What is Metadata Lineage & Why You Should Care About It?

Last updated on: July 18th, 2023
Metadata Lineage

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Metadata lineage provides a record of the systems, processes, and transformations that data has undergone, from its original source through to its current state. Essentially, it is a kind of “data about data” tracking and understanding where a piece of data came from, where it has been, and how it has been altered over time.

In this article, we will understand what is metadata lineage and how it helps organizations gain insights into data flows, understand how data is manipulated and transformed, identify potential data quality issues, assess the impact of changes, and maintain data integrity.

Let’s dive in!

Table of contents

  1. What is metadata lineage?
  2. Metadata lineage - Explained with practical examples
  3. Visualizing metadata lineage for data flow and transformation
  4. How to ensure the availability of visual and actionable lineage in your data systems?
  5. How to evaluate a metadata lineage tool?
  6. Atlan’s approach to comprehensive and interactive metadata lineage
  7. Rounding it all up
  8. Metadata lineage: Related reads

What is metadata lineage and why is it crucial for data management?

Metadata lineage is the documentation and tracking of the origin, transformation, and movement of metadata throughout its lifecycle within a data ecosystem.

Metadata, which provides information about data attributes, characteristics, and relationships, is critical for data management, and understanding data lineage is essential for ensuring data quality, governance, and compliance.

Metadata lineage is crucial in data management for several reasons:

  1. Data quality
  2. Data governance
  3. Impact analysis
  4. Troubleshooting and data integrity
  5. Transparency and trust

Let’s look into each of the above reasons in brief:

1. Data quality

Metadata lineage helps establish the quality of data by tracking its source and transformations. For example, if an inconsistency is found in the data, lineage can help identify where the error might have originated from.

2. Data governance

In many industries, particularly those with heavy regulations such as finance or healthcare, knowing where data came from, who has touched it, and how it’s been used is not only good practice but a legal requirement. Metadata lineage plays a key role in these data governance efforts.

3. Impact analysis

Metadata lineage helps in understanding the potential impacts of changes in data structure or architecture. For example, if a field in a database is being considered for modification, data lineage can help identify all the downstream systems, processes, and reports that could potentially be affected.

4. Troubleshooting and data integrity

If a problem arises with the data, lineage can provide a roadmap for tracing the issue back to its source, making it easier to identify and fix the problem.

5. Transparency and trust

By giving a clear view of data’s origins and transformations, metadata lineage can boost confidence in data among users, making them more likely to trust and use the data.

Why is understanding metadata lineage crucial? Explained with practical examples

Now that we’ve seen why metadata lineage is important, let us understand it better with the help of examples.

1. Data quality

Let’s assume you’re analyzing data related to the manufacturing process of a semiconductor chip. You notice that certain entries for a temperature measurement field appear to be abnormally high.

Through metadata lineage, you can trace this data back to its source. You might find out that the reading is coming from a faulty sensor in a particular machine, which has been recording erroneous temperature data. This ability to trace back data anomalies can help improve the overall data quality.

2. Data governance

For instance, in a heavily regulated industry such as healthcare, the source of patient data and who has accessed it is critical information. Metadata lineage can track this data from the time it was entered into the original system (say a hospital’s electronic health record system), every system it has been transferred to, who accessed it, and what transformations it underwent.

This ability is critical to maintaining compliance with regulations such as HIPAA, which requires a detailed record of all interactions with patient data.

3. Impact analysis

Suppose you are considering a change to your data architecture, such as removing a column from a database table because it appears redundant. With metadata lineage, you can trace the usage of this column across various reports, analytics processes, and more.

You might discover that while the column seems redundant, it’s crucial to a particular report relied upon by the marketing team. This understanding helps you assess the impact of proposed changes and plan them better.

4. Troubleshooting and data integrity

Assume there’s an error in the final report generated for monthly production stats. Metadata lineage can help trace the data used in this report back through each transformation, database, and system it passed through.

You may find that a specific transformation step in the ETL process was incorrectly implemented, causing the error. The ability to backtrack through the data flow is invaluable for troubleshooting and ensuring data integrity.

5. Transparency and trust

For example, a business analyst in your company uses certain data sets for decision-making. Through the metadata lineage, they can see where this data originated, how it was transformed, and who else has accessed it. This transparency creates trust in the data, and the analyst can confidently use the data sets for their decision-making process.

In all of these examples, a strong metadata management tool is key. Such a tool should be able to automatically capture lineage information, provide clear and understandable visualizations of that lineage, and allow for annotations or additional context to be added as needed.

