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Home > Blog > Power BI

What is Data Lineage in Power BI: Quick Insights

So, what is data lineage? Is it a trendy new DNA test for your spreadsheet?

What is Data Lineage

No. Data lineage is the Sherlock Holmes of the digital world, revealing the data’s origins, transformations, and destinations.

Data lineage answers the fundamental question of “Where did this data come from?” This question lies at the heart of data governance, allowing you to trace data origins, movements, and transformations. Data lineage illustrates the journey of data from its source through various processes and systems to its destination. This transparency is invaluable for ensuring data quality, understanding data dependencies, and meeting regulatory requirements.

Moreover, data lineage empowers you to make informed decisions based on trustworthy data. Documenting the data flow enhances your analytical capabilities, mitigates risks, and streamlines compliance efforts.

In essence, understanding what is data lineage provides a foundation for leveraging data as a strategic asset.

Join us as we unravel the complexities of what is data lineage and explore its practical applications. This exploration will provide valuable insights into harnessing the power of data within your organization.

It’s time to decode the story hidden within your data’s lineage.

Table of Content:

  1. What is Data Lineage?
  2. What is the Data Lineage Meaning?
  3. Why Do Businesses Use Data Lineage?
  4. Data Lineage vs. Data Provenance vs. Data Governance
  5. How to Implement Data Lineage?
  6. What are the Different Data Lineage Techniques?
  7. 4 Types of Data Lineage
  8. 5 Real-Life Data Lineage Examples 
  9. Data Lineage Tools
  10. Data Lineage Visualization in Power BI
  11. How to Conduct Data Lineage Analysis in Power BI?
  12. Challenges of Implementing Data Lineage
  13. Data Lineage Benefits
  14. Data Lineage Best Practices
  15. FAQs About Data Lineage
  16. Wrap Up

First…

What is Data Lineage?

Definition: Data lineage is the digital genealogy of data. It answers the fundamental question: where did the data originate, and where has it traveled? Think of it as a roadmap for your data’s journey. It guides you through the twists and turns of your data’s life cycle.

Without data lineage, your information is a puzzle, missing crucial pieces. Therefore, understanding what is data lineage is not just a tech-savvy luxury but a necessity in today’s data-driven era. It provides transparency, accountability, and control over data, ensuring you’re not lost in the sea of information.

What is the Data Lineage Meaning?

Data Lineage Meaning refers to the process of tracking and visualizing the flow of data from its origin to its final destination. Understanding data lineage meaning helps ensure data accuracy, compliance, and transparency by showing how data moves, transforms, and is utilized across different systems. Organizations rely on data lineage to improve data governance and streamline decision-making.

Why Do Businesses Use Data Lineage?

Why do businesses willingly embrace and invest in data lineage? Let’s unravel the multifaceted reasons behind this strategic move.

  • Data quality assurance: Businesses deploy data lineage to ensure the integrity and accuracy of data. It allows you to track and maintain the quality of information from its source to destination.
  • Risk mitigation: Data lineage is a risk management tool that helps businesses identify potential pitfalls in their data processes. Understanding the data flow helps to proactively address vulnerabilities and minimize risks associated with data breaches or errors.
  • Enhanced decision-making: Data-driven decision-making relies on accurate and timely information. Data lineage provides a transparent view of data sources. This enables you to make decisions based on a comprehensive understanding of the data’s origins and implications.
  • Operational efficiency: Businesses leverage data lineage to streamline workflows and enhance efficiency. By visualizing the data flow, you can identify bottlenecks, eliminate redundancies, and optimize processes for smoother operations.

Data Lineage vs. Data Provenance vs. Data Governance

Aspect Data Lineage Data Provenance Data Governance
Definition The tracking and visualization of the flow and transformation of data throughout its lifecycle. The historical record of the origin, movement, and changes made to data elements over time. The overarching framework and set of practices ensure high data quality, compliance, and risk management.
Focus Emphasizes the journey and relationships of data, highlighting its path from source to destination. Concentrates on the historical context of data, capturing changes and activities related to its creation and modifications. Encompasses policies, procedures, and standards to ensure data is handled consistently, securely, and ethically.
Purpose Provides insights into data dependencies, helping organizations understand and manage their data assets. Offers a detailed historical context, aiding in troubleshooting, auditing, and ensuring data credibility. Establishes data usage, access, and management guidelines to maintain data integrity, security, and compliance.
Scope Specific to the flow and transformation of data, offering a visual representation of its journey. Focuses on the historical timeline of data, capturing its origin, changes, and movement across systems. Encompasses a broad set of policies, procedures, and controls governing data across the organization.
Use Cases Useful for impact analysis, understanding data dependencies, and ensuring data quality. Valuable for auditing, troubleshooting, and maintaining the credibility and trustworthiness of data. Critical for compliance with regulations, risk management, and establishing a structured approach to data handling.

