In simple terms, Data Lineage can be compared to the children's telephone game. While the message changed to something entirely different by the time it reached the last person, it left the players puzzled and clueless, wondering how the message transformed. In the same way, as an enterprise' data assets pass through its data architecture, the data lifecycle becomes a concern.

Your business should always have data access, as almost every aspect of your business is dependent on data to some extent. You might feel rushed trying to figure out how to manage your data and lack time to look deeply into how it's performing for your company. For this reason, it's crucial to understand the source, movement, and application of data within the business to determine its value.

This is where data lineage as a process and tool comes in to uncover and examine the data source to ensure that it reaches the right place.

Let’s explore what data lineage is and why it’s essential for your needs.

What is Data Lineage?

Data lineage is a log of the data journey, which explains where the data originated, where it has stopped along the way, and how it has progressed. Data lineage can be visually mapped from source to destination, including variations, differences, and breaks. In short, you can consider data lineage as a GPS that tracks the data route map.

Using accurate data to make strategic decisions is crucial. Operational aspects such as everyday use and error resolution become easier to track with the process. The data lineage process ensures users that the data originated from reliable sources has been transformed and placed at its designated location.

Therefore, data lineage helps to track data processes correctly, check the quality and consistency of data from source to destination, and identify and correct anomalies.

Thus, data lineage helps companies to:

·        Analyze data processing errors

·        Ensure a smooth system migration

·        Reduce risk associated with process changes

·        Utilize and integrate metadata to map data and discover new insights

Why is Data Lineage Important?

As cloud-based data streams continue to increase, users need access to data that is easy to understand and simple to integrate for business intelligence. Businesses can improve product lifecycles by understanding the ETL (extract, transform, load), documents, reports, and databases that a data lifecycle provides. Data lineage supplements this information. Data curators can ensure the integrity and confidentiality of data at any point in its lifecycle. As data lineage is a critical component of data governance, let’s dig deeper to understand its importance.

Sustaining Regulatory Compliance

As databases became more and more important, the threat of privacy and data manipulation increased, which contributed to decreased data security. As a result, several countries adopted compliance laws. Most of these regulatory laws require companies to disclose and specify the data sources that supply them. Today, organizations must implement data lineage to be on good terms with government regulators.

For example, traceability between analysis and source data is required by the ADaM standard in medicinal clinical trials. Similarly, California Consumer Privacy Act (CCPA), General Data Protection Regulation (GDPR), and Personal Informational Protection and Electronic Documents Act (PIPEDA) encourage organizations to implement data governance and data lineage for tracking sensitive and personal data. Data lineage helps organizations to view how sensitive data flows throughout the business so that organizations have checkpoints at required places. Hence, this is one of the significant reasons data lineage gained massive importance.

Data Visibility

When large organizations use various mediums to communicate with data sources, isolating the stage at which that prospect lies in a system often becomes difficult. Data lineage tools help data scientists to consolidate and arrange data efficiently. Moreover, data lineage tools ease information accessibility by providing the data scientists with a visual flow.

It is not only the data scientists who benefit from data lineage. Data lineage can allow ETL developers to track errors within the ETL job. It also allows checking for any modifications in the data fields. This kind of impact analysis is beneficial when dealing with complex reports because it helps identify the data source that should be used in that report. Data stewards also benefit from recognizing the data sets used or not used frequently.

Reduced Effort on Data Processing

Almost all the departments in an organization require data. As data transforms over time, it should be valuable enough for departments to decide on sales or products. Due to growth in datasets, data processing becomes problematic and time-consuming in a traditional data warehousing model.

Data lineage is helpful when companies wish to analyze sales information to try a new product or process. It gives a clear picture of where the data came from and how it has traveled through the system and reduces risk.

Data lineage procedures maintain a track of the dataset from its very sources. This system makes it easier to sort out and classify datasets that can be reconciled to utilize new and old datasets. Thus, data processing becomes uncomplicated and streamlined with data lineage.

Data Processing Transparency

Big data can be hard to process, extract, and transform, but data lineage systems are designed to handle it. They can easily be implemented and scalable in a very short time. Data lineage systems can also provide data analysts with insights and forecasts to prevent database chokepoints. This system can help IT operations better understand the effect of data changes on downstream analytics and applications and handle changes more efficiently. Data lineage is also essential in the model-ops and machine learning life cycle. It can determine when it is necessary to retrain models and reduce drift when sufficiently new or changed data have been collected.

Conclusion

The above are some reasons for implementing data lineage in your organization. Understanding and using data lineage can be complicated, but organizations must use data efficiently. Both business and the IT operations team can benefit as it can help them do their jobs efficiently and focus on strategic plans. As enterprise data increases, more regulations are being implemented. Therefore, companies will need to focus on data governance and data lineage.