Struggling to know how data enters the organization, where it exists or transforms as it travels through different processes and systems, data leaders coined the term data lineage to visualize data’s heritage from a dependency perspective (order). Lineage documents data’s origins, movement, uses and any changes during its journey.
Data lineage allows organizations to track data as it moves through each destination, tracing data alterations and errors back to their source. When data changes, so does its meaning, purpose, and data quality score, resulting in data inaccuracies. Data lineage empowers data users to pinpoint the cause of data issues and fix them. It also provides an information audit trail.
When trying to explain lineage, different people will offer different definitions because their views depend on their role as a user and the question they are trying to answer.
Sample questions that lineage can help answer:
We can classify those views into two groups. The first view is technical data lineage, which oversees elements that are crucial to compliance, operations, procedures, transformations and data combinations. The other is business data lineage, it provides a view of technical lineage at an application or business layer, which allows business users to understand data’s role in business processes.
Technical data users typically reside in the IT department. With technical data lineage, IT resources can identify where sensitive data is located, how it changes, who has access, how its shared and its quality levels. This information empowers technical users to answer data questions regarding how sensitive it is and its quality, to ensure compliance and accuracy.
Business data lineage provides insight into how data fits the business and the various business outcomes if information transforms. Data lineage from a business perspective allows users to answer questions about the data as it relates to the business, providing transparency into the data analytics channel to produce accurate, reliable business intelligence. Business data lineage looks beyond the connections between metadata, it looks at rules, frequency, criticality, usage context, and determines ownership and documents knowledge.
Following both business and technical lineage through a data governance framework helps organizations develop standard data definitions and document the information. Furthermore, companies can include quality scores and business lineage to build data trust and connect data definitions to business cases.
To make a detailed data catalog a reality, organizations must adopt a data intelligence platform with integrated and automated data lineage capabilities. By incorporating automated data lineage features, companies automatically profile data and extract metadata as soon as it enters the business. However, capturing lineage is not enough, it is also critical to be able to view it and browse it. The ideal lineage view would present users with a “lineage browser” that allowed them to view the lineage at the perspective that is most meaningful to the question they are trying to answer. As a result, business users quickly uncover patterns and connect information to different business processes and outcomes. This provides detailed business context and knowledge around all enterprise data assets.
Data quality capabilities within the platform also measure the impact data quality efforts have against business expectations. By understanding the effects data quality has on different business environments, business users gain trust in their data.
With an extensive business-ready data catalog that incorporates data quality and automated data lineage tools, companies reduce the risk of data misunderstanding among business users. Instead, they ensure business users have high-quality, business data on demand to generate increased profits.
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For additional information about the basics data lineage, read this definition from TechTarget’s WhatIs.com: https://whatis.techtarget.com/definition/data-lineage.
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