How to Implement Data Lineage?

Data lineage is a choreographer orchestrating the dance of data through identification, documentation, mapping, and visualization. Let’s dive into the fascinating intricacies of this data choreography.

  1. Identifying data sources: The journey begins by pinpointing the sources of data. Whether originating from databases, applications, or external feeds, a comprehensive understanding of data origins is crucial.
  2. Capturing metadata: Metadata, the unsung data hero, is meticulously captured. This includes information about data structure, format, ownership, and usage, forming the foundation for a detailed data biography.
  3. Mapping data flows: Data doesn’t stay put; it moves. Data lineage maps out these movements, tracing the flow from source to destination. This involves identifying pathways, connectors, and touchpoints within the data journey.
  4. Documenting transformations: Data lineage documents these transformations as data undergoes metamorphosis through various processes. From cleaning and enrichment to aggregation, each metamorphic stage is recorded.
  5. Recording dependencies: Dependencies between data elements are crucial to comprehend. Data lineage records the relationships, ensuring a holistic view of how data pieces rely on one another.
  6. Establishing lineage relationships: Building upon recorded dependencies, lineage relationships are established. This step defines the parent-child relationships between data elements, creating a structured lineage framework.
  7. Visualizing lineage: The magic happens with data lineage visualization. Complex data relationships transform into clear, digestible visualizations, providing stakeholders with an intuitive map of the data’s journey.
  8. Monitoring and maintenance: Data lineage is not a one-time affair; it’s an ongoing process. Regular monitoring ensures the accuracy and relevance of lineage information. Any changes in data sources or transformations are promptly reflected.

What are the Different Data Lineage Techniques?

Understanding data lineage involves exploring an array of techniques. Each technique offers a unique approach to unraveling the intricate dance of information flow.

  • Manual Documentation

Manual documentation is a hands-on method where data professionals manually document data flow, Data transformations, and dependencies. While time-consuming, it allows for a detailed understanding of the data journey.

  • Automated Discovery

Automated discovery involves leveraging automated data lineage tools to scan and discover data sources, relationships, and transformations. This technique significantly reduces the manual effort, providing a quicker and more scalable approach.

  • Metadata Harvesting

Metadata harvesting involves extracting metadata from sources like databases and applications to build a comprehensive data elements profile. This technique ensures a rich foundation of information for the data lineage.

  • Data Lineage Tracking

Data lineage tracking implements tracking mechanisms that monitor data movements in real-time. This technique is crucial for dynamic environments where data is in constant flux.

  • Reverse Engineering

Reverse engineering unravels data lineage by working backward from the result to identify its sources and transformations. This method is particularly useful when the original lineage documentation is unavailable.

  • Data Lineage Modeling

Visual Storytelling utilizes graphical models and notations to represent the relationships and data flow. This technique provides a visual abstraction, making it easier for stakeholders to comprehend complex data lineage structures.

  • Semantic Lineage

Semantic lineage focuses on the meaning and semantics of data elements rather than just their physical attributes. This technique enhances understanding by considering the context and purpose of data.

  • Impact Analysis

Impact analysis examines how changes in data sources or transformations affect downstream processes. This technique is crucial for foreseeing potential consequences before implementing changes.

  • Data Lineage Visualization

Data lineage visualization transforms complex information into visual representations like flow charts or diagrams. Data visualization enhances comprehension and facilitates communication of data lineage insights.

What is Data Lineage Visualization
  • Integrated Lineage Platforms

This involves employing comprehensive platforms that integrate various lineage techniques into a unified solution. These platforms often combine automated discovery, metadata harvesting, and visualization for a holistic approach.

4 Types of Data Lineage

1. Business Lineage

Business lineage provides a high-level overview of how data flows between different business processes. It helps non-technical users understand data movement without getting into technical complexities, making it useful for decision-makers and compliance tracking.

