How can you define metadata standards?

Setting metadata standards was essential to our data governance in the finance sector. First, we divided the metadata into three categories: operational, business, and technical. Technical metadata defined data formats, business metadata offered context on the transaction and balance tier type, and operational metadata traced data lineage from the source system for processing credit card data analytics. This organized approach improved data-driven decision-making by ensuring uniformity and clarity across our data assets.

We are currently building an enterprise metadata. As much as we wanted to use automation, it is always best to define it based on the business needs. In financial industry, reporting submission to Central Bank is a must and it has to follow through a defined parameters to ensure the submissions are tally with Central bank standards hence minimize error which requires re-submission. There are also additional data that only applicable to a specific business has hence gathering all these metadata will take sometime. What important is to really look into what the organisation is looking into and how those data is important to them for various purposes i.e reporting, customer analysis, product management and etc2.

In the landscape of data management and governance, identifying metadata types has traditionally been a daunting task. But with the power of advanced technology and the intelligence of AI, we're now able to seamlessly pinpoint and categorize metadata types within our framework. This precision not only streamlines our data operations but also empowers us to make more informed decisions. It's a transformative step towards robust data management.

When choosing a metadata standard, it is important to consider the specific needs of your organization and the types of data resources that you need to describe. Some of the most common metadata standards include: Dublin Core: A general-purpose metadata standard that can be used to describe a wide variety of digital objects. ISO/IEC 11179: A metadata standard for scientific datasets. MODS: A metadata standard for library materials. METS: A metadata standard for digital objects. PREMIS: A metadata standard for digital preservation.

Metadata standards can be defined as a set of guidelines or specifications that determine how information about data is structured, organized, and described. These standards ensure consistency and interoperability across different systems and platforms. They provide a common language for describing data attributes such as format, quality, provenance, and other relevant information. By adhering to metadata standards, organizations can effectively manage and exchange data, making it easier to search, discover, and understand.

When setting up metadata sources, I have found that it's essential to strike a balance between manual and automated processes. Manual metadata involves direct input, like creating business glossaries or data dictionaries. This method allows for customization and can address specific business needs. Automated metadata is generated without constant human intervention, using tools that capture details such as data lineage or quality. It's a little more efficient. To decide between manual and automated, you want to consider factors like the volume of data, frequency of updates, and the precision required. Leveraging the right combination ensures data is both accurate and accessible, optimizing the way the organization utilizes its information.

And along with your metadata sources, make sure you know who your experts or process owners are. Remember one important thing regarding data management, in this discipline work with human judgement to improve data, and that is the most essential thing about metadata.

The metadata standards will help to define the requirements when crowd sourcing for business glossaries, data dictionaries as well as data catalogs when these are used across businesses and functions. The manually created metadata should be linked to the data lineages to help with the data flow processes.

This point about balancing manual and automated methods is crucial. Just because something can be automated doesn't mean it should be. Sometimes, the human touch on metadata is what makes the difference, especially if you're dealing with sensitive or complex information.

Identifying manual and automated metadata sources is key to effective metadata management. Manual metadata ensures alignment with organizational standards, while automated metadata offers dynamic insights with less manual effort. Utilizing a mix of tools like data catalog software and metadata management systems can simplify metadata collection, enhancing data governance endeavors. #MetadataManagement #DataGovernance #DataIntegration

Metadata is closely related to the “business reality” of your organization. To make maintaining and keeping metadata up to date easier, it would be smart to sync these processes with business transformation projects as part of the deliverables or “definitions of done”. As with other data types, metadata is only as good as people trust it - and it’s always easier to break trust and harder to recover it, so go for preventative measures rather than reactive ones.

There's a noticeable gap between data producers and data consumers. In software development, it's essential that metadata generation policies are both understood and included as part of the criteria for implementing systems. Furthermore, the introduction of the Data Governance Act (DGA) promises improved clarity. With its legal enforcement, it ensures that data intermediaries are accountable for their information sources. This elevates the discussion beyond just the metadata of internal systems. Now, all external data sources will also need to adhere to specific guidelines.

I'd define them in a data quality standard document as follows - Definition: A clear and concise explanation of the attribute, ensuring a shared understanding of its purpose. - Usage Intent: Describes where and how the attribute should be used for consistency. - Acceptable Values/Examples: Provides valid values and illustrative data-completion examples. - Obligation: Specifies if filling the data is mandatory or optional. - Data Entry Method: Identifies whether the data is filled automatically or manually. - Business Rules: Outlines pertinent rules like code lists, entity dependencies, default values, format requirements, and data quality validation rules. - Data Quality Owner: Designates the individual or team accountable for data accuracy

Defining relevant attributes for each metadata type is crucial for a clear, comprehensive documentation. It tailors the metadata to organizational needs and enhances data understanding across technical, business, and operational realms. Your delineation of attributes for each metadata type provides a clear pathway for organizations to follow for effective metadata documentation. #MetadataManagement #DataGovernance

You have to build a template what guides the definition and the needs of metadata. If you dont do that, you may have less information that uou need for your projetcs or for your data democratization or your data management.

