Emily Washington, Executive Vice President of Product Management at Infogix, a multinational data controls and analytics software company, talks about why Metadata management is critical for businesses that want to uncover accurate, reliable business intelligence. A successful metadata management strategy operates within a comprehensive data governance framework that prioritizes high-quality data.
It takes many pieces to make up a data map, but the most important piece, metadata, is the foundation of any current or future data-driven initiative. Simply put, metadata is data about data.
Metadata is critical because it helps answer questions about the data that helps organizations be successful—with insights, such as location, date of creation, meaning, usages, access restrictions, owners, and more. Metadata provides the necessary details about an organization’s data assets and empowers data users to analyze information precluding the use of the wrong data set or misinformation. Metadata allows you to equip data users with the necessary details about their data assets so they can better understand and effectively deploy data resourcesOpens a new window to support any data analytics efforts. Metadata also supports data governance, regulatory compliance, and other data managemenOpens a new window t demands.
To achieve the most success, businesses need to organize metadata into a central repository. But before taking on this endeavor, data users must first understand the varying types of metadata.
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Uncovering Metadata from Different Perspectives
To achieve a comprehensive understanding of where data lives across the enterprise and how it’s used, companies must gather and arrange three different types of metadata.
- Physical Metadata: This type of metadata details the specifics of where your data lives. For example, the system it resides in, the schema, table, and column or key-value level of detail. Physical metadata is machine-generated and automatically captured from software systemsOpens a new window .
- Logical Metadata: The details surrounding how data is linked to form larger sets and how data flows through various systems and processes, from ingestion, through storage, transformation and consumption are known as logical metadata. With logical metadata, you can trace the data’s path through the entire data supply chain.
- Conceptual Metadata: Analyzing data’s meaning and purpose within the company to provide a business context for data is critical. Conceptual metadata represents the accumulated knowledge of subject matter experts within the organization, providing critical information about data usage for business users.
However, metadata management must also operate within a comprehensive data governanOpens a new window ce framework that prioritizes data quality execution, combines people and processes, cultivates open communication and establishes a data-driven culture enterprise-wide. With the proper governance foundation, transparent processes and the right people to execute the work, implementing a successful metadata management strategy becomes easier.
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Integrating Data Quality into Governance to Manage Metadata
Effective data governance promotes collaboration between data owners, data stewards and data users to give both business and technical users clarity into their data landscape.
Data governanceOpens a new window produces a collaborative approach to data understanding, empowering diverging lines of business to work together to define and document metadata. As a result, teams build consensus regarding standard data definitions.
Data quality also plays an integral role in data governanceOpens a new window and metadata management. It’s one thing not to trust your data; it’s an entirely different issue when using data you thought was trustworthy turns out to be inaccurate. Getting understandable, contextual metadata to business users requires trustworthy data, especially when business-oriented data enters into the organization from internal and external sources. As a result, business users across departments need to make data quality a top priority.
But the essence of data quality starts with the right tools. Traditional data quality tools only address standardization, cleansing, enrichment, etc., leaving key requirements around business processes and data lineage in question, as data moves through different points. If an external source sends data through 40 different processes to complete a compliance report, how can anyone be sure its quality remained intact?
With varying data quality throughout diverging lines of business and systems, it is essential to map data quality metrics back to the data governance program, inclusive of metadata, and ensure the integrity of critical data elements.
With a foundation of understanding, trust, support and collaboration, successful management of metadata ensures the availability of data for all data users while reducing confusion, ensuring appropriate data usage and, ultimately, leveraging data to generate actionable business insights.
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