2. Technical Lineage

Technical lineage focuses on the detailed flow of data, including transformations, dependencies, and processing logic. It is mainly used by data engineers and IT teams to troubleshoot issues, optimize data pipelines, and maintain data accuracy.

3. End-to-End Lineage

End-to-end lineage tracks the complete journey of data from its source to its final destination. It ensures transparency, data integrity, and compliance by providing a full view of how data is collected, processed, and consumed.

4. Operational Lineage

Operational lineage focuses on real-time data tracking and processing. It helps organizations monitor data changes, identify inefficiencies, and ensure seamless data operations by capturing live data flows and system interactions.

5 Real-Life Data Lineage Examples

1. Financial Reporting Compliance

Banks and financial institutions use data lineage to track financial transactions, ensuring compliance with regulations like SOX and GDPR. It helps auditors verify the accuracy of financial reports by tracing data back to its source.

2. Healthcare Data Management

Hospitals and healthcare providers rely on data lineage to track patient records, ensuring accurate and secure data sharing between systems. It helps maintain data integrity and compliance with HIPAA regulations.

3. E-commerce Customer Insights

Online retailers use data lineage to analyze customer behavior across multiple platforms. It ensures that sales, marketing, and customer support teams work with accurate, up-to-date data for personalized recommendations and business strategies.

4. Cloud Data Migration

Organizations migrating to the cloud use data lineage to track data movement, ensuring that no information is lost or altered. It helps in verifying data integrity and improving efficiency during cloud transitions.

5. Fraud Detection in Banking

Banks utilize data lineage to track transactions in real-time, identifying unusual patterns that may indicate fraudulent activity. By mapping the flow of financial data, they can quickly detect and prevent fraud.

Data Lineage Tools

1. Power BI Data Lineage

Power BI provides built-in data lineage features that help users track data sources, transformations, and dependencies within reports and dashboards. It enhances data governance and ensures transparency in analytics.

2. Informatica Enterprise Data Catalog

Informatica offers automated data lineage tracking, helping organizations visualize data flow across various sources. It improves compliance, governance, and data discovery.

3. Alation Data Catalog

Alation provides a user-friendly data lineage solution that integrates with multiple data sources. It allows teams to track data origins, transformations, and dependencies efficiently.

4. Collibra Data Lineage

Collibra automates data lineage mapping, ensuring transparency and compliance. It visually represents how data moves and transforms across systems.

5. Talend Data Fabric

Talend provides end-to-end data lineage, allowing users to monitor data transformations and ensure quality control. It is widely used for ETL processes and data governance.

Data Lineage Visualization in Power BI

Every workspace automatically includes a lineage view, accessible with at least a Contributor role in the workspace.

To enter the lineage view, navigate to the workspace list view. Click the arrow next to View and choose Lineage.

What is Data Lineage in Power BI

In this view, you’ll see all workspace artifacts and understand the data flow from one artifact to another.

  • Data Sources

Identify the sources for semantic models and dataflows. Data source cards provide additional information, such as the database name for the Azure SQL server.

for SQL After Learning What is Data Lineage
  • Gateways

If a data source connects through an on-premises gateway, gateway details are included on the data source card. You can access additional information, such as the data gateway in the Power BI name if you have permission.

for Web After Learning What is Data Lineage
  • Semantic Models and Dataflows

You can see the last refresh time and certification or promotion status on semantic models and dataflows.

for Dataflows After Learning What is Data Lineage

If a report within the workspace is built on a semantic model or dataflow from another workspace, the source workspace name appears on the respective card. Click the source workspace name to navigate to that workspace.

Access the options menu by selecting More options (…) for any artifact. It features all the same actions available in the list view.

To view additional metadata for any artifact, click the artifact card. A side pane displays extra information about the selected artifact. In the image below, the side pane showcases metadata for a selected semantic model.

Revenues Forecast After Learning What is Data Lineage

Select the double arrows under the artifact to display lineage for a specific artifact.

Lineage Display After Learning What is Data Lineage

How to Conduct Data Lineage Analysis in Power BI?

We have broken down the process into four stages to make it easier for you to follow along.

Stage 1: Logging in to Power BI

  • Open the Power BI web service login page.
  • Enter your email address and click the submit button.
Enter email to login to Power BI
  • You will be redirected to your Microsoft account login page.
  • Enter your Microsoft password and click the “Sign in” button.
Enter Password to login to Power BI
  • You have the option to stay signed in or not.
Click on stay signed in
  • Once done, the Power BI home screen will open.

