What is data lineage, and how does it work?

Data lineage is a visualization of how your data moves through systems and applications within your business. It is a recorded history of the data journey: its origins, its changes, and its dependencies. 

Understanding where data is and what it’s used for will improve your business outcomes. You’ll be able to identify efficiencies, reduce risks, and plan for migrations and application updates with complete knowledge of how data flows through your ecosystems. 

Now, let’s explore exactly what data lineage is and why it’s important for your business.

Why is data lineage important? 

You can’t trust what you can’t see. How can you expect to make smart, evidence-based decisions when grappling with data sets that are incomplete, inaccurate, and inconsistent? 

Data lineage eliminates these gaps and issues by mapping the journey and relationships of your data within complex systems. Having an up-to-date and accurate data framework is transformative for:

• Data quality and integrity: Data lineage is used for root cause analysis (RCA): identifying issues or errors within a data pipeline. This resolves problems and ensures data is accurate and reliable.
• Data governance and compliance: Regulatory frameworks demand transparency about collecting, processing, and storing data. A root cause analysis (RCA) also provides a clear audit trail, which is vital for reporting and compliance.
• Data security and risk management: Data lineage minimizes security risks by visualizing the flow of data and pinpointing where sensitive data resides, ensuring there aren’t any weak points malicious outside actors can exploit.
• Decision-making: Better data supports better decision-making. Having accurate and trustworthy data enables leaders (CDOs, CTOs, CIOs, CEOs) to plan and strategize based on clear and verified data and relevant insights. 
• Data operations: Even minor changes, like adding a column to one table in a single application, can have implications for a data ecosystem. Data lineage is critical for impact analysis: identifying how these small changes to data affect things “downstream.” It’s also an excellent tool for data migrations and data modeling.

How does data lineage work? 

Data lineage tracks data from origin to rest and everything in between. It’s an exhaustive process that brings together data from multiple systems and processes and makes sense of them, complete with visual representations, such as maps and diagrams. Let’s look at the key components of data lineage and what’s involved. 

Data sources

Data sources are the origins of data. This is where everything flows from. Sources include data lakes, web services, business tools, sensors, and APIs. Understanding your “upstream” locations is key to data consistency and quality.

Data transformations

Transformations are the processes applied during the data journey and how it evolves from start to endpoint. These processes include cleansing, merging, filtering, and reformatting unstructured data. 

Data destinations

Destinations are the places where data resides, either for use or storage. These are also known as the “downstream.” They include data warehouses and dashboards. Knowing where data needs to go will ensure it’s delivered to the right location.

Metadata

Metadata describes the data. It provides essential information about source, type, format, structure etc. Effective metadata management will give you the information and context to track and understand data during its lifecycle. 

The data lineage process

In a world where data is the new currency, trusting its accuracy (and demonstrating it to others) can be transformative. With clear data lineage, you can have confidence in high-stakes decisions, avoid costly compliance violations, and engage with key stakeholders as you drive a path forward toward sustainable growth and success. Now, let’s explore how to get started.

Step 1: Identify data sources

First, locate where data originates. You need to identify all sources: internal and external, and map them. Only with a complete understanding of where your data comes from can you master data lineage.

Step 2: Track data transformations

Next, use a data build tool (or spreadsheet) to log and track how data changes as it moves through different systems. For example, if a summary report has aggregated data from multiple sources, you need to capture how specific algorithms, rules, or steps combined and changed this data. Full visibility of transformations improves data accuracy and relevance.

Step 3: Map data flows

Data lineage is all about visualization. You must create a data lineage diagram to map out data points, covering the entire journey. This process allows you to fully grasp the relationships and dependencies between different data sets and systems.

Step 4: Visualize data lineage

Stakeholders and non-technical users also need to understand how data flows. Interactive lineage diagrams and color-coded charts are great for visualizing data workflows and highlighting risks, bottlenecks and other issues. They can also be used to demonstrate compliance to auditors and regulators. 

What are the benefits of data lineage? 

