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All data-driven organizations are aware of the fluidity of data. Far from being a static resource, they are constantly moved from place to place, transforming with every step of their journey. However, this malleable resource is critical to making informed business decisions.
This is why data lineage is so important. It allows organizations to understand how data travels from one place to another within a cloud or on-premises infrastructure, tracking its course through databases, data lakes, ETLs, and more.
In this article, we explain what data lineage is and why it’s so important to commit to accurately building one in your company. Follow!
What is data lineage?
We can define data lineage as the complete lifecycle or journey of the data. This cycle includes where the data originates, how it got from point A to point B, and where it is today.
By leveraging data lineage, companies can better understand what happens to data as it travels through different pipelines. It also allows companies to trace specific data sources to look for errors and implement process changes, as well as streamline system migrations.
Why is it important to track data correctly?
The modern data ecosystem is a minefield, a complex web of systems and processes that users can only successfully navigate with a dedicated governance tool. If your ecosystem lacks data lineage, the consequences can be significant.
Lack of trust in data products
Users are becoming increasingly disillusioned with data products because, without precise lineage, there is no proof that they are what they claim to be.
Data quality issues
When you can’t trace the source and flow of data, you can’t improve its quality. As a result, the lack of lineage leads to ongoing data quality issues.
regulatory compliance
Data privacy compliance is just one of many regulatory compliance statutes that affect businesses across all industries. Auditors need proof of data lineage to ensure users handle data correctly.
What are the benefits of data lineage for companies?
Data lineage has five significant benefits to an organization’s digital health and success. Here they are in order of importance.
Conformity
Data lineage is vital to comply with data privacy regulations. Only with complete data lineage can your compliance team confirm that the data is secure and has not been exposed to a breach at any point in its lifetime.
Impact analysis
Data lineage allows organizations to quickly identify any data assets affected by modifications. Speed is essential in impact analysis to avoid adverse downstream outcomes. When it is implemented, it is possible to mitigate any inadvertent disruption of data assets.
data quality
Data quality improves through a process called the data quality improvement lifecycle. One element of this is root cause analysis. As the name suggests, examining the root cause of data quality issues involves going back to the source and analyzing what went wrong and why. Data lineage allows for this analysis.
What are the key techniques for evaluating and performing data lineage?
When building the data lineage system, we need to track each process within the system where we are doing some transformation or processing. Here are some common techniques used to perform data lineage on strategic datasets.
pattern-based lineage
Without managing the code used to generate or change the data, this technique identifies patterns in the data and applies them to deduce its origin and history. It is built on analyzing metadata for tables and business reports using pattern recognition.
Lineage by marking information
This method assumes that the data has been tagged or tagged in some way by a transformation engine. And then it follows the tag all the way through the process to discover the lineage.
Lineage by analysis
This technique involves automatically reading the logic for data processing. It performs full end-to-end tracing by reverse engineering the transformation logic, which can be useful for identifying data dependencies as well as detecting data quality issues.
independent lineage
Independent lineage refers to the ability of an information system or structure to independently trace the history of its components and dependencies without the need for external records or information. It is a self-contained process where information is centrally stored, processed and managed for complete control over metadata.
When can data lineage be used?
Data lineage supports a variety of use cases not only for IT teams, but also for stakeholders across the enterprise.
root cause analysis
Knowing the sources of the data can be essential if you need to track down the root cause of an issue triggered by a data quality issue. For example, user login requests may be failing because the database storing login credentials has changed. If you know what data transformations took place, you can more easily correct them to fix the login issue.
reporting
Various industries—such as IT, marketing, sales, and others—may want to generate data-driven reports. These reports will be faster to generate and more accurate if stakeholders can disaggregate the most relevant data from a broader dataset.
Maybe the sales team wants to extract sales-related data from a database that contains a broader set of customer-related information, for example.
Definition of data retention rules
Compliance, security, or governance requirements may require you to store certain types of data for a certain period of time. In some cases, you may also be asked to delete data after a certain period.
With data lineage, you’ll know which specific records are subject to which retention policies, even if you’ve aggregated or transformed the records since they were originally created. In turn, you can define appropriate data retention rules in a granular way.
In summary, data lineage helps the user to ensure that the data is coming from a trusted source, the transformations are done properly and correctly loaded in the designated location. It plays an important role where key decisions depend on accurate information and allows tracking of the data flow from both endpoints to ensure the data is accurate and consistent.
Since we are talking about data, continue here on the blog and check out 5 steps to migrate your hosting without errors!
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