A Complete Guide to Data Lineage

Data lineage is a process for tracking the evolution of data as it flows from source to destination. It makes it possible to understand the connections between different data sources.
Last updated
May 2, 2024

Data lineage is the path that data goes through from the original source to final consumption or storage. It can also refer to the description of how and where data changes as it moves through its life cycle. The value of data lineage is that it's a track record of everything that piece of data has gone through, and therefore creates accountability for those interacting with data.

A typical data lineage diagram documents the location of data, either in a database or data warehouse, and shows how it travels from one place to another. It also shows how that data changes as it moves through the system.

What is Data Lineage?

Data lineage shows how data has been transformed and where it is stored

  • You can create data lineage by repeatedly taking small and discrete steps. These steps are:
  • Identifying all the sources for your data (e.g., which tools or apps generate it);
  • Determining how the data gets transformed along its journey (e.g., when it is cleaned, filtered, or combined with other datasets);
  • Finding out where the final versions of each dataset are stored.

Understand the connections between different data sources

Data lineage is a process for tracking the evolution of data as it flows from source to destination. It makes it possible to understand the connections between different data sources so that users can answer important questions about where their data comes from, what transformations occurred on it along the way, and how it is ultimately used.

By understanding these connections, you can make smarter decisions that are backed by valuable data. For example:

  • If a source system updates its schema or API in a way that will impact how your company collects its raw data, you need to know how that change will affect downstream systems and processes. There may be an opportunity cost of making changes to your analytics pipeline if those changes affect other parts of your organization. Data lineage helps you make sense of these relationships so that you don't have to keep everything in your head at once.
  • If someone makes an update to derived data which has unintended consequences on other parts of the organization, you need to be able to track down who made this change and revert back before any damage is done

Data lineage provides a visual representation of the complexity of data analysis

A visual representation can often communicate information in an easier way than text alone. As the old saying goes, “A picture is worth a thousand words.” Data lineage diagrams are a great way to visually depict the inner workings of a data analysis project.

For example, if you have several people working on one big project, a data lineage diagram will show how all their parts come together to produce the final result. Or if you're running multiple parallel projects with some elements that overlap, a data lineage diagram can show how those projects are related and which pieces are shared between them.

Data lineage is needed for organizations that want to provide transparency about where data comes from and how it's used.

In some industries, such as health care, this is a legal requirement. In others, such as banking, it's simply best practice because of the value of the data involved. Regardless of why you need it, creating data lineage can be challenging and even overwhelming if your company stores its data in multiple places and on multiple systems.

Data lineage documentation can help with compliance issues

Data lineage documentation can help with compliance issues. If your organization is subject to GDPR, PCI-DSS or other regulatory compliance regimes, then data lineage documentation can help you respond to the inevitable data requests that will arise during a compliance audit or investigation.

If your organization has ever been audited for any reason, you know that it’s time-consuming and expensive to gather all of the requested information and ensure it’s accurate and complete. If you don’t have documented data lineage, then every request for information generates new work—and every time a business process changes, you need to update all of those records manually again.

A data catalogue tool like Secoda can be used to create data lineage

You can create data lineage at any point in the business process. For example, you can create data lineage between a source system and a staging table. On the other hand, you could also create data lineage between a target table and the source system. In addition to that, you can create data lineages for individual columns as well as for groups of columns.

A tool like Secoda is an integrated platform that lets you create data lineages automatically. It has simple drag-and-drop interface and powerful algorithms which let you update your data lineage diagrams as soon as the code is changed or when new systems are added to your enterprise architecture (EA). Secoda has a built-in business process modeler which lets you link your business processes to the underlying code itself. You do not have to draw these diagrams manually anymore because building this kind of documentation is now automated using Secoda's tools

A tool like Secoda can be used to create ERDs from which data lineage diagrams are derived

Creating a data lineage diagram is easy with a business process modelling tool like Secoda. If you have an existing ERD, you can use it to automatically create a data lineage diagram using the built-in tool. This allows you to start with an existing database design and generate a diagram showing how data flows through processes and systems. Alternatively, if your organization has already created a data lineage diagram, you can export that diagram into an ERD using the same tool. This lets you easily start creating new database designs while showing how they will be connected to your existing systems and processes.

Creating ERDs before data analysis projects begin helps ensure that all analysts use the same definitions for concepts and terms.

Everyone needs to have a common understanding in order to communicate clearly. The same is true for data! Creating ERDs ensures that everyone is using the same definitions of terms and concepts. If a conversation breaks out between analysts about what "customer" means, you can consult your ERD to see how it's defined, and then make sure that everyone agrees on your definition.

ERDs also help with compliance, ensuring that people are using the correct definitions when working with things like personally identifiable information (PII) or protected health information (PHI). A clear, up-to-date ERD helps analysts understand all the rules and requirements for their specific organization.

