Data lineage allows organizations to identify datasets usage, changes, and dependencies, analyze data flow, and recognize transformations. Businesses can not only identify and eliminate the source of errors, but can create new applications with ease, solve problems in existing applications more quickly, and improve data quality. Moreover, data lineage provides insight into the correlation between data objects and flows to ensure successful cloud data migrations and modernization initiatives. A deeper understanding of data helps data architects predict and validate the effects of moving or changing data on downstream processes and applications that rely on it.

Let’s delve deeper to understand data lineage techniques, implementation, and best practices.

Data Lineage Techniques

Organizations can execute data lineage on premeditated datasets using prevailing techniques. These techniques enable organizations to visualize data elements at every stage by tracking every data transformation or processing.

The following are a few standard techniques used to perform data lineage of strategic datasets.

Lineage by Data Tagging

In lineage by data tagging, every single data that travels or transforms is tagged by a transformation engine. Each tag is then read from start to end in order to determine and portray its lineage. However, this technique's effectiveness depends on the consistency of data lineage tools or the transformation engine used to control data movement.

Lineage by data tagging technique also disregards data movements outside the transformation engine. Hence, it becomes unsuitable to perform data lineage on a closed data system. Thus, making it a not-so-preferred data lineage technique.

Lineage by Pattern

The lineage by pattern technique deals with patterns instead of codes used to transform or generate the data. It involves the evaluation of metadata for reports, tables, and columns and profiling them to investigate similarities and patterns. Its primary advantage is that it screens only data and not data processing algorithms. Hence, it is unrestricted by technology and can be used in the same way across any database technology such as Oracle or MySQL.

However, lineage by pattern may not always be accurate. It is because the metadata may not reflect the data connections, mainly when the data processing logic is not evident through the code and is invisible in human-readable form. Therefore, this technique is suitable for data lineage use cases when understanding programming logic is unlikely because of remote or inaccessible code.

Lineage by Parsing

Lineage by parsing is one of the most superior lineage forms that automatically reads the logic used to process data and easily captures transformations across systems. The users can achieve complete end-to-end traceability by reverse engineering data transformation logic.

The lineage by parsing technique is somewhat complex to set up. It requires understanding all the tools and programming languages used to transform and process data.

Data Lineage Implementation

The benefits of data lineage should not only be the sole reason for its implementation in your organization. It will help if you have a data management framework and an alliance with data management professionals for successful data lineage implementation. When you have these in place, consider the following points to implement data lineage in your organization successfully.

1. Identify the reasons: An organization might wish to implement data lineage for data quality initiatives, auditing requirements, or changes within the business. Therefore, determine whether implementing data lineage is critical to meeting business goals.

2. Involve the senior management team: Implementing any key initiative in an organization involves human resources, cost, and time. Hence, the involvement and support of the senior management professionals are crucial to making the project a success.

3. Measure the plan: When the senior management team approves the project, determine whether its scope aligns with identified business drivers and critical data elements. An organization’s performance and customer satisfaction are most significantly affected by essential data elements.

4. Outline data lineage scope: Data lineage begins with data sources and terminates at the point of the last usage. Depending on the size of an organization, it can decide data lineage length starting during the data aggregation process.

5. Understand stakeholder/business requirements: Business and technical stakeholders have diverse interests and expectations in an organization. While business stakeholders can be more interested in the value of data lineage, technical stakeholders might be interested in impact analysis. Hence, industry experts believe that the data lineage team should spend some time clarifying stakeholders’ expectations and documenting the requirements.

6. Decide data lineage documentation method: Data lineage can be documented through descriptive and automation processes. While each process has its pros and cons, both involve time, cost, and resources. Thus, evaluate the suitable approach for your organization and then decide on the software or application to be used to document data lineage. Although data lineage documentation is complex, it is vital for an organization that aims to comprehend and effectively use its data.

Data Lineage Best Practices

Data governance requires planning, implementing, and ensuring where your data comes from and where it will go. You can consider a few practices while planning and implementing data lineage initiatives in your organization:

1. Automate Extraction: Data is dynamic, and so is its lineage. Hence, manually tracking and recording the lineage is not feasible to compete in today’s fast-paced business environment.

2. Verify and review metadata sources: Big data tools, ETL software, and other customized applications create data about the data they process. Those responsible for using the data lineage applications and tools should be able to analyze the respective metadata sources and identify the root causes of data errors. You should include this metadata because it helps understand the flow and modification of the data.

3. Plan extraction and validation: Consider extracting lineage and metadata according to the data flow sequence through your organization’s system. This will simplify to outline relations and dependencies between systems and within data. Examine the connections between systems on a high level. Once the data set is connected, drill down to the data elements and validate the transformation level's documentation.

4. Implement a data catalog software: Use an automated and intelligent software tool to collect lineage data from all sources. You can also infer lineage and extract from metadata using such software.


When making critical business decisions, accurate data is crucial. It is virtually impossible or time-consuming and costly to track data without appropriate technology and processes. Experts recommend that technology leaders explore data lineage solutions. The information you get from lineage helps you make better decisions for your company. With data lineage, organizations gain insight into data flow throughout the lifecycle. It can help them spot errors beforehand, manage data governance, analyze the impact, and make better data-driven business decisions.