Mapping the journey: Visualizing metadata lineage for data flow and transformation

Metadata lineage is typically visualized as a flowchart or graph, mapping out the journey of data from its source to its destination. Each node on the graph represents a system, process, or dataset that the data passes through or is stored in. Edges between nodes represent the movement or transformation of data.

Here’s an example of how metadata lineage might look in practice:

Suppose we have sales data that originates from an online sales platform. The data might go through the following steps:

1. Sales platform (source)

This is the original source of the data, where each transaction is recorded as it happens. Each transaction record includes details like product ID, customer ID, sales price, and timestamp.

2. ETL process

The raw data from the sales platform is then passed through an ETL (Extract, Transform, Load) process. This might involve cleaning the data (e.g., handling missing or inconsistent data), transforming the data (e.g., aggregating sales by product or by day), and loading the data into a data warehouse for further analysis. This is a transformation node.

3. Data warehouse (intermediate storage)

The transformed data is stored in a data warehouse, where it can be accessed and analyzed by various users and tools across the organization.

4. Business intelligence tool (destination)

A BI tool pulls data from the warehouse and generates reports and visualizations for business users. For instance, it might generate a daily report on sales performance.

5. Daily sales report (end product)

This is the end product that business users interact with. Metadata lineage would track this data from the Daily Sales Report back through the BI tool, the Data Warehouse, the ETL process, and finally to the Sales Platform source.

If a user finds an issue in the Daily Sales Report, they can trace it back through the lineage to identify where the issue originated.

Metadata lineage tools would present this flow of data visually, making it easy for users to understand the origin and transformation of data.

Some tools also allow users to click on nodes to get more detailed information, like who owns a particular process, when the last data load occurred, what transformations were applied, etc.

This enhances the understanding and transparency of the data lifecycle, fostering trust and facilitating troubleshooting and impact analysis.

How to ensure the availability of visual and actionable lineage in your data systems?

Whilst we are talking about metadata lineage, let us also look at some strategies you can implement to ensure the availability of visual and actionable lineage in your data systems:

  1. Adopt a metadata management tool
  2. Automate metadata collection
  3. Standardize metadata
  4. Integrate your systems
  5. Simplify visualizations
  6. Provide training
  7. Leverage tool features
  8. Document and share tribal knowledge
  9. Offer support

Let us look into each of the above strategies in detail:

1. Adopt a metadata management tool

Use a metadata management tool that provides strong lineage visualization capabilities. These tools can automatically capture metadata and lineage as data moves through your systems, and they provide visualizations to help users understand the data’s history.

2. Automate metadata collection

Whenever possible, automate the collection of metadata at every stage of the data lifecycle. This includes extraction, transformation, loading, analysis, and reporting. This can often be achieved through the metadata management tool, or it may require coordination between various systems.

3. Standardize metadata

Establish clear standards for metadata, including what information should be recorded, how it should be formatted, and who is responsible for ensuring its accuracy. This will ensure that your lineage data is consistent and accurate.

4. Integrate your systems

Your metadata management tool should be integrated with all data sources, data warehouses, data lakes, ETL tools, BI tools, and other systems involved in the data lifecycle. This ensures that lineage is captured as data moves through each step.

To make data lineage understandable and actionable for all kinds of users, you can:

5. Simplify visualizations

While technical users may appreciate detailed graphs, non-technical users may prefer simpler visualizations. Choose a tool that allows users to toggle between different levels of detail.

6. Provide training

Conduct training sessions or workshops to help users understand how to read and interpret lineage diagrams. Include real-world examples and use cases relevant to your business.

7. Leverage tool features

Many metadata management tools have features designed to help non-technical users, such as natural language search, easy-to-understand icons, tooltips with explanations, etc. Make sure these features are enabled and users are aware of them.

8. Document and share tribal knowledge

Encourage users to add their own annotations to the lineage diagrams to capture tribal knowledge. This can be invaluable for other users who might be using the same data.

9. Offer support

Have a support system in place, such as a data steward or a data team, who can help users with questions or issues related to data lineage. This will ensure that users of all levels of technical expertise are able to understand and act on data lineage.

These strategies can help ensure that your lineage data is not only available but also useful and accessible for all kinds of data users in your organization.

How to evaluate a metadata lineage tool?