Stage 2: Create a Data Set and Select it to Use in Your Sankey Chart.

  • Navigate to the left-side menu and click the “Create” button.
  • Select “Paste or Manually enter data.”
select Paste or manually enter data in Power BI ce487
  • We’ll use the human resource sample data below for illustration.
Application Channels Initial Screening Conduct Interviews Employee Onboarding Total Candidates
Social Media Short Listed Final Interview Hired 32
Social Media Short Listed Final Interview Not Hired 400
Social Media Short Listed Knocked Out 800
Social Media Knocked Out 1100
Company Career Page Short Listed Final Interview Hired 20
Company Career Page Short Listed Final Interview Not Hired 250
Company Career Page Short Listed Knocked Out 500
Company Career Page Knocked Out 900
Events Short Listed Final Interview Hired 5
Events Short Listed Final Interview Not Hired 100
Events Short Listed Knocked Out 200
Events Knocked Out 350
Paper Media Short Listed Final Interview Hired 3
Paper Media Short Listed Final Interview Not Hired 80
Paper Media Short Listed Knocked Out 135
Paper Media Knocked Out 700
Employee Referrals Short Listed Final Interview Hired 10
Employee Referrals Short Listed Final Interview Not Hired 70
Employee Referrals Short Listed Knocked Out 80
Employee Referrals Knocked Out 110
Direct Short Listed Final Interview Hired 25
Direct Short Listed Final Interview Not Hired 150
Direct Short Listed Knocked Out 425
Direct Knocked Out 600
  • Paste your data table into the “Power Query” window.
  • Select the option “Create a dataset only.”
Create a Dataset Only After Learning What is Data Lineage
  • Go to the left-side menu and click the “Data Hub” option.
  • This will display a list of available data sets in Power BI (If you haven’t created a data set yet, refer to the section titled “Error! Reference source not found”).
Data Hub After Learning What is Data Lineage
  • Click the “Create report” dropdown.
Create Report After Learning What is Data Lineage

Stage 3: Adding the Power BI Sankey Diagram Extension by ChartExpo

  • To create a Sankey Diagram, we need to use either an add-in or Power BI visual from AppSource.
  • Navigate to the right side of the Power BI dashboard to access the Power BI Visualizations panel.
  • Click the ellipsis symbol (…) as highlighted in the diagram below. This is to import the Power BI Sankey Diagram extension by ChartExpo.
  • Select the option “Get more visuals.” Then, enter “Sankey Diagram for Power BI by ChartExpo” in the search box.
Get More Visuals After Learning What is Data Lineage
  • The “Sankey Diagram for Power BI by ChartExpo” option will appear as shown below.
Sankey Diagram for Power BI After Learning What is Data Lineage
  • Click the “Add” button.
Click Add Button After Learning What is Data Lineage
  • Power BI will add the “Sankey Diagram for Power BI by ChartExpo” to the visualization panel.
Click ChartExpo Icon After Learning What is Data Lineage

Stage 4: Drawing a Sankey Chart with ChartExpo’s Power BI extension

  • Click the “Sankey Diagram for Power BI by ChartExpo” icon in the visualization panel.
  • This action will result in a new window opening in the report section of your dashboard.
New Window After Learning What is Data Lineage
  • To create a Sankey Chart, you must choose the fields you want to incorporate.
  • It is essential to note that you must select the ChartExpo visual beforehand. Select the field in the following sequence:
Select Fields of data After Learning What is Data Lineage
  • You will receive a request to enter your ChartExpo license key or email address.
  • Add the key under the Visual section. After that, you can see the Sankey Chart for your data.
ChartExpo License Key After Learning What is Data Lineage
  • Click the General tab to add the header text.
Add Title After Learning What is Data Lineage
  • Your final look at the HR Dashboard in Power BI using ChartExpo will appear below.
Final What is Data Lineage

Insights

  • Social Media: High candidate numbers indicate strong outreach but intense competition, especially in the early stages. Most eliminations occur during initial screening.
  • Across all channels: A low ratio of hired to total candidates suggests a competitive selection process or high hiring standards.
  • Employee Referrals and Direct Channels: Fewer initial eliminations suggest potentially higher-quality referrals or direct applicants.
  • Overall: These insights guide recruitment strategies for resource optimization and improved hiring efficiency.