Incorporating metadata standards into a data governance framework is a pivotal step toward automation and efficiency. From my experience, the process of capturing technical metadata, especially aspects like data lineage and the structure of the data lake, can be completely automated. We've previously implemented this automation, seamlessly capturing technical metadata like the lineage and the data lake architecture. By integrating these automatically derived insights into our governance framework, we've streamlined our data operations, ensuring accuracy and consistency without manual intervention.

The basis of a solid data governance framework is a proper understanding of the data that exists in and around the business and knowledge about how it is utilised. As part of this discovery process, the opportunity exists to capture key information that explains what every piece of data are, their meaning and their purpose. Documenting this in line with other aspects of the overall data governance framework, ensures strong alignment and consistency in the metdata standards.

Few policies to be considered when aligning metadata standards to data governance initiatives are: - data architecture policy (including data integration and interoperability, if any) - data security policy (related back to acceptable encryption in company) - data quality policy - data access policy (related back to minimal access, IAM and database credential policies, if applicable for the company) - data storage and retention policy - document management policy (applicable for data resided outside of database such as Word, PPT, etc.) - data modeling standards and guidelines, if any Essentially metadata is also data bounded by governance context even it complements governance framework.

In regulated Regions, metadata standards help organizations comply with data governance, privacy, and security regulations by providing a structured way to document relevant information about data sensitivity, access restrictions, and privacy considerations, aiding in data protection efforts. As Data grows one needs to manage more data, metadata standards provide a scalable approach to organizing and cataloging data assets. These standards need to follow the best practices of the regions, domain which enhances data understanding, quality, governance, and accessibility, while standards provide the structure and consistency needed to ensure that metadata serves its intended purpose efficiently and accurately.

These standards are never one time implementation, the team has to be agile and dynamic in terms of reviewing the standards periodically, promote long-term sustainability by ensuring that metadata remains understandable and usable even as data environments evolve. An updated metadata reduces ambiguity and the need for custom mapping when integrating data from various sources. This leads to more efficient data integration and analytics processes leading to better decision making and empowering advanced AI capabilities within an enterprise.

All kind of pattern shoud be manage. Its mean that you have to evaluate, review and upgrade the metadata standards. You have to observe what period you define t? review all the standards. This is important to alingn the standards with the changes trought the time

It depends on the unit of analysis. Everything should be about the unit of analysis. Is it measurement or evaluation? Is it impact or influence?

The dynamic nature of data assets and business needs necessitates a regular review and update of metadata standards. Establishing a disciplined process for these reviews, coupled with feedback incorporation, ensures that metadata standards remain relevant and continue to bolster data quality, usability, and trustworthiness. Your emphasis on measuring the impact of metadata standards is indeed a prudent approach to maintain a resilient data governance framework. #MetadataManagement #DataGovernance

First, we have to define / describe / illustrate what metadata is. Show me a set of metadata or even better and example of what you think metadata is and we can go from there.

A metadata standard is a set of specifications and guidelines that define the structure, content, and format of metadata for a particular domain or application. Metadata refers to data that describes and provides additional information about other data. It helps in understanding, managing, and organizing data resources effectively. By adhering to a metadata standard, organizations and systems can enhance data discoverability, interoperability, and reusability. It facilitates data integration, sharing, and exchange, enabling efficient data management and supporting various data-related activities and applications.

Develop policies for data retention and archiving that align with legal and compliance requirements. Classify data using metadata tags, allowing for prioritization in governance efforts. Regularly monitor and assess metadata quality through quality checks and data profiling. Make metadata easily discoverable through a user-friendly data catalog with efficient search functionality. Encourage metadata creators to document their sources, methodologies, and assumptions. Foster collaboration among data stakeholders and provide training to ensure awareness of metadata's significance. Stay informed about data governance regulations and compliance requirements that may affect metadata management.

Automation is the key for long term sustainability of the model. - Proactive monitoring of access controls. - Closely link Data Governance framework to Data Quality rules to have metrics of Accuracy, completeness and freshness of the datasets and attributes - Automated refresh of metadata, Alerting for additional information, orphan datasets/attributes

Indeed, are we talking about technical, architectural, business, semantic… All of these are needed to provide full context and meaning to data.