Data Lineage: Why Every Data Management System Needs It

What is data lineage?

Data Lineage documents the flow of data through an organization, visualizing its journey and how it changes through each part of it. Important metadata information is used to determine its history, its future direction, and everything in between. As data lineage is used for tracking data from beginning to end, it is an essential tool for all data users, be they data scientists, stewards, analysts, etc.

What exactly does data lineage do?

Data lineage tracks the flow of data, every kind of process it goes through, and every interaction it makes. If a particular piece of data has been used to serve a specific purpose, this will be logged within the data flow cycle. Every process and change is logged and recorded, with the goal being total transparency for all those that use it.

A summary of how data lineage works can be broken down into three main ways of recording information about data:

– Any transformations the data went through

– How these transformations occurred

-The reasons for these transformations

Data lineage can bolster your data governance.

Implementing a data lineage solution within your company’s data governance strategy can provide numerous benefits.

Firstly, it provides sufficient information to data managers to confirm data validity, reliability, and cleanliness. As the data’s past has been fully logged, a level of clarity enables governance staff to make well-informed decisions on the data utilized.

Another essential aspect of data lineage is that it ensures that all data being used complies with regulatory requirements, both in and out of the organization. It provides users with everything they need to prove that the data satisfies all the necessary criteria and has not been corrupted. Additionally, if it is ‘bad’ data, lineage effectively roots it out and displays this information.

Boosts any data migration strategy

Data migration involves the transition from old to new data storage and/or software systems. Data lineage helps tremendously in this respect, too, as the detailed history of data’s journey through different sources will directly influence its suitability for future migrations.

Yield greater analytics & BI

Another positive outcome of data lineage that cannot be overlooked is how data analysts and scientists can utilize it. Lineage enables users to locate the most appropriate datasets for analysis by providing ample background information on all potential data incomings. Facilitating the cherry-picking of good data means a greater chance of yielding positive data analytics results and BI, resulting in better business decisions.

Reinforces data security

Thanks to the data lineage’s profound and far-reaching mapping of all data and everything about it, the scope of data covered ultimately helps organizations’ data security measures. By widening the lens in this way, data users can quickly and effectively pinpoint the most sensitive data that needs to be managed more securely. It also helps users develop sounder risk management protocols for all incoming data.

Conveniently reliable

All departments within an organization are heavily dependent on good data. With data lineage, staff can retain complete confidence in their data. Data lineage covers all tracks, enabling staff to make decisions quickly and driving efficiency in strategic operations. This coverage invariably leads to a consistent understanding of data validity across marketing, sales, etc.

Different types of data lineage

Pattern-based lineage

Pattern-based lineage is a technique for evaluating metadata (columns, tables, etc.). It involves looking for any patterns and relationships that may be present between datasets. For example, if there is a column named “Total Expenses” in multiple tables with very similar data values, these are likely the same data at two different periods in the same flow. These relationships are then displayed in a lineage chart.

Data tagging

Another technique, data tagging, involves the examination of metadata to add tags that describe its properties and characteristics. This process can be automated by utilizing data lineage tools.

Data parsing

This complex technique automatically reads data algorithms to track the data end-to-end, offering a categorical, definitive result. Parsing is the most accurate form of data lineage as it traces data every step of the way.

Data lineage’s multiple benefits

Incorporating data lineage will bring many improvements to any data management system. Whether bolstering a data governance strategy or improving BI and analytics, many organizations see it as an indispensable addition.