Data lineage charts the full life cycle of data: the path from its creation through consumption, and everything that happens along the way. For organizations interested in achieving strong data management programs, data lineage is a key component. It provides a more granular view of your data, allowing you to gain insights from the ways in which data is manipulated and transformed from collection to application.
The goal of any data management program is to ultimately bring a greater sense of trust in your information which plays an important part in data governance, compliance, and data quality as a whole. It’s easy to understand why data lineage is so crucial. Whether you are implementing more a rigorous data quality strategy, advanced analytics, or a more effective data governance plan, data lineage (also known as both data life cycle and information chain) can help deliver better results. Here are three key ways understanding your data’s lineage is important:
1. Enables root-cause analysis.
Sometimes data can be like the game of telephone: what you get at the end is something completely different from what you started out with. Sometimes, that is due to data transformation, standardization, or some other intentional manipulation of your data. Other times, it’s because something went wrong along the way. That’s where root-cause analysis comes in. This analysis allows you to identify where on a data pathway the defects occurred and more importantly, why it occurred. This allows you to more quickly get to the source of the issue, figure out how to resolve it and put into place procedures to avoid it happening again.
2. Illuminates data flows.
Without the help of data lineage, the flow of data throughout your organization can inevitably seem like a black box process. You may know what the data looked like at the beginning, and what it looked like at the end, but everything along the way is a mystery. The information chain grants you access to the internal workings of your data, so you can understand the role each data item plays as it makes its way through each step in the process. This process does not necessarily have to be visual, but it does need to be accessible and well documented.
3. Increases accountability.
One of the most crucial pieces of an effective data governance strategy is establishing data roles throughout your organization. While assigning responsibility for data to various individuals across your business is a good start, holding those individuals accountable is the next step. Data lineage increases accountability for roles like data owners and data stewards. Data owners are the leaders of your organization tasked with ensuring the overall quality of a defined dataset and data stewards handle the day-to-day duties required to upkeep the data quality. Along with a view of how data morphs and evolves throughout its life cycle, data lineage allows you to discover the rightful owners and stewards of the information to ensure they are keeping up their end of the bargain.
The right technology allows your organization to track your data’s lifecycle, and you can even automate data lineage mapping. It requires advanced data discovery capabilities to identify relationships among your data, track data items throughout workflows, and monitor pathways for data defects. Of course, these complex capabilities are necessary in order to keep up with best practices for modern data management—particularly data governance.
Are you interested in understanding your organization’s data lineage? Experian Pandora is a robust data management platform that empowers you to discover and document the data lifecycle.