Data lineage provides many advantages to businesses that are fed up with wading through a sea of disparate data sets and want clarity and consistency in data environments. The benefits include::

1. Enhanced data quality

Data lineage untangles a web of inconsistent data by providing a holistic view of its lifecycle. Everything can be traced and extracted, from its origin to how it changed and where it resided during its journey. 

Fully visualizing data in this way and having the tools to correct inaccuracies, inconsistencies, and gaps enhances data quality. Better quality data can be transformative for a business, improving decision-making and reducing costly errors.

Additionally, businesses that want to tap into the power of GenAI tools need reliable, high-quality data to feed into LLMs, to ensure trustworthy and secure AI outcomes. 

2. Improved data governance

Data lineage actively supports effective data governance: the process of ensuring the accuracy, security, and availability of data. Data lineage underpins data governance efforts by providing clarity on where data resides, where it has been, and how it needs to be managed. This clarity makes it easier for you to implement data governance frameworks and AI governance frameworks.

3. Accelerated data analysis

Analyzing data needs to be accurate, complete, consistent, and available to everyone. Data lineage enhances quality and provides analysts with a clear map of flows and transformations, so teams can focus their efforts on generating insights and delivering value rather than wrestling with data blind spots and issues. 

4. Reduced risk and compliance costs

Data lineage provides a verifiable audit trail, making it easier for you to demonstrate compliance. This is invaluable in industries like finance and healthcare, where businesses need to ensure data is secure and accurate to meet strict regulatory requirements for GLBA, GDPR, APA, HIPAA, and CCPPA.

5. Optimized data operations

Data pipelines are complex. It’s common for bottlenecks, inefficiencies, and redundancies to wreak havoc on data processing practices. Data lineage rescues data from this chaos, optimizes processes, and improves system performance.

Netflix’s approach to data lineage

Building data lineage has helped organizations overhaul and improve their data infrastructures so that teams can rely on them for critical business decisions.

Netflix documented its efforts to create a data lineage system that establishes data integrity and trust for users while enabling seamless integration with a growing number of tools and platforms.  

The streaming giant even created a mission statement for its data lineage transformation: “Provide a complete and accurate data lineage system enabling decision-makers to win moments of truth.”

The benefits of data lineage can be realized across industries. 

• Financial services: Banks track and map data assets to meet regulatory compliance requirements and detect fraud. 
• Healthcare: Hospitals, clinics, and other providers are seamlessly integrating and tracking patient data across systems to improve the accuracy of diagnoses and comply with privacy laws. Learn more about using RecordPoint for healthcare data. 
• Retail: Retailers are extracting insights from data lineage to optimize supply chains and improve customer service
• Telecommunications: Telecom companies use data lineage to manage vast datasets, improve the quality of networks, and spot and act on fraudulent patterns of behavior.

What are the challenges of data lineage? 

Modernizing data isn’t always easy. The sheer scale of datasets with their ever-growing diverse formats can be difficult to manage. Especially when rolling out new lineage processes. Challenges that often arise can include the following:

1. Data quality issues

Poor quality data, including missing, or inaccurate data, makes the process of identifying and mapping it difficult. For example, data corrupted during transformation processes will be reflected in lineage maps, leading to faulty analysis and bad decision-making. 

To address this, you need to undertake exhaustive cleansing and validation processes before data is traced and mapped and implement data governance initiatives to maintain accuracy and integrity. 

2. Complex data pipelines

Modern data environments are complex. It’s common for interconnected systems applications and processes to create a tangled web of data pipelines. Cataloging data flow and transformation stages for each of these can be daunting, especially when conducting data lineage manually.

Automating data lineage is a solution. Modern tools use AI and machine learning to track real-time data, even as it moves and evolves through different systems.

3. Evolving data landscapes

Data lineage solutions need to evolve to keep pace with new and changing technologies, platforms, and regulations. This can be time and resource-intensive, as lineage maps need to be regularly reviewed and updated. 

Using flexible and scalable cloud data systems to integrate new data sources and track lineage is a remedy.

