Implementing Data Quality through Metadata
Posted: Tue Jan 21, 2025 10:48 am
A metadata strategy helps data quality by identifying poor, erroneous or missing data and engaging in data governance.
Good metadata management can lead to good data quality, as having and trusting metadata can identify poor data, incorrect data, and missing data.
Similarly, having good metadata shows us that the company cares about understanding and improving data management and indicates that the organization is committed to good data, hence an improvement in data quality .
What is metadata?
The standard definition says that metadata is data about data . But that doesn't mean anything to most people. We can say that metadata is the context and descriptions of the data (what it is, what it means, where it is located, how it is used, etc.)
Technical metadata is used by IT staff to manage data and linkedin database whether it is correct according to technical specifications (whether it is actually in date format, whether it is updated according to the calendar, etc.)
On the other hand, business-related metadata is often not well understood because it uses business definitions, context, usage, etc. of that data. Technical metadata is much easier to capture than business metadata , and most organizations focus solely on this technical metadata, if they pay attention to the metadata at all.
Data quality as an essential part of MDM
What is the goal of a metadata strategy?
The main objectives of a metadata strategy should be the following:
Develop a business understanding of the intrinsic value of metadata to the entire organization.
Recognize the value of metadata to improve data quality.
Understand the different types of metadata and choose those categories of each type that are important to this organization.
Decide how the organization will use the metadata.
Select the technology that will support your metadata activities.
Reuse is essential . There is no need to constantly create the same metadata over and over again.
Capturing them once, in a controlled, centralized environment, and managing them properly with data governance , should reduce the need to recreate them .
Good metadata management can lead to good data quality, as having and trusting metadata can identify poor data, incorrect data, and missing data.
Similarly, having good metadata shows us that the company cares about understanding and improving data management and indicates that the organization is committed to good data, hence an improvement in data quality .
What is metadata?
The standard definition says that metadata is data about data . But that doesn't mean anything to most people. We can say that metadata is the context and descriptions of the data (what it is, what it means, where it is located, how it is used, etc.)
Technical metadata is used by IT staff to manage data and linkedin database whether it is correct according to technical specifications (whether it is actually in date format, whether it is updated according to the calendar, etc.)
On the other hand, business-related metadata is often not well understood because it uses business definitions, context, usage, etc. of that data. Technical metadata is much easier to capture than business metadata , and most organizations focus solely on this technical metadata, if they pay attention to the metadata at all.
Data quality as an essential part of MDM
What is the goal of a metadata strategy?
The main objectives of a metadata strategy should be the following:
Develop a business understanding of the intrinsic value of metadata to the entire organization.
Recognize the value of metadata to improve data quality.
Understand the different types of metadata and choose those categories of each type that are important to this organization.
Decide how the organization will use the metadata.
Select the technology that will support your metadata activities.
Reuse is essential . There is no need to constantly create the same metadata over and over again.
Capturing them once, in a controlled, centralized environment, and managing them properly with data governance , should reduce the need to recreate them .