Data lineage traces the journey of your data: its origins, destinations, and characteristics. The term may also refer to the quality of your data and how it’s cataloged in your system. This information is stored as technical metadata and used by database systems to address issues. Tracking data lineage will provide your organization with visibility into how data moves through your systems, allowing you to optimize organizational decision-making.

The amount and type of data lineage needed will be determined by the scope of the data governance, which are the rules that lay out how you handle the data gathered by your organization. A data lineage program is a core component of any organization’s data governance structure. Visualizing this data landscape using data lineage principles will give you a view into how your data is being used, how often, and by whom, which will in turn help you better structure your governance policies.

The identifiers used in your data lineage should be unique, so they can be used to locate the source of an individual data point in your organization’s cloud-based or on-premises data warehouse. It can also simplify the process of transferring data, either to the cloud or across systems, as well as make that process more efficient by documenting the entire data journey from source to destination. 

It’s possible to display data lineage visually, with, for example, dots and lines representing the containers of the data and how it is transformed on the journey among containers. The lineage can be displayed in various levels of granularity. You can zoom way out to show the big picture or focus in on the point level to present the details of the data and its attributes, behavior, and journey through that specific location in the data lineage. 

Benefits and importance of data lineage

The benefits of data lineage are significant for all data-driven businesses: Today’s companies have to manage larger volumes of data than ever before. In order to stay competitive, organizations will need to quickly locate relevant data and analyze it to extract insights that will drive smarter decisions. 

When your data is properly identified and traceable, its journey can be monitored. If a group of database records happens to go missing, the data lineage will help you determine what went wrong and how to restore it. 

Data lineage tools can also be used to enable better decision-making by allowing data scientists to visualize the flow of information through processing to its final destination. Gathering information about the configurations and flow of data pipelines can easily organize a data architecture that feeds into an analytics platform to create a single source of truth across the business. 

The use of data lineage can also help increase operational efficiency and effectiveness, by freeing up IT staff to work on other initiatives instead of spending time mapping the data manually. It can also make your systems run more smoothly by identifying any potential bottlenecks before they impede your operations. Data lineage provides a map of data pipelines, so execution times can be profiled across several different pipelines, and stoppages can be avoided by redistributing processing. 

Data compliance for companies with large volumes of data is complex and challenging. It can take hours of manual labor, and if it isn’t performed correctly, the result can be severe penalties and large fines, depending on the industry and the regulations of the country where the business is located or subject to laws. Both the data governance and risk management teams can benefit from a map that serves as a single source of truth, drawn from data lineage, to enable a clear view of the audit trail. 

Steps to data lineage implementation

  1. Secure support of C-level executives 
    With incentives like boosts in efficiency and increased revenue, data lineage initiatives will be more likely to be approved by senior management once they understand how it can improve the quality of your insights and the bottom line.

  2. Identify key business reasons 

These might be:

  • Changes in business drivers
  • Regulatory or audit requirements
  • Data quality projects

3. Lay out the requirements of your data lineage project

Choose the datasets that you will be tracking, and decide which are the critical elements within each set. 

4. Determine the method of data lineage documentation

The two main methods of documenting data lineage are descriptive and automated. Choose the one that is most relevant for the needs of your organization. 

5. Select the appropriate software

Sure, you can do it with Excel — but a specialized application can take the manual burden off your IT staff and offer more robust features and greater ease of use.

What do I need to track data lineage?

In order for your data lineage repository to be worthwhile, you need high-quality metadata. Metadata is a key element that must be properly maintained and managed. It should contain the following data attributes:

  • Database names, data types, and table names
  • Details of reporting systems
  • Lists of data flow users, processes, and systems
  • Business terms, operational procedures, and rules
  • Time stamps for data changes, deletions, and adds 
  • Data retention, location, and format policies

This metadata is used to create a map of the entire flow of data, from original sources to final destinations. The resulting report lists each value and field stored in the repository and can be used to create a visualization of the data flow to help quickly determine where data originated, what processes were used in the calculations, and what reports used the results.

Although you can track your data in a spreadsheet program like Excel or use a mapping of extract, transform, load software to provide lineage, a specific data lineage application will provide more granularity and even offer automation. There are some excellent open source data lineage software tools available, and some are even free. These tools are designed to help business users and developers transform, map, and manipulate their data in a variety of data sources and formats. CloverDX was one of the original offerings in the marketplace and is still going strong. Dremio and Kylo are designed to be enterprise-ready solutions. Talend’s Open Studio offers a variety of data lineage solutions for different use cases.

In order for companies to most efficiently utilize their data, they must understand the data lineage. This enables them to effectively predict trends and improve BI efficiency. When an organization understands where its data came from and what happened along the way, it can extract the most valuable insights from it. 

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