Data Lineage Challenges: Understanding and Overcoming

Exploring common data lineage challenges and learning how to tackle them

Data Lineage Challenges: Understanding and Overcoming

Struggling to follow the journey of your data while your company grows? 

Did you know that workers spend 30% of their time every week looking for the right data to do their jobs? Having good visibility of all data flows is critical within the business.

This makes data lineage an important consideration for every leader. Still, it can be difficult to do data lineage well. Don’t worry, we are here to help. In this article, we are looking at common data lineage challenges and tips on how to overcome them. 

Introduction to Data Lineage 

What’s an easy way to define data lineage? Think of it as a family tree but for data. 

Data lineage displays the complete data journey from start to finish — from its origins to its transformations and destinations. It allows you to understand and visualize the relationships between data assets, such as parent and child tables. Using the analogy of the family tree, it helps you review problematic branches while reviewing all dependencies to guarantee accuracy and consistency. 

In other words, it helps you understand why, when, and how someone uses data. It is an essential tool for data management to help data teams save time while giving everyone in the company clarity on how data is related. 

Why Data Lineage Matters 

Data lineage is essential for data governance as it helps you understand where the data comes from or who is using it. Data lineage tools can help teams understand and visualize data sources, how they are connected, and the impact of potential changes.

As you get more consistent with a data lineage tool, it becomes easier to guarantee accuracy, compliance, and transparency in data management. 

For example, you can use data lineage to trace the root of data issues and how they are linked to different branches. You can also help different teams understand the impact of one data change on different downstream datasets and processes. 

Moreover, you can prevent compliance risks with automated data lineage that makes it easier to review your data, its journey, and how your company adheres to the latest regulations.

Ultimately, data lineage helps your company foster a data-driven culture that relies on quality data to make informed business decisions.

Common Challenges in Implementing Data Lineage

The best way to implement data lineage successfully is to be prepared for potential challenges you might face. Let’s look at a few of them that may already be familiar to you. 

Granularity Issue 

It’s a common headache for many data teams to struggle with the level of granularity they may face when implementing data lineage. 

How granular should you be and how challenging will it be to manage data at this level? Even worse, what do you do if you decide to implement data lineage too late when you already have too many data points?

A good way to address this challenge is to be clear from the beginning about what you want to achieve and the level of granularity you will need. Finding the right data lineage tool can also help you in the process. 

Lack of Standardization 

The lack of standardization -both internally and externally- can be a challenge for many data teams.

Internal standardization allows you to have a clear overview of all the available data. Not having a system in place before you implement data lineage can be time-consuming with a risk of losing important data. In this case, it’s good to start with data documentation to map out all your data sources and how you want to bring them together.

External standardization has to do with the relationships between different tools and systems. There are still many problems of compatibility and inconsistencies that prevent data teams from bringing their data stack together. A good way to address this challenge is to aim for cross-system data lineage, which provides an end-to-end overview of your data flow –from the data warehouse all the way to the BI tool and reporting. 

Variety of Data Sources and Transformation Techniques 

There is a growing variety in the number of data sources businesses have and number of data transformations on top of all that data. This gives you more options in choosing what works for your needs. However, it also makes it more challenging to stitch together different databases.

The more databases you have, the harder it becomes to track the full data journey. Things can become even more complicated in column-level data lineage with different SQL dialects among databases.

How do you address this challenge? You need to invest in a data lineage tool that helps you reach every corner of your data stack. Start with your company’s ETL technique to understand the best way to manage your current data and the best way to maintain an efficient process.

Timeliness and Updating Lineage 

The best way to maintain a successful data lineage is to keep it up-to-date. As simple as it sounds, it is a big challenge for many data teams. The lack of resources and time are important considerations in this case. 

In order to make the most of your data lineage, you need to set a schedule for the frequency you’ll be capturing data changes. Missing lineage changes could lead to incorrect metadata, so the best way to address this challenge is to plan how the ETL executions will be linked to data lineage tracking. 

