What is Data Lineage?
- Justin Reynolds
- February 8, 2020
Your organization puts a great deal of faith in the data that you use every day. After all, your data is deployed across all enterprise areas, from sales, marketing, and R&D to your website, CRM, and mobile applications — and everything in between. As such, your data needs to be clean, accurate, and compliant. Business outcomes and your organization’s reputation depend on it. Data lineage helps understand the impact of data changes on downstream analytics and applications, understand the risk of transformation to business processes.
Unfortunately, maintaining clean and accurate data can be a difficult task in a fast-moving enterprise. Data continuously flows in large organizations, moving in and out of on-prem servers and the cloud and back again. As data moves, it changes in structure and quality. And throughout this process, it becomes less and less reliable.
On top of that, when data has been in existence for a while, it can be impossible to tell where it originated or where it’s been. In the age of high-profile data heists and malware, this is a significant security and governance issue. To solve these challenges, many organizations are using the concept of data lineage to improve data management.
Data Lineage Defined
As we move deeper into the big data era, the need is steadily increasing for solutions that can analyze data as it flows across the enterprise to its final destination. This process of tracking and analyzing the origin, movement, and quality of data is referred to as data lineage.
Companies are now using various tools to survey and visualize this process, which we’ll explore momentarily. First, let’s take a deeper look at why you need to be using data lineage in the first place.
Why is Data Lineage Important?
Over the last several years, data governance has emerged as a critical need for organizations due to rising cybercrime, widespread cloud adoption, and increased regulatory compliance (e.g., the GDPR and the California Consumer Privacy Act).
More than ever, companies need to keep a tight lock on their data. As a result, data lineage tools are now playing a fundamental role in corporate data governance, giving organizations greater visibility and control over their sensitive data.
At the same time, companies need to be very careful about their information when compiling reports. Run analytics against outdated data, and all of a sudden, your “data-driven decision” is quite the opposite. Lineage tools can help employees get to the source of a dataset, enabling them to check for accuracy, which immediately prevents this problem.
With all this in mind, let’s explore some of the top reasons why data lineage is rising in importance.
Performing Data Root Cause Analysis
Discovering a data error in a product is one thing. Finding its location in the data pipeline is quite another.
Using data lineage tools, data stewards can investigate data values and determine the origin of an error — making troubleshooting faster and less painful. Teams no longer have to spend hours searching for the cause of a problem. It can be done in a matter of minutes.
Businesses today are collecting big data. Yet, the bulk of that data isn’t getting used or analyzed. Oftentimes, this is because companies can’t process it fast enough or because they don’t know where it is or whether it exists in the first place.
Data lineage tools can process, analyze, and transform big data into actionable insight — giving businesses access to more insight while improving ROI on analytics and IoT systems.
Mapping Data Transformations
Data pipelines are critical in an enterprise setting. However, many organizations have little to no visibility into these essential pathways. This is something that needs to change as companies continue to implement digital transformation and increase their data usage.
Data lineage tools let analytics professionals visualize how data moves throughout the company. They also make it possible to embed governance policies and guardrails across data pipelines, enhancing security while enabling ongoing monitoring and auditing.
Data Lineage Use Cases
Data lineage can be used across in a variety of ways — and across just about any vertical. Basically, any organization collecting and using data at scale should be using it to streamline operations. To give you a better idea of what that looks like, check out these three data lineage use cases.
Healthcare organizations need to keep a careful watch on where clinical and operational data originates, travels, and lives. Data teams can use data lineage to prove that the company is sticking to certain policies and regulatory procedures (e.g., HIPAA).
A global finance organization may use data lineage to protect itself in the event of an audit or to prove compliance with strict regulatory frameworks. A company can use data lineage to trace data through a pipeline to its source and see who accessed it or modified it along the way (e.g., financial statements). This is critical for referencing reporting data and providing accurate information to consumers and auditors.
3. Software Design
Companies today are challenged to create software quickly and cost-effectively. When errors arise, they need to be quickly discovered and eradicated. Data lineage tools can give engineers instant access to data sources, allowing them to work at a much faster pace.
Data Lineage Tools
Now that you have a better understanding of why data lineage is important, let’s take a look at some of the leading data lineage solutions on the market today.
Apatar is an open-source data integration solution and ETL tool for moving data across multiple formats and sources. This service makes it easy to map data using built-in mapping models. Apatar also offers pre-built integration tools that make things easy for end-users.
SentryOne offers documentation and data lineage analysis through the cloud. You can track data lineage over a visual display and automatically document multiple data sources — including SQL Server, SQL Server Integration Services (SSIS), SQL Server Analysis Services (SSAS), and SQL Server Reporting Services (SSRS).
Kylo is another open-source, enterprise-ready platform for data lake management. You can use Kylo to ingest and prepare data through cleansing, validation, and profiling. Besides, Kylo lets you search and explore data and metadata, providing instant visibility into lineage and profile information.
Octopai’s automated data lineage solution enables users to map the data journey from end-to-end. Octopai lets you see where data came from, how it was created, and how it transformed during transit. The user-friendly platform is known for helping businesses expedite their lineage projects.
Datameer SaaS Data Transformation is the industry’s first collaborative, multi-persona data transformation platform integrated into Snowflake. The multi-persona UI, with no-code, low-code, and code (SQL) tools, brings together your entire team – data engineers, analytics engineers, analysts, and data scientists – on a single platform to collaboratively transform and model data. Catalog-like data documentation and knowledge sharing facilitate trust in the data and crowd-sourced data governance. Direct integration into Snowflake keeps data secure and lowers costs by leveraging Snowflake’s scalable compute and storage.
In Datameer, complete data lineage is captured, all the way down to each and every transformation. This helps you understand where your data came from, how it was shaped, and where it went to. This helps data professionals with their governance and compliance processes and lets the analytics teams see how data was formed to build trust in the data.
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