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What Is Data Lineage?

Data lineage is the process of tracing the movement history of data over its life cycle. It provides insights into the data’s source, updates, flow and organizational touch points. Insight from data lineage archives gives you better control and observability of data transformations and quality, as well as information on improving data journeys for consistent data quality, precision and formatting. It is an integral part of data governance.

Data Lineage for Business Applications

Data lineage tracking provides insight into your data history by making metadata visible and accessible. Metadata is the information about your data, such as its source, format, creator, relevant dates, size and more. Data lineage models and systems use mapping, diagrams and other frameworks to show data movement details. Ways to track data lineages include:

  • Data tags
  • Pattern identification
  • Parsing
  • In-system tracking

The first three techniques often require external systems or tools to monitor data history, while the fourth relies on your organization’s data ecosystem having built-in metadata capabilities.

Data lineage tools like ours help you gather metadata and organize data into suitable formats or frameworks for convenient use and access.

Lineage Benefits and Use Cases

The metadata diagrams and visuals available through lineage tracking have many applications and benefits for your organizational data governance and management. Advantageous uses of lineage tracking include the following:

  • Managing data information during system changes or migrations
  • Giving an enhanced analysis of data relationships across your organization
  • Providing insights into organizational changes with the visibility of data transformations
  • Enabling data security, risk management and auditing compliance for privacy regulations
  • Increasing understanding of the impact of data errors, anomalies and other issues on performance
  • Simplifying data patterns and trends into easy-to-consume models
  • Monitoring errors and inconsistencies during data ingestion and processing
  • Tracking user access, queries, reports, data user and other applications of datasets

Lineage Challenges and Best Practices to Address Them

Here are the five common challenges to effective data lineages and how you can address them:

Standardization

Ensuring cohesive and consistent systems and procedures is essential to lineage success. Inconsistencies can lead to complicated data, confusion and inaccuracies that impact strategy and business intelligence. Establish clear standards, frameworks and systems relevant users can utilize when entering, accessing or adapting data. This includes common schema, diagrams and formatting, must-fill fields and more. Standardization will help maintain and integrate consistent data into various parts of your ecosystem.

Integration

Another aspect of cohesive integration is system compatibility. Ensure your chosen data lineage tools are compatible with existing solutions so they can perform efficient tracking.

Big Data

Large data volumes can challenge your system functionality and data flow tracking. Advanced lineage tools with automation can enable you to ensure effective and accurate tracking for big datasets and maintain data quality, integrity and security.

Frequent Transformations

Frequency is another challenge for data lineage implementation. It’s crucial that you select an excellent automated system to help you monitor every step of your data’s life cycle and trace transformations, access and other data movement factors.

Complex Ecosystems

Many modern institutions have multiple data sources, users and actors responsible for data transformations, leading to complicated data environments. Standardization, integration and intentional data governance can promote excellent data lineages when supported by superior systems and capable technologies.

Essential Data Lineage Tracking Tools and Features

Employing advanced technologies and systems can help you enhance your data lineage tracking, implement the best practices above and address inherent challenges. Data tools and system features to incorporate into your data ecosystem for superior data lineages include: