Guide to achieving effective data governance
Posted: Tue Jan 21, 2025 5:18 am
As actionable information becomes essential for effective business development, ensuring data governance plays a crucial role.
After all, without quality and reliable data, trusting the information obtained from its analysis — or from the content created with artificial intelligence based on these records — is impossible.
However, if we take into account that the volume and complexity of data is currently increasing rapidly, carrying out a data management strategy using only one person or an internal team is becoming increasingly difficult and expensive.
To achieve effective data management, it is essential to create a strategy that includes three core elements: people, processes and technological tools.
In this article, we will delve into each of these components that help drive a successful data governance strategy.
Data governance, a crucial concept to mitigate risk and ensure efficiency
Data governance is a central element for georgia phone number lead companies that deploy their business strategies based on different types of data.
This concept contemplates the effective management of data, developing an organized structure in which responsibilities and processes are defined in order to guarantee the proper use of records and greater democratization of information.
Developing an appropriate data governance strategy is essential for several reasons.
First, it promotes reliable decision-making, supported by solid data, and fosters confidence in the data obtained, reducing the time and resources dedicated to searching, reconciling and cleaning information.
In addition, data governance ensures compliance with current regulations, avoiding fines and penalties and protecting companies' reputations in the eyes of partners and customers.
Finally, by establishing clear policies and controls on how data is handled and protected, you mitigate security risks, minimizing the chances of sensitive data being exposed.
People, processes and technology, the triad that drives the success of the data governance strategy
Today, it’s not enough to have a data governance team. For the strategy to be successful, it’s critical to create a collaborative culture in which high-quality data thrives.
This involves not only establishing the appropriate processes and functions, but also having staff with the necessary skills and expertise, as well as technological solutions that facilitate collaboration around data.
Processes
Data governance and data quality go hand in hand. In this sense, governance is part of a broader, ongoing data management process that includes discovery, standardization, consolidation, operationalization, and ongoing monitoring of data quality and integrity.
Among the components of data quality maintenance , governance consists of defining data owners, roles and policies related to them, including various tasks:
Data ownership and management to assign owners that ensure accountability and oversight
Data modeling and metadata management to create logical and physical data models that represent organizational assets
Data lineage to visualize and trace data back to its source
Analyzing the impact of data on existing reports, dashboards and analytical models
Authentication of users, in order to determine if they are legitimate
Detailed access controls to enable or restrict users and functions
Data field obfuscation to mask sensitive information and remove personal data identification
People
In recent years, the tasks of data management personnel have multiplied and evolved.
Today, data teams include roles such as data protection officer, data architect, data steward, data engineer or developer, data scientist, and data analyst.
After all, without quality and reliable data, trusting the information obtained from its analysis — or from the content created with artificial intelligence based on these records — is impossible.
However, if we take into account that the volume and complexity of data is currently increasing rapidly, carrying out a data management strategy using only one person or an internal team is becoming increasingly difficult and expensive.
To achieve effective data management, it is essential to create a strategy that includes three core elements: people, processes and technological tools.
In this article, we will delve into each of these components that help drive a successful data governance strategy.
Data governance, a crucial concept to mitigate risk and ensure efficiency
Data governance is a central element for georgia phone number lead companies that deploy their business strategies based on different types of data.
This concept contemplates the effective management of data, developing an organized structure in which responsibilities and processes are defined in order to guarantee the proper use of records and greater democratization of information.
Developing an appropriate data governance strategy is essential for several reasons.
First, it promotes reliable decision-making, supported by solid data, and fosters confidence in the data obtained, reducing the time and resources dedicated to searching, reconciling and cleaning information.
In addition, data governance ensures compliance with current regulations, avoiding fines and penalties and protecting companies' reputations in the eyes of partners and customers.
Finally, by establishing clear policies and controls on how data is handled and protected, you mitigate security risks, minimizing the chances of sensitive data being exposed.
People, processes and technology, the triad that drives the success of the data governance strategy
Today, it’s not enough to have a data governance team. For the strategy to be successful, it’s critical to create a collaborative culture in which high-quality data thrives.
This involves not only establishing the appropriate processes and functions, but also having staff with the necessary skills and expertise, as well as technological solutions that facilitate collaboration around data.
Processes
Data governance and data quality go hand in hand. In this sense, governance is part of a broader, ongoing data management process that includes discovery, standardization, consolidation, operationalization, and ongoing monitoring of data quality and integrity.
Among the components of data quality maintenance , governance consists of defining data owners, roles and policies related to them, including various tasks:
Data ownership and management to assign owners that ensure accountability and oversight
Data modeling and metadata management to create logical and physical data models that represent organizational assets
Data lineage to visualize and trace data back to its source
Analyzing the impact of data on existing reports, dashboards and analytical models
Authentication of users, in order to determine if they are legitimate
Detailed access controls to enable or restrict users and functions
Data field obfuscation to mask sensitive information and remove personal data identification
People
In recent years, the tasks of data management personnel have multiplied and evolved.
Today, data teams include roles such as data protection officer, data architect, data steward, data engineer or developer, data scientist, and data analyst.