We explore what Data Management is, one of the most important disciplines for businesses to leverage the value of their data.

In today's business world, where information is as valuable as time, data management emerges as the essential foundation for progress and intelligent decision making.

In this sense, data management technologies are becoming increasingly relevant in the corporate world. In this publication we explore what exactly Data Management is, delving into its key processes, its transformative impact on business operations and how it stands as the catalyst for unlocking the hidden potential of data.


What is Data Management?

Data management refers to the set of practices, processes, technologies and policies that an organisation implements to efficiently manage its data throughout its lifecycle. The main objective of data management is to ensure that data is available, accurate, secure and usable when it is needed. In this sense, data management is closely linked to data governance, as both disciplines are interconnected in the field of information management within an organisation and share common objectives.

Some of the key activities included in data management are:

  • Data collection: The acquisition of data from a variety of sources, both internal and external.
  • Storage: The creation of appropriate storage systems for the data, whether in databases, file systems or in the cloud.
  • Data Quality: Ensuring the quality and consistency of data, through cleansing, validation and standardisation.
  • Security: Implement measures to protect data against unauthorised access, loss or damage.
  • Data integration: Ensuring that data is integrated in a coherent and efficient manner across the organisation, enabling its analysis and effective use.
  • Data Governance: Establish policies and standards for the management and use of data, including the definition of roles and responsibilities.
  • Maintenance: Perform regular maintenance tasks, such as backups, upgrades and purges of obsolete data.
  • Data analysis: Facilitate access to and analysis of data to extract valuable information that can support decision-making.

Data management processes and policies are essential for any data-driven organisation. Data management not only facilitates access to data, but also ensures data quality, optimises its use by employees, ensures its security and maximises its business value.

The 3 key processes of data management

The three main processes of data management are:

  1. Data Collection: This process involves the collection of data from various sources, both internal and external to the organisation. It includes the acquisition of raw data, either generated within the company's operations or obtained from external entities.
  2. Data Storage: Once collected, data must be stored in a structured and organised manner. This process involves selecting appropriate storage systems, such as databases, data warehouses or cloud storage solutions, to efficiently manage and retrieve data when needed.
  3. Data Processing and Data Analysis: This process involves transforming data into meaningful information through processing and analysis. It includes cleaning and validating data, performing calculations and generating insights. Data processing and analysis are crucial for making informed decisions and extracting valuable knowledge from data.

These three processes are interconnected and form the basis of effective data management within an organisation. Properly managing data throughout its lifecycle ensures that information is accurate, accessible and can be used to support business operations and decision making.

The importance of data management in business

Data management is crucial for a company for several reasons: 

  1. Decision Making: Facilitates data access and analysis, enabling more informed decision-making based on relevant and accurate information.
  2. Operational Efficiency: Optimises data management, leading to greater efficiency in day-to-day business operations.
  3. Regulatory Compliance: Helps comply with privacy and data security regulations and standards, avoiding legal penalties and protecting the company's reputation.
  4. Innovation: Facilitates innovation by providing a solid foundation for the development of new data-driven solutions and services.
  5. Competitiveness: Companies that manage their data effectively can gain competitive advantage by using information strategically.
  6. Improved Data Quality: Contributes to maintaining accurate and up-to-date data, which improves the quality and reliability of available information.
  7. Data Security Protection: Implements measures to protect data security, preventing unauthorised access and possible loss or damage.
  8. Governance and Compliance: Establishes policies and standards for the ethical and responsible use of data, ensuring compliance with internal and external standards.

In short, data management is essential to maximise the value of data, mitigate risk, drive efficiency and maintain competitiveness in an increasingly information-driven business environment.


What are the four types of data management?

There is no widely recognised classification of data management into four distinct types. However, we can speak of four general categories or aspects of data management:

  1. Operational Data Management: Involves the day-to-day processes and practices for collecting, storing and using data within an organisation. It includes activities such as data entry, updating records and maintaining databases to support routine business operations.

  2. Analytical Data Management: Focuses on the preparation and analysis of data to derive information and support decision making. It involves data processing, data analysis and the creation of reports or dashboards for business intelligence purposes.

  3. Data Governance: A set of policies, processes and controls that define how data is managed within an organisation. Data governance processes ensure that data is accurate, secure and compliant with relevant regulations. It involves defining roles and responsibilities, establishing data quality standards and implementing security measures.

  4. Data Architecture and Infrastructure: This aspect is concerned with designing and managing the overall structure and technology infrastructure to handle data. It includes decisions on database systems, data warehouses, data lakes and other storage solutions. Ensuring adequate data architecture and infrastructure is crucial for data scalability, performance and accessibility.

While these categories provide an overview, it is important to note that data management is often an integrated and holistic approach, encompassing various processes and practices to ensure the effective and efficient use of data within an organisation.

Data Management and Master Data Management (MDM)

La gestión de datos (data management) y la gestión de datos maestros (master data management o MDM) están relacionadas pero se centran en aspectos diferentes de la gestión de datos dentro de una organización.

Data management and master data management (MDM) are related but focus on different aspects of data management within an organisation.

  1. Data Management: This term encompasses all practices, processes and technologies used to comprehensively manage data throughout its lifecycle. It includes aspects such as data collection, storage, processing, quality, security and analysis. Data management is a broader approach that deals with all data in the organisation, regardless of its type or specific use.

  2. Master Data Management (MDM): A subset of data management that focuses specifically on master data management. Master data are the fundamental and common data sets that are used throughout the organisation, such as customers, products, suppliers, employees, etc. MDM focuses on ensuring the consistency, integrity and accuracy of this master data across all enterprise systems and applications.

Ultimately, master data management (MDM) is an integral part of data management. Data management addresses all data in the organisation, while MDM focuses specifically on ensuring the quality and consistency of key master data that is used cross-cutting across the enterprise. Both are essential to ensure the reliability and usefulness of information within an organisation.

At Bismart we are experts in Master Data Management (MDM). We have worked with numerous companies in this area and have our own Master Data Management solution that provides a unified view of master data across the organisation, improving interoperability between systems through effective master data management.

Posted by Núria Emilio