Using an agreed-upon set of definitions helps ensure good data quality, which improves data governance and contributes to better data security and privacy practices. With an ERD in place, it becomes easier to ensure that data is being used correctly, from who can access it to where it’s stored.

You need a tool to manage an enterprise-wide view of your organization's data

Your data catalog is essential to understanding your data lineage. It is also used to manage your data quality and governance, metadata, and help you define your data quality rules.

The best way to create a lineage view is to have a tool that tracks the most important aspects of the lifecycle of your data – from creation through consumption. To create a complete picture of the lineage for any given dataset, you will need the ability to see all types of operations on that dataset. This includes:

  • Creating datasets (whether it be manual upload or writing code)
  • Finding datasets (i.e., searching in a catalog)
  • Using query languages like SQL and HiveQL to process datasets
  • Data transformations using libraries such as Spark or Python pandas, etc...

Exporting or sharing processed data with downstream consumers

An integrated platform can let you automate the creation of data lineages and automatically update them as code changes are made and new code is deployed.

Without a single source of truth for what data is created, where it goes, and how it gets used, your business runs the risk of making strategic decisions based on faulty or outdated information. The key to ensuring that you have accurate data lineage is to make sure that your organization has an integrated platform. Once you have this kind of platform in place, you can then automatically create data lineages as code changes are made and new code is deployed. Let’s take a look at how this works by examining real-world examples of how companies are using software to automate their lineages—and how they’re getting even more value out of the process.

To get started with automating your lineage creation, you need access to the right tools and technologies. You need to be able to easily manage and control your data so that you can quickly identify the sources for each piece of data used in any analysis or decision-making process. It starts with knowing where all your data actually comes from: Is it from internal systems like HR databases? Is it gathered from outside sources like market research reports? Or does it come from external systems like social media feeds? Once you know where your data comes from, you need to be able to model the entire business process so that there is always a clear path back to the source for each piece of information that gets used along the way.

Manually creating data lineages just isn't practical anymore

You can't manually create data lineage. Why? The process of doing so is time-consuming, cumbersome and error-prone. It's just not practical with today's volume and velocity of data, especially given the fact that your data is constantly changing. For example, when you update a field in a source system or make a change to an ETL script that transforms your data, the metadata used to generate your data lineage diagram must be updated accordingly. That's why it's essential you have a tool that automatically generates your data lineage.

Implement automated data lineage to optimize efficiency

In order to optimize efficiency when it comes to automated data lineage, you should consider implementing the following:

  • Automate tasks that would traditionally require manual intervention. For example, using a data dictionary will automatically give you an inventory of the fields being used in your various reports.
  • Automate the creation of lineage representations and mappings between systems. An automated system can run daily and compare what's in production versus development—and then document any changes made by developers. When a project is complete, this tool can generate a mapping diagram for stakeholders to review.
  • Use automation to provide greater visibility and control over data processes. This can help reduce errors that may be difficult or time-consuming to fix manually.

Its important to understand data lineage and its impact on the business

Lineage is the end-to-end history of data. It’s important to know that you can use lineage for two main purposes:

  • gaining insight into data — Lineage gives you a clear view of what your data represents and how it travels through the organization, giving you greater understanding of both the business and technical aspects of your data.
  • retaining control over your data — Lineages are also useful in troubleshooting why datasets may have been compromised or if they are improperly used. They can help quickly determine the root cause when there’s an issue with the accuracy of your datasets, which reduces the time to resolution when there’s a problem with your data, which means less revenue loss.

Although lineage is commonly associated with ETL (extract, transform, load) processes for moving and transforming raw or legacy formats into a structured format for reporting and analytics, it isn't limited to any specific type or class of process or environment. A lineage diagram should be able to trace all artifacts from raw source all the way through to final delivery in reporting and analytics environments. For example, here's a visualization showing the path from various source systems (including Excel spreadsheets) through transformations into Salesforce reports:

Without good data lineage it is difficult to maintain good governance

Good data governance is an important part of running a successful organization, so having good data lineage is essential to running your business.

When you have good data lineage, you can understand how your data flows around the organization and how it gets used.

Knowing where your data comes from and where it goes not only helps you keep track of what's happening with your company's data; it also helps you keep your business afloat in bad times because it enables you to follow best practices for quality assurance and compliance.

Understanding automation of data lineage is important.

Data lineage is the process of tracking data through its lifecycle, from its origin to how it’s used. You can think of it as a trail that each piece of data leaves. It shows how the data is changed and by whom, and how different systems interact with it.

Developing a formalized system for tracking data lineage will help you find where potential issues are, save time in your business processes, and give you more confidence in your business intelligence reports.

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