Remove data-lineage
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7 Data Lineage Tool Tips For Preventing Human Error in Data Processing

Smart Data Collective

Errors in data entry might have serious effects if they are not discovered quickly. Human mistake is the most common cause of data entry errors. Since typical data entry errors may be minimized with the right steps, there are numerous data lineage tool strategies that a corporation can follow.

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Maximize Data Impact with an Effective Data Lineage Strategy

Dataversity

High-quality data can produce powerful insights that enhance decision-making. For data-driven organizations, this leads to successful marketing, improved operational efficiency, and easier management of compliance issues. However, unlocking the full potential of high-quality data requires effective Data Management practices.

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Focus on Your Most Critical Data Lineage to Manage Risk

Dataversity

Data lineage is extremely important since unknowns in the data flow are a root cause of enterprise risk. Enterprise Risk Management and Data Governance professionals are teaming up to implement the “Three Lines of Defense” model (3LOD). The goal of 3LOD is to provide an early detection and risk mitigation warning system.

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Article: Building End-to-End Field Level Lineage for Modern Data Systems

InfoQ Articles

In this article, authors discuss the data lineage as a critical component of data pipeline root cause and impact analysis workflow and how automating lineage creation and abstracting metadata to field-level helps with the root cause analysis efforts. By Mei Tao, Xuanzi Han, Helena Muñoz.

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Exploring Data Provenance: Ensuring Data Integrity and Authenticity

Astera

What is Data Provenance? Data provenance is a method of creating a documented trail that accounts for data’s origin, creation, movement, and dissemination. It involves storing the ownership and process history of data objects to answer questions like, “When was data created?”, “Who created the data?”

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What is Metadata Management? Benefits, Framework, Tools, Use Cases, Best Practices

Astera

Metadata refers to the information about your data. This data includes elements representing its context, content, and characteristics. It helps you discover, access, use, store, and retrieve your data, having a wide spread of variations. Examples include time stamps, execution logs, data lineage, and dependency mapping.

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What is a Data Catalog? Features, Best Practices, and Benefits

Astera

What is a Data Catalog? A data catalog is a central inventory of organizational data. It provides a comprehensive view of all data assets in an organization, including databases, tables, files, and data sources. This practice is especially applicable to large organizations with scattered data.