How to choose the best solution to ensure data quality?
Posted: Tue Jan 21, 2025 5:41 am
Data quality refers to a set of techniques, practices and methods used to obtain data that is suitable, robust and objective to make better decisions, achieve operational efficiency and ensure compliance.
There are a variety of software tools that offer functionality for data cleansing, integration, enrichment, and governance.
In this article we already told you what the steps are to achieve better data quality . In this new installment we will tell you what aspects to consider to choose the best solution to guarantee data quality.
5 dimensions to consider to achieve data quality
Every data strategy must incorporate data quality practices to ensure data integrity, accuracy, timeliness, consistency, and accessibility. What do we mean by each of these terms?
Integrity
Integrity is what determines whether the japan phone number lead data is complete enough to be used by the company's different teams for their analysis objectives and needs.
Accuracy
They must be reliable, correct and supported by some type of data governance scheme.
Update
Records of the information collected must be recent, that is, updated to be relevant.
Consistency
Data maintains its format and should not be distorted as it passes through the various stages of its life cycle.
Accessibility
They must be achievable by the people who must work with them without compromising compliance requirements.
In this other article we mentioned 6 key practices to achieve data quality .
Aspects to consider when choosing the best solution to ensure data quality
With the volume of data currently handled by organizations, it is impossible for a single person or team to successfully manage all of the data. An IT data integration and quality solution enables management of the entire data lifecycle and helps foster a data-driven culture in organizations.
A secure, unified, cloud-based platform is the best option for proper data management. It provides better accessibility, scalability, and reliability , while allowing users to share, operate, and transfer data, trigger, and run models.
Many specialized data quality tools require extensive experience for successful implementation and extensive user training. Interfaces can sometimes be very confusing for business users and require IT intervention.
On the other hand, the most basic programs can be too limited. While they can provide an excellent user experience, they do not apply to a comprehensive data quality process, where collaboration in data management becomes key.
The process of assessing the nature and condition of data stored across the enterprise is a vital first step in gaining control over organizational data. Choosing a solution that offers this functionality provides broad and deep visibility into data.
Another important point when selecting advanced data quality tools is that you can selectively share quality data using on-premises or cloud-based applications without exposing personally identifiable information to unauthorized persons.
There are a variety of software tools that offer functionality for data cleansing, integration, enrichment, and governance.
In this article we already told you what the steps are to achieve better data quality . In this new installment we will tell you what aspects to consider to choose the best solution to guarantee data quality.
5 dimensions to consider to achieve data quality
Every data strategy must incorporate data quality practices to ensure data integrity, accuracy, timeliness, consistency, and accessibility. What do we mean by each of these terms?
Integrity
Integrity is what determines whether the japan phone number lead data is complete enough to be used by the company's different teams for their analysis objectives and needs.
Accuracy
They must be reliable, correct and supported by some type of data governance scheme.
Update
Records of the information collected must be recent, that is, updated to be relevant.
Consistency
Data maintains its format and should not be distorted as it passes through the various stages of its life cycle.
Accessibility
They must be achievable by the people who must work with them without compromising compliance requirements.
In this other article we mentioned 6 key practices to achieve data quality .
Aspects to consider when choosing the best solution to ensure data quality
With the volume of data currently handled by organizations, it is impossible for a single person or team to successfully manage all of the data. An IT data integration and quality solution enables management of the entire data lifecycle and helps foster a data-driven culture in organizations.
A secure, unified, cloud-based platform is the best option for proper data management. It provides better accessibility, scalability, and reliability , while allowing users to share, operate, and transfer data, trigger, and run models.
Many specialized data quality tools require extensive experience for successful implementation and extensive user training. Interfaces can sometimes be very confusing for business users and require IT intervention.
On the other hand, the most basic programs can be too limited. While they can provide an excellent user experience, they do not apply to a comprehensive data quality process, where collaboration in data management becomes key.
The process of assessing the nature and condition of data stored across the enterprise is a vital first step in gaining control over organizational data. Choosing a solution that offers this functionality provides broad and deep visibility into data.
Another important point when selecting advanced data quality tools is that you can selectively share quality data using on-premises or cloud-based applications without exposing personally identifiable information to unauthorized persons.