Below we’ve mentioned a comprehensive checklist to assess the capabilities of a metadata lineage tool. Here are 14 factors that you should be keeping in mind while evaluating a metadata lineage tool:

  1. Automatic SQL parsing: The tool should be able to automatically parse SQL queries, especially from systems that lack a built-in “query history” feature.
  2. Support for various types of SQL statements: The tool needs to support a wide range of SQL statements, including CREATE TABLE, CREATE VIEW, MERGE, INSERT INTO, and UPDATE.
  3. Programmatic lineage creation and retrieval: The tool should allow you to create and retrieve lineage programmatically through an API, enabling you to process lineage from unsupported sources.
  4. Native lineage capability: The tool should natively support lineage and not rely on an external partnership, which could lead to delays in support and fixes.
  5. Future-proof against changes in the modern data stack: The tool should be capable of adapting to changes in data transformation tools and processes.
  6. Scalability: The tool should be cloud-native, meaning it is designed with modern patterns and microservices for limitless scalability.
  7. Column-level lineage: The tool should offer lineage at the column level for a deeper understanding of your data’s transformations and dependencies.
  8. Connection of SQL sources with downstream BI assets: The tool should automatically connect upstream SQL sources with downstream BI tools like Looker, Tableau, Power BI, etc.
  9. Field-level lineage for BI dashboards: The tool should support field-level lineage for BI tools, which is essential for root cause and impact analysis.
  10. Support for Salesforce lineage: The tool should support lineage from Salesforce, providing visibility into the ETL pipeline and how changes in Salesforce data affect downstream systems.
  11. Integration with modern data integration tools: The tool should integrate with modern data integration tools like Fivetran, allowing you to create end-to-end lineage.
  12. Integration with Databricks and support for Spark jobs: If you use Databricks, the tool should be able to generate lineage for Spark jobs, providing visibility into your transformations.
  13. Incorporation of other types of metadata: The tool should incorporate various types of metadata including operational, quality, business, owner, and social metadata to provide additional context to the lineage.
  14. Driving action programmatically: The tool should not only investigate issues but also drive action programmatically, like automating alerts when there are data quality issues.

Remember, the best tool for you depends on your specific needs, the complexity of your data environment, and the skill level of your users. Make sure to conduct a thorough assessment before making a decision.

Unlocking data insights: Atlan’s approach to comprehensive and interactive metadata lineage

Atlan’s approach to metadata lineage, specifically column-level lineage, is centered on providing a comprehensive, intuitive, and interactive experience for users. Here are the 6 key components of their approach:

  1. Column-level confidence
  2. Automated SQL parsing
  3. Out-of-the-box integrations
  4. Open APIs
  5. Smooth navigation
  6. Clean layout

Now, let us look into each of the above key components in brief:

1. Column-level confidence

Instead of limiting the lineage to the table level, Atlan provides column-level lineage. This means that users can trace data transformations and dependencies down to the level of individual columns in their tables. This approach helps users gain a deeper understanding of their data’s origins and transformations.

2. Automated SQL parsing

To enable the detailed lineage at the column level, Atlan uses automated SQL parsing. This helps uncover column-level relationships and dependencies automatically, making the lineage process more efficient and accurate.

3. Out-of-the-box integrations

Atlan connects with various data warehouses and Business Intelligence (BI) tools to establish cross-system lineage. This means that users can trace data lineage across different systems and tools, providing a holistic view of their data landscape.

4. Open APIs

Users can bring in any data product they want by building on top of Atlan’s open APIs. This extends the flexibility and adaptability of the lineage system, allowing it to accommodate a wide variety of data products and tools.

5. Smooth navigation

The user experience is designed to be smooth, intuitive, and interactive, making it easy for users to explore their data lineage. The interface supports seamless traversing through the lineage paths, enhancing the user’s ability to understand their data’s journey.

6. Clean layout

Atlan’s lineage is praised for being well laid out, clean, and easy to understand. This design philosophy helps users avoid confusion and complexity often associated with understanding data lineage.

In summary, Atlan’s approach is to provide a detailed, clean, and easy-to-navigate view of data lineage, with a focus on column-level details and cross-system lineage tracking, powered by automated SQL parsing and open APIs for flexibility.

Rounding it all up

If you’re looking to deploy a metadata management tool, ensure that it has strong capabilities for tracking and visualizing data lineage. This should include the ability to automatically track lineage as data is ingested and transformed, as well as intuitive tools for exploring and understanding that lineage.

Also, look for capabilities that allow for the annotation of metadata to include tribal knowledge or other contextual information that can improve the utility and understanding of data across your organization. This will help your team get the most value from your data by ensuring they can trust its accuracy and understand its context.

The overall goal of this guide is to highlight the importance of understanding and managing data lineage, especially when it comes to enhancing data governance, ensuring regulatory compliance, improving data quality, and promoting better data understanding across an organization.

Robust metadata management is the key for data-driven teams to discover, understand, trust, and collaborate on data assets across your data universe. If you are looking to implement a metadata management tool for your organization, do take Atlan for a spin.

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