Challenges of Implementing Data Lineage

1. Data Complexity

Organizations deal with vast amounts of data from multiple sources, making it difficult to track data movement, transformations, and dependencies accurately.

2. Integration Issues

Data lineage tools need to integrate with various databases, cloud platforms, and analytics tools, which can be challenging due to compatibility and technical constraints.

3. Incomplete or Inaccurate Metadata

If metadata is missing or inconsistent, it becomes difficult to establish a clear lineage, leading to gaps in data tracking and governance.

4. High Implementation Costs

Setting up a robust data lineage system requires significant investment in tools, resources, and skilled professionals, which can be a barrier for many organizations.

5. Maintaining Up-to-Date Lineage

Data systems constantly evolve, and keeping data lineage documentation updated with changes in data sources, transformations, and workflows is an ongoing challenge.

Data Lineage Benefits

1. Improved Data Governance

Data lineage ensures transparency by tracking data movement, transformations, and usage, helping organizations maintain compliance with regulatory requirements.

2. Enhanced Data Quality

By identifying errors and inconsistencies in data workflows, organizations can improve data accuracy and reliability for better decision-making.

3. Faster Troubleshooting

Data lineage helps teams quickly trace data issues back to their sources, reducing the time required for debugging and error resolution.

4. Better Decision-Making

With clear visibility into data origins and transformations, businesses can trust their analytics and make informed strategic decisions.

5. Streamlined Integration

Organizations using tools like Power BI can leverage data lineage to seamlessly integrate various data sources while ensuring data consistency.

Data Lineage Best Practices

1. Define Clear Objectives

Establish the purpose of data lineage, whether for compliance, data governance, or improving data quality, to ensure alignment with business goals.

2. Automate Data Lineage Tracking

Use automated tools like Power BI to map data flow efficiently, reducing manual errors and ensuring real-time updates.

3. Maintain Comprehensive Metadata

Keep metadata well-documented and up-to-date to track data transformations, sources, and dependencies accurately.

4. Ensure Cross-Team Collaboration

Encourage collaboration between IT, data analysts, and business teams to create a unified approach to managing data lineage.

5. Regularly Monitor and Update

Continuously review and update data lineage documentation to reflect changes in data sources, transformations, and workflows.

FAQs About Data Lineage

What is the difference between data lineage and data flow?

Data lineage traces the journey of data, highlighting its source, transformations, and destinations. In contrast, data flow represents the movement of data from one point to another. It illustrates the sequential steps in its transfer without necessarily capturing the full historical context.

What is a data lineage diagram?

A data lineage diagram visually depicts the path and transformations of data. It offers a clear illustration of its journey from source to destination. This provides a comprehensive and intuitive overview of data flow within a system or organization.

What is a lineage in ETL?

In ETL (Extract, Transform, Load), lineage refers to tracking and documenting the flow of data throughout the entire process. It involves understanding the origin, transformations, and destinations of data, ensuring transparency and accountability in the data integration pipeline.

Wrap Up

Data lineage is the storyteller of the digital world, revealing the narrative of data from its origin to its application. It is the GPS guiding organizations through the labyrinth of information flow. Understanding what is data lineage unveils its role in managing the complex dance of data within systems.

Data lineage is not a mere concept; it’s a necessity in the dynamic landscape of technology. It answers the fundamental questions of where data comes from and where it goes. This transparency is the cornerstone for effective data governance, ensuring compliance, risk mitigation, and data quality assurance.

Businesses leverage data lineage for a multitude of reasons, from risk mitigation to enhanced decision-making. It is the guardian of data integrity, tracing every bit and byte’s journey and safeguarding against pitfalls. Impact analysis becomes intuitive, resource optimization becomes more strategic, and operational efficiency becomes a tangible outcome.

The techniques employed, whether manual documentation or automated discovery, contribute to the rich tapestry of data lineage implementation. Each technique brings a unique perspective, unraveling the complexity and revealing the interconnected web of data relationships.

As we conclude this exploration into what is data lineage, it’s evident that it’s more than a technical concept. It’s a strategic asset. It empowers you to navigate the data landscape with precision, unlocking the potential for informed decision-making and streamlined operations.

Follow the breadcrumbs, not the buzzwords. Dive into data lineage and unlock the secrets your data is dying to tell.

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