Data lineage can be described as the journey taken by data from its creation through a transformation over time. It is also viewed as a store of a wealth of information that can be difficult as tracing data sources is not a small task. As our data systems become more and more massive each day, the identification of "source" data is becoming near impossible yet more and more essential.
Just like tracking the source of your family lineage, tracing the origin of data can be a difficult task. Imagine figuring out your family lineage to understand your origin, how it contributes to genealogy, discover the birth and death rates in the family, and most importantly, identify your medical history. While this can be an arduous and demanding task, the knowledge of the above can be of great help and contribute much to your present life.
The same rules apply to data lineage. Large organizations have acquired different systems with various data entry points and transformation rules to modernize with technology. These monitor the movement of data into and across the organization. Thus, getting ahead of dart lineage is critical for data success as it increases compliance in your organization.
Some of the tools are extract, transform and load (ETL), file transfer protocol (FTP), business intelligence (BI), Procedural code, application program interfaces (APIs), among others. Business intelligence reports help in the aggregation and transformation of data.
The diverse data sources, together with their integrated systems, form a complicated data web that's difficult to understand. This is the reason behind tracing data lineage and why its role is so essential to the operation of businesses. How it is transformed also provides information on the origin of data and how it moves into, across, and outside an organization.
Here are five reasons why you should prioritize data lineage in your organization:
Data policies and business terms should be implemented through documented and standardized legislation.
For instance, The General Data Protection Regulation (GDPR) and the California Consumer Privacy Act of 2018 (CCPA) regulates how organizations, companies or websites, that collect, use, or share consumer data handle individual' personal data obtained both offline and online in a bid to ensure its protection.
Data lineage helps in tracing compliance with the business rules. Incorporating auditability and validation controls across the data transformations and pipelines help generate alerts when there are non-compliance data instances.
Different organizational stakeholders need to understand and trust the reported data. Data lineage helps prove that the data provided is reflected accurately.
Data ownership, traceability, and accountability are foundational to a sound data governance program/strategy.
An automated data lineage solution puts together metadata to validate and understand data usage and vindicate the associated data risk.
It can auto-document end-to-end downstream and upstream data lineage, revealing any changes made by whom and when.
Four factors affect data quality: data's movement, interpretation, selection, and transformation through technology, people, and processes.
Root-cause analysis is the first step in repairing data quality. The reason for data error can only be determined once a data steward determines where the data flaw was introduced.
Data lineage and mapping help data stewards tracing the information flow backward to examine the transformation and standardization applied to confirm whether they were performed correctly.
Reporting and analytics are data-dependent. This makes the collaboration among different business groups crucial.
Visualization of data lineage helps business users spot the inherent connections of data flows, which provides greater transparency and auditability.
The essential nature of data to every organization's survival cannot be underestimated. Therefore, businesses must think about the flow of data across multiple systems that fuel organizational decision-making. One bad link in the chain and your data integrity is compromised.
For a business to have a clear understanding of where data comes from, who uses it, and how it transforms, it needs data lineage. Businesses make clear and well thought through decisions when they have a clear understanding of their dataset's three fundamentals, that is; where it comes from, who uses it, and how it transforms.
Data lineage provides solutions in the event of a change in data expectations. It provides a way to determine which downstream applications and processes are affected by the change and plan for application updates.
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by Mike Higgins
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