What is Data Lineage and Why is it Important?
If you work with data, you’ve likely found yourself asking questions about where that data comes from and whether it’s accurate and up to date. Data lineage metrics is your roadmap that answers those questions, and protects data quality.
Why is that so important? Enterprise data is in constant motion. As soon as an organization creates or ingests a piece of information, it begins its journey.
As data moves through a variety of extractions and ingestion points, it’s manipulated and transformed to meet your varying business needs. And as the journey continues through different platforms, the data’s format, function, and integrity levels may change multiple times without any transparency and oversight.
To protect the quality of your data and provide an audit trail throughout its lifecycle, it’s essential to track, monitor, and apply governance standards to the data’s movement from beginning to end. Data lineage is the monitoring of that movement, providing your business with visibility and traceability of data and empowering trust in the information that’s leveraged for decision-making – resulting in powerful, actionable insights and new business opportunities.
However, documenting lineage requires organizations to look at different lineage perspectives to monitor data quality and encourage data utilization.
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Business data lineage vs. technical data lineage
Data has different meanings to different users. Identifying which school of thought an individual subscribes to regarding data lineage depends on that individual’s role and objectives in the organization. It ultimately boils down to two perspectives:
Who needs it:
Business users who are required to understand how data fits into the business, and the impact of making modifications to that data.
Let’s say a business user from the operations team is looking to update the format of the data. Before submitting a change request to IT, they need visibility into where the data is being used. Business data lineage enables them to quickly identify the reports, business processes, and metrics relying on that data, so they can understand the impact of the format change before moving forward.
Who needs it:
IT users who are required to quickly narrow down the cause of data quality issues and triage efficiently and effectively.
Technical data lineage captures data on the physical level, such as schemas, tables, columns, and how it moves across systems using ETL jobs, procedures, and transformation rules.
Each above perspective represents the “truth” of data lineage according to their own needs and objectives, which is why collaboration between the IT and business community is critical. Next, we’ll explore this in more detail.
How Different Data Users Can Benefit
In any business, different data users have diverging goals and priorities. Data lineage helps both IT-focused data analysts and business users company-wide accomplish various tasks.
For example, if you’re a data analyst working within an organization’s IT department, you may use data lineage to understand the multiple steps information takes throughout a data supply chain, across data lakes and other technical changes. This information helps demonstrate the impact regulatory or internal policies have on the data landscape.
By knowing the physical storage and movement of data, you’re able to quickly identify where sensitive information is located and how that data changed over time.
For business users, data lineage can be leveraged at a strategic level by providing the context of the data, enabling you to decipher its origins and flow:
- Where did the data came from?
- What processes did the data go through?
- How was data integrity ensured?
The value in knowing that you have clear and dependable data to generate reliable, trustworthy insights simply can’t be overstated – it’s your key to unlocking bigger, better business decisions.
Tracing data lineage through data governance
To produce quality, dependable business intelligence, organizations need to understand the origin of their information. To track and understand business and technical data lineage, organizations require a comprehensive, enterprise-wide data governance program.
Following data lineage from inception through consumption means you need an integrated data governance framework that incorporates data quality – then, you can utilize data governance to take inventory of all enterprise data assets by building a data catalog.
A data catalog provides transparency into the details of your organization’s data assets – including data definitions, synonyms, and key business attributes – so all users understand and utilize their data as an asset.
More importantly, a data catalog also documents data lineage, from origination and throughout the data supply chain. This gives both business and technical users a clear understanding of the flow, context, and dependencies of their data, enabling them to take full control.
By tracking data lineage metrics from varying perspectives, organizations establish data trust and empower users to leverage data as a valuable business asset.
Watch our free webinar Foundational Strategies for Trust in Big Data: Data Lineage and learn how Precisely helps to support teams in documenting and meeting the regulatory, compliance and data governance requirements of their critical applications and end-to-end data.