What is data lineage?

A comprehensive guide to data lineage

As data moves through an organization, it's possible to lose track of its origin, intended purpose, or even its final destination. You may have disparate data sources, several different data owners, or trouble finding where to point the finger when something goes wrong.

However, understanding precisely where data comes from, how it transforms, and its journey is essential to maximizing its value. That's where data lineage comes in. It offers a clearer picture of your data landscape and its reliability.

This article will dive into the data lineage best practices and much more, let’s dive in.

What is data lineage?

Data lineage tracks data from its source until its eventual ending or purpose. It uses tools to understand, document, and visualize the entire lifecycle of a data point. Typically, you track these events and metrics via your data catalog to keep up with daily use and help resolve errors.

What are the two types of data lineage?

There are two ways of doing data lineage: the table-level and the column-level (field-level)

Table-Level

Table-level data lineage is the most basic and frequently used form. It shows the relationships between different tables in a data environment. While it provides insight into how a particular table is constructed, it does not reveal the source of the data within that table.

Column-Level (Field-Level)

Column-level data lineage, in contrast, serves as a detailed map tracking a dataset's journey from the point of ingestion through to its final visualization. This level of lineage is crucial in Data Observability, as it enables data engineers to swiftly identify the root cause of data quality issues and perform impact analysis to determine which downstream processes or systems may be affected.

Why is data lineage important?

As with any data management process, data lineage can enhance data assets and better outcomes for your organization. If you have a better idea of where your data is coming from and how it's handled, you can proactively strategize for the future, finding better ways to manage the data, save time, and avoid costly errors.

Here are a few more reasons why data lineage is important:

• Trustworthy data: Data lineage allows you to perform "root cause analysis" (tracing the root of data quality issues back to their source), maintain transparency, and hold responsible parties accountable throughout your data pipelines. It plays a critical role in upholding data quality importance.
• Eliminate data silos. It's easier to track data and not have it handled separately, so you don't have a data steward working on one error, a data engineer on another, or working on the same error over and over again when you need to use a particular data set.
• Compliance: Data lineage is mandatory for regulations like GDPR and CCPA to prove data usage aligns with standards and for swift action in case of breaches, customers demanding access to their data, and the right to erasure.
• Simplified data operations: Lineage paints a picture of dependencies, interconnected systems, and downstream impacts, enabling smooth data changes and streamlining operations. It simplifies data migration with a better understanding of data flows, mapping transformations, and data quality assurance throughout.

What are the components of data lineage?

We've established what data lineage is and why it's important. Let's review the core components to see how they will fit your organization.

• Data origins/sources: Identify where your data comes from and the owner/person responsible for that data in databases, applications, third-party data feeds, etc.
• Data flows and transformations: Tracking every modification made to data (data transformation) and every destination it passed along its journey (calculations, aggregations, merging datasets, data quality improvements, who accessed the data, etc).
• Dependencies: Understanding relationships between data elements and how they impact each other.
• Metadata management. Essential data that provides context to data points, organizing them into technical categories and different business processes.

What are the benefits of data lineage?

Beyond its essential nature, here are some of the main data lineage benefits organizations can experience from automated data lineage tools.

• Proactive data governance: Data governance encompasses the policies, processes, roles, and technologies you use to action your data strategy. Lineage provides the framework for managing and governing data effectively.
• Streamlined troubleshooting: Pinpoint the cause of data issues and inaccuracies so you can address them quickly and effectively.
• Boosted collaboration: Data lineage brings data scientists, stewards, engineers, and business users together by providing context about data and its movement through the business. This leads to faster workflows and better decision-making.
• Effective change management and analysis: Study the impact and ripples that data changes cause throughout your systems.

How can companies use data lineage in practical applications?

At Ataccama, we've been working with data lineage for decades, aiding businesses with better tracking and understanding of their data lifecycle. We'd like to provide you with some real-world use cases from our experiences in this field:

1. Augmented data lineage

Some say this is the next step or evolution in the lineage landscape. It enriches lineage tools with valuable metadata from the catalog, such as real-time data quality, business terms, and anomalies detected in data loads.

2. Data observability

Data observability is an interconnected network of data management tools that gives you a near-real-time understanding of the state of your data and its quality. Data lineage is crucial to this process, especially when investigating data quality issues.

What are 5 common data lineage challenges?

1. Complex data environments

Modern data environments can become very complex because of the various data sources, transformations, and endpoints that are involved. All of this can complicate the data lineage mapping and visualization process. As a result, there can be inaccuracies and gaps in the information.

2. Varying or unclear standards

Another data lineage challenge is when an organization has varying standards for how data lineage is represented across different systems. Lack of standardization can lead to inconsistencies and difficulties in integrating lineage information from multiple sources.

3. Large amounts of data to track

If your organization needs to process a large volume of data, some data lineage tools may not have the capabilities to rapidly process and maintain updated lineage records. High data throughput can complicate the task of tracking and documenting the flow of data.

4. Data that changes frequently

Data that is frequently modified can make the data lineage tracking process more difficult. Frequent changes in data structures, processes, and systems will make a data lineage tool work overtime. This requires diligent maintenance and ongoing updates to successfully track the flow of data.

5. Inability to integrate with other systems

If a data lineage solution is integrable with other systems an organization uses, this poses a major data lineage challenge. Compatibility issues and integration complexities can make tracking data more difficult or hinder its ability to do so altogether.

What features are included in a good data lineage tool?

• Robust visualization: Data lineage diagrams are a must-have feature because they offer clear visibility into the data flow, allowing you to see everything every step of the way.

• Data cataloging: The best tool should enable you to search and find the information you need through a comprehensive data catalog system.

• Automation: A worthwhile data lineage solution will automate certain activities, removing the manual necessity of updating documentation, metadata, fields, and much more.

• User-friendly interface: If the interface is not intuitive or simple to use, move on! Fortunately, there are many tools on the market that are user-friendly.

• Integration capabilities: Find a solution that integrates into other systems and applications used within your organization versus functioning entirely separate. This will make managing your data much easier.

Elevate your data lineage capabilities with Ataccama!

Data lineage is more than just a nice feature. It's a necessity for organizations that want reliable insights and streamlined data-driven strategies. By understanding what it is, why it matters, and how it works, you're well on your way to maximizing the power of your data.

Want our help with data lineage? Access the Ataccama data catalog software today.