one of the top benefits of good data management.

Managing data can improve organizational efficiency by reducing the amount of time spent searching for data, improving data quality, and making data more accessible to employees.

Organizations nowadays are looking for different ways to improve business efficiency in order to decrease costs and be agile. Data management can play a big role in turning organizations into data-driven organizations.

7- Improves data security

Data security can be improved by ensuring that data is properly backed up and by providing access control to data. Additionally, it can help to identify and track data breaches so that they can be quickly addressed.

This can be improved through different actions including:

– Data backup and recovery: This is an important process that should be implemented to ensure the security of data. It also helps in reducing the risk of an organization’s data loss due to unexpected events like natural disasters or hardware failure. It also helps in maintaining long-term storage for historical records and compliance purposes.

– Data encryption: Encryption is a method used to protect sensitive information from unauthorized access, misuse, or disclosure. Encryption helps in securing information from cybercriminals as well as from internal threats like disgruntled employees and hackers who may have access to the company’s network.

One way to improve data privacy is by using data management tools such as databases and encryption. These tools can help you organize your data in an efficient way, allowing you to better protect it from malicious attacks.

9- Reliability

Employees may be certain that data is always available and of good quality thanks to regulations. Companies can adapt to market changes and client requirements more effectively if they have trustworthy, up-to-date data.

Data management software gives you the ability to store and organize your data, making it easy to find when you need it. They can help increase reliability by saving time searching for lost files.

What are the 4 Types of Data Management?

The four types are data mining, data warehousing, data integration, and data analysis. Each one of these types contributes to the overall success of implementation and helps organizations get the most out of their data.

Data mining involves the extraction of raw data from a database in order to gain information about the data. The extracted information can then be used to make business decisions and other decisions.

Data warehouse refers specifically to the storage required by a database that stores large amounts of structured, semi-structured, and unstructured data.

Data integration is the process of combining master data from multiple sources into a single, coherent view. This process allows organizations to make better decisions, improve operational efficiency, and gain a competitive advantage.

Analyzing data is the process of organizing, manipulating, and understanding data. It can be used to find patterns, and relationships in master data.


Understanding how master data management works and how businesses can put up the most effective policies and procedures to get the most out of it and applying best practices is the key to unlocking the potential of data as a driver for organizational success.

source: Microsoft PowerPoint – DAMA DMBOK2 (dama-dk.org)

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