4. Organizational adoption

Data culture comes from the top down and requires buy-in from multiple departments. A misalignment in goals, priorities, or understanding between leadership and operational teams can lead to date lineage efforts falling flat. Closer collaboration and employee training are key to driving adoption across the organization.

Data lineage best practices 

Addressing challenges and adopting these five data lineage best practices will overhaul how you discover and control data. 

1. Choosing the right tools and technologies

Selecting the right data lineage tools will make it easier to capture, track, and visualize data flows. Use a data discovery platform to catalog and map your data. Opt for software that detects a range of signals, including data size, type, PII, and PCI, to get a complete overview of your data pipeline. 

2. Regular updates and maintenance

To perform data lineage consistently, regularly update diagrams to reflect any changes to sources, transformations, and destinations. Leverage AI to automate data tracking and discovery when new systems are added and pipelines are modified.

3. Clear documentation

Data lineage isn’t just for technical data engineers. The maps and insights must be usable for stakeholders too. Leverage dashboards to create easily digestible lineage graphs and charts outlining data flows, changes, and dependencies. 

4. User training and adoption

Seven in ten U.S. companies say skills gaps are negatively affecting business performance. Effective data lineage processes cannot be implemented overnight. Take the time to train teams and outline clear guidelines to encourage adoption and align data lineage with broader objectives. 

5. Collaboration with data teams

Accurately visualizing high-quality data is the end goal. This requires active cross-departmental collaboration to prevent siloed operations, inconsistent data standards, and mismatches of technology and software. Data teams (engineers, analysts, scientists, etc.) need to work together within a centralized platform so data sharing is seamless and standardized practices are followed.

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Future developments in data lineage 

We’ve already come a long way since the days of manual data lineage, which was – to say the least – labor-intensive and error-prone. Today, we’re in the midst of major developments in how data is recorded and tracked, with the advent of artificial intelligence and the adoption of cloud-based data management services.

Future developments in data analytics and lineage will center around:

• AI and machine learning: Generative AI and machine learning will automate data lineage and reduce the burden on teams while increasing the accuracy of repetitive processes
• Data fabric: Unifying and managing complex data environments will be made easier with data fabric solutions, which deliver end-to-end integration of all data sources within a single architecture. Data fabric promises to vastly reduce the time needed for the design, deployment, and maintenance of data lineage.
• Cloud-native data lineage: McKinsey & Company says companies can unlock $1 trillion in value by following the example of cloud-migration leaders. With more organizations on the move, cloud-native lineage tooling will become essential for lineage tracking.
• Real-time data lineage: The prospect of tracking data in real-time will also make organizations more agile and flexible, and better positioned to take advantage of new opportunities and combat risks. 

Wrapping up 

Data lineage is a critical tool in today’s data-driven business landscape. By effectively mapping all the twists and turns your data takes downstream, from origin to endpoint, you can enhance the quality and integrity of data, as well as the value it brings to your business. 

Unlock the full potential of your data with data discovery and management tools from RecordPoint.

Our cloud-based solutions can help you make sense of your data and catalog it accurately, giving you a clear and actionable view of its lifecycle to enhance decision-making and support compliance. 

Contact us today to start your data lineage transformation.

FAQs

What is a data lineage tool​?

A data lineage tool is software that empowers users to track the flow of data across systems, providing a visual history of its origins and lifecycle. 

What is data lineage analysis?

Data lineage analysis examines exactly how data flows through systems, and more importantly, what changed during its lifecycle and why it went on this specific path from source to destination. 

By analyzing data lineage, you can extract actionable insights and use them to identify and address inefficiencies in data workflows, create a clear audit trail for regulatory requirements to meet compliance, and improve the overall quality of data sets. 

How can I measure the ROI of data lineage?

You need to link data lineage to clear business goals and key performance indicators (KPIs) to track return on investment effectively. 

What is data provenance?

Data provenance is a record of a data’s history, from its origins to its final state and every transformation in between. This record uses metadata to capture the changes. Data provenance informs and supports data lineage by ensuring data is transparent and accurate.