For example, you can refresh lineage in every ETL deployment or you can choose the frequency that works best for your business based on your data changes and how closely you want to monitor them.

Case Studies: Challenges in Real-World Scenarios 

Let’s look at two real-world examples of companies overcoming their data lineage challenges.

Difficulty in Finding Context in Data

Printify was experiencing rapid data growth but the lack of data governance was impacting the overall data experience. Data consumers were spending too much time trying to find context in the data they were using but they still weren’t happy with data documentation. 

Data stakeholders realized that data lineage was important to help the team access the company’s data flow but it has to be a simple and straightforward process.

They decided to invest in a data catalog (this is us!) that allows them to make the most of end-to-end lineage. Collaboration was an important factor to lead the project to success. Each team is responsible for the data assets they are creating while they are working closely with the data steward to ensure documentation and definitions are standardized across the company. 

Answering the Same Questions Over and Over Again

JW Player was struggling with data discovery, especially for non-technical users who couldn’t navigate the data warehouse.

For many employees, finding the right data was a challenge, which led to asking different questions to different people to find an answer. 

They wanted to implement data lineage to help data users understand how each change affects the data infrastructure to ultimately build a single source of truth with standardization that everyone could follow.

JW Player wanted to find a data lineage tool that was both visually appealing and user-friendly. Their goal was to find a solution that was approachable for all business users. They ended up choosing Castor for its “strong, easy-to-use lineage feature” that empowered their non-technical team members. 

Overcoming Data Lineage Challenges 

Implementing data lineage doesn’t come without challenges. The first step to dealing with data lineage difficulties is to be clear on what you want to achieve.

Set goals on how you want to use data lineage, what problems it will solve, and who is going to use it. Scoping the required work before and after the implementation will help you organize your internal resources.

You can even be more specific with your needs by splitting them out between technical and business requirements. In order to make data lineage work, it’s important to talk to different teams to understand how they are going to use it. Your data team may want to know more technical details while business users would focus more on trust and transparency.

Last but definitely not least, you need to make it easy for everyone in the company to understand data and use the data lineage tool efficiently. You can’t have a successful implementation if your teams are not comfortable with using the tool.

You can follow Printify’s idea of organizing sessions to introduce the new tool to all stakeholders to explain its ROI.

The Future of Data Lineage 

Data lineage is evolving. New tools and features will show up and it’s ultimately all about bringing data closer to everyone.

Automated data lineage, for example, can make everyone’s jobs easier. Data teams will be happy that they reduce the manual processes and get a clearer mapping of data asset relationships. Business users will benefit from improved collaboration and transparency that promotes a data-driven culture. 

As our data assets grow, it will be a necessity to have a clear picture of all data flows and changes. We are expecting data lineage to become an even bigger priority and there’s no better time than now to build context around your data’s family tree.

How CastorDoc Can Help 

If you’re looking for your next data lineage tool, you’re in the right place.

CastorDoc helps you track your entire data ecosystem in minutes to save time and ensure data reliability. You can map your entire data lineage to the column level while automation promises to help you see exactly where data is coming from. 

“Our #1 most favorite part is the lineage tool - it has been incredibly timesaving for us when making logic updates and changes to understand the downstream impact of said changes.” – Clare M. 

Many users praise CastorDoc’s user interface, which makes it easy for everyone in your company to use it. In fact, G2 users have rated it with 9.6/10 in Ease of Use and an overall rating of 4.7/5.

Interested in trying it? Try CastorDoc today with a 14-day free trial to improve your data experience.

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We write about all the processes involved when leveraging data assets: from the modern data stack to data teams composition, to data governance. Our blog covers the technical and the less technical aspects of creating tangible value from data.

At Castor, we are building a data documentation tool for the Notion, Figma, Slack generation. We designed our catalog software to be easy to use, delightful and friendly.

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