Data lineage – let’s talk what’s good about it

data lineage

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Every piece of information or data goes through a journey from creation to final utilization. On this journey, you must keep points where something happened to that data. We often disregard all that is related to data by thinking that the information we got is as it is. After its utilization, you either forget about that data or don’t maintain it. But, for proper data management, data lineage is what makes the difference.

Data lineage is not anything new, but still something that gets a bit disregarded in some systems. It’s actually crucial in following data, it’s quality and usability.

Data lineage as a support system

Data lineage follows data and its journey, and it’s the process of recording, understanding, and tracing data origin from start to finish. It shows how data flows from source to its users, and records every change or transformation, usage, and historical touch points with data assets. We can almost equalize data lineage to the data life cycle. 

It is critical in data management since it provides a clear and transparent view of data movements across systems and technology. Data governance, security, and privacy are widely affected and improved by maintaining proper and adequate data lineage. Basically, data lineage is the context of how data travels and transforms within different tools, systems, software, and others. 

In a world of massive amounts of data, having a support system like that provides certain structure and reliability. Data teams and other stakeholders have a complete view and insight into what happens with data at any given time.

What can you expect from data lineage?

Data lineage shouldn’t be considered just as “documentation” on data. It should be observed as a tool that allows trackability and data management.

More trusting data migrations

By knowing the data origin, its path, and transformations, it is easier to migrate data from one depository to another. There is a higher level of confidence and trust that the right data will be migrated to the right new destination. Migration projects are easier and less risky when data characteristics and lifecycle are easily understandable. 

Easier errors and mistakes tracking

Data lineage provides a clear overview of data movements and their history. This in turn ensures easier errors and mistakes tracking. In case there is an issue, data teams can easily find out at what point did something happen to the data and where the error originated from. 

Better data governance

Data governance, compliance, auditing, privacy enablement, and regulations, can be more optimally managed with data lineage in tow. Complete clearness of data provides support in ensuring that policies and regulations are followed. It also simplifies regulatory compliance implementation.

Higher understanding of data meaning and validity

Higher data lineage allows for better data trustworthiness. Data users can understand data better when they know how it came to be, from the start to the point of observation or utilization. This improves data validity and veracity

Lower risk process changes

If there are upcoming process changes or changes in general within the data management domain, they can be handled with much less pain or stress points since the risk of them is much lower.

Data map and comprehensive metadata overview

Data lineage is extremely closely connected to data cataloging, data classification, and metadata management. It maps out each data point on its journey or lifecycle. 

Impact review

Data lineage provides insights into how one change can affect other elements in the company. If some aspect of data changes, data lineage can show what else (where this data is used) will be influenced.

It’s a must

There is no way for you to avoid data lineage and not devote your time to it. Not only because of the benefits it brings but also because it’s an integral part of any data management system and flows. Data integrity and reliability are outcomes of full data visibility or lineage. 

Trust is a big thing in data. How can you be sure of your final results, metrics, and insights, if you do not trust data and its sources? Transparency in data is vital in knowing if you’re dealing with quality and correct data or not. This in turn makes your analytics more accurate and useful. If you can trace data from its source or moment of generation to the end, you can be sure that when you use it in analytics it’s for the right reasons. This shows that data practices are correctly organized and performed. 

Data’s value derives from its characteristics and integrity, so we need to be sure it wasn’t manipulated or wrongly transformed at any point. Data lineage also allows users to determine if some of it was streamlined to the wrong depository or endpoint. 

But, what major advance data lineage provides is easier implementation and management of regulatory compliances. Companies can introduce changes to data systems more easily when they know where to look, process, and introduce the changes. But, when you surveil data, security, and privacy are tighter and stabler. 

The best use

Data lineage is not limited only to data teams. With data democratization on the rise, business executives, sales, production, marketing, and other departments, are all included in the data lineage process. Each data user needs to be familiar with how data is formed and used. These are also the people who can verify the validity of data and its usability. 

Lineage in itself should always produce benefits and value. It’s about leveraging data to enable better business decisions and strategies. Because in the end, if you do not drive value from data, it’s either redundant or it will become obsolete. 

But, let’s not forget that we can divide lineage into business and technical lineage. Business lineage refers to the high-level information about data origin, movements, and business context, whereas technical is linked to transformations, pipelines, storage, tables, data format, and such. The value of each depends on the final user. 

Data lineage has the cause-and-effect principle down to pat. It shows or traces how one change will influence downstream systems and stakeholders. Basically, it preemptively helps users understand what will happen to results or data if it’s changed, added, or influenced in some way.

Make it useful, not a must-have

It’s easy to get something just because is mandatory or if everyone else has it. You don’t introduce data lineage just because. It has to have a purpose and proper structure. Tools used for data lineage have to be in line with existing systems and they should support it. It’s no issue betting on data, but make sure that you’re doing it for the right reasons and that you know how will it behave. It’s better to have less data, but higher quality, than bigger amounts of bad data. 

Data lineage is supposed to help you keep track of data and support proper documentation on it. It needs to be useful, plain and simple. It will have to be a system reserved to answer each and every question on all data or information, no matter the role of the end user.

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