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How can metadata be used to support data quality and validation?

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Data quality and validation are essential aspects of data engineering, as they ensure the reliability, accuracy, and usability of data for various purposes. Metadata, or data about data, can play a key role in supporting data quality and validation processes by providing information about the structure, meaning, origin, and lineage of data. In this article, you will learn how metadata can be used to support data quality and validation in different ways.

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1 Metadata types

Metadata can be classified into different types depending on the level of detail, source of generation, and purpose of use. Structural metadata describes the format, schema, and organization of data, such as the data type, length, and constraints of each field. Descriptive metadata provides information about the content and context of data, such as titles, descriptions, keywords, and categories. Administrative metadata records information about the creation, management, and access of data, like authorship, ownership, date, version, and permissions. Technical metadata captures information about the technical aspects of data like location, size, encoding, and compression. Lastly, operational metadata tracks information about data processing and transformation activities like source, destination, frequency, and status.

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2 Metadata standards

Metadata standards are sets of rules and guidelines that define how metadata should be created, stored, and exchanged. These standards can ensure data quality and validation by providing consistency, interoperability, and compatibility of data across different systems and platforms. For instance, the Dublin Core standard is a widely used standard for descriptive metadata that defines 15 basic elements for describing digital resources. ISO 19115 is a standard for metadata specific to geographic information, while the Data Documentation Initiative (DDI) is a standard for metadata specific to social and behavioral sciences. All of these standards define the structure, content, and encoding of metadata for their respective fields.

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Nicholas Leong
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Metadata management tools, like Datahub, offer detailed statistics for each field, aiding data validation efforts. For instance, using Datahub, we can identify anomalies in multiple fields. Given that it provides statistics like median and max values, engineers can craft tests to spot and address these anomalies. Another valuable feature of Datahub is its ability to showcase the most extensive query for every table. This allows us to monitor data issues in real time, ensuring our queries perform as intended. While metadata tools are packed with functionalities, it's the engineer's responsibility to harness their full potential.

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3 Metadata management

Metadata management is the process of creating, storing, updating, and accessing metadata in a systematic and efficient way. It can help support data quality and validation by providing tools and methods for data discovery, data lineage, data quality assessment, and data validation rules. For example, data discovery involves finding and understanding relevant data assets based on metadata attributes like keywords, categories, and tags. Data lineage involves tracing and visualizing the origin, transformation, and usage of data based on metadata events. Data quality assessment involves measuring and monitoring the quality of data based on metadata indicators. Lastly, data validation rules involve defining and enforcing rules for validating data based on metadata constraints like data type, length, and format.

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Kaushikkumar Patel

Data-Driven Solutions Architect | AWS Solutions | Credit Card Analytics
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In one of my projects, I had the opportunity to dive deep into metadata management. I was responsible for optimizing data quality and ensuring our data was reliable and valid. By leveraging metadata, I made it easier for the team to discover and understand the relevant data, using attributes like keywords and tags. I also focused on tracing data lineage, which was crucial for maintaining data integrity throughout various transformations. Metadata allowed us to monitor data quality continuously and enforce strict validation rules, ensuring our data was accurate and adhered to our standards. This experience really highlighted the importance of effective metadata management in maintaining high-quality, reliable data.

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4 Metadata best practices

Metadata can be a powerful tool for supporting data quality and validation, but it requires careful planning and implementation to ensure its effectiveness and usability. To start, you should define metadata requirements and objectives, including the types, standards, and sources of metadata that are relevant and useful for your data quality and validation goals. Additionally, you should use tools and techniques to generate, extract, and synchronize metadata from different sources. Furthermore, it is important to maintain the quality and currency of metadata by updating it regularly to reflect changes. Lastly, document and communicate metadata policies and procedures that explain the purpose, scope, and format of metadata as well as the roles of creators and users.

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5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How can metadata be used to support data quality and validation?

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1 Metadata types

2 Metadata standards

3 Metadata management

4 Metadata best practices

5 Here’s what else to consider

Data Engineering

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How can metadata be used to support data quality and validation?

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1 Metadata types

2 Metadata standards

3 Metadata management

4 Metadata best practices

5 Here’s what else to consider

Data Engineering

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