Unlock Trust and Transparency with Data Lineage

By Michael Meyer

Published on March 11, 2024

Two people shaking hands

When it comes to unlocking the potential of data, stakeholders require visibility on how data flows from sources, how it is transformed, and where it is used. This is critical from a data management and governance point of view. Lineage provides a deep level of transparency so that people can gain trust in the data.

Without data lineage, organizations cannot understand where sensitive information travels across data systems, how critical data elements are sourced, when data quality crosses a critical threshold, the downstream impact of changing a source table, and other important data management activities.

Every person in an organization can find valuable information from lineage. Data governance leads, business analysts, and all other job roles can benefit from the right level of context presented to them visually. Technical roles will benefit from column-level mapping and transformations when solving issues. Business roles need less technical details but must understand the relationship between assets, including crucial items such as data health and classifications like whether something contains a critical data element.

Lineage unlocks several benefits, such as maturing data processes, increasing transparency for trusting data, and reducing cost in areas such as BI rationalization and root cause analysis time.

Maturing data processes

Companies must mature their data processes to innovate faster in today's AI world. Lineage is no longer a nice-to-have item but an essential part of improving data governance, detecting business process issues, and reducing the time spent on arduous activities like audits.

Improving data governance

A crucial governance area is understanding how sensitive data moves throughout the organization and its use. The ability to track sensitive information allows governance teams to ensure the proper policies and procedures are followed. Governance leads and others can use classification metadata overlays in a business lineage view to the movement and the use of this data. If there is any non-compliance use, it can be addressed quickly with the stewards of that data.

Detecting business process issues

Business process issues often present themselves through the data that the process produces. For example, as a global firm, you notice in several different financial reports that the numbers are out of balance. There are discrepancies that you can’t explain. What could the issue be? Are the calculations suddenly incorrect? That would seem suspicious since the numbers were correct for the past few months.

Business and technical employees alike can use lineage to trace the reports with all the data assets used to produce them. In this investigation, lineage showed that two exchange rate sources were used, causing the differences in the reports. Now, IT and the governance team can work together to standardize one source for rate exchange and show the proper way to use it.

Reducing the time spent on audits

For CDOs and other data managers, there can be anxious moments when it is time for audits. There is nothing like sitting in a room being questioned about how this critical number, like net income, is being calculated and where the source of the information is coming from. No one wants to wait patiently for a Data Engineer to dig through source code to show the requested information. Lineage provides transparency to auditors and saves hours of time.

Increasing transparency for trusting data

For individuals in organizations to make data-driven decisions, they must be able to trust the data. There are various means to enhance trust, such as others endorsing the use of a data asset. Lineage can help by providing data maps for all, showing data quality, and tracing the data journey of critical KPIs and metrics. For example, GXS Bank relies on Alation’s lineage capabilities to trace back source tables and understand downstream impact to enable users to understand better and discover their data assets.

Providing data maps for all

Lineage is the essential way that everyone in an organization can better understand the data in a visual context that is meaningful to them. Business people need to understand concisely the flow of data that delivers information such as data health, policies assigned, and other business metadata that enhances trust. Technical people can benefit from having column-level data mappings, transformation details, and impact analysis to proactively address downstream impacts on data assets when making changes.

Showing data quality

Knowing the health of the data provides a deeper level of trust. Visualizing the health in the lineage is highly impactful to see where data quality issues are occurring and the impact on other assets such as dashboards and reports. Visual indication must provide the ability to drill into the data asset and see what data quality rules are crossing critical thresholds. Data stewards can then begin to address the issues so that the data can return to a trusted status.

Tracing the data journey of critical KPIs and metrics

Transparency to KPI and metrics definitions aligns everyone in the organization with the intended purpose and use. Glossary terms help to provide the narrative and also the calculations. Business analysts often need a deeper understanding and look to lineage to track vital metrics to the data sources where the metrics originate. The journey's starting point helps to trace the data flow for the metric to understand any transformations before final consumption to ensure alignment with the metric definition.

Reducing costs

Companies are looking to gain the benefits of a data culture, which includes a positive data return on investment. A critical part of this is understanding your data landscape, which provides for data sources, systems, and processes. Lineage can assist in eliminating waste, preventing costly outages, and decreasing root cause analysis time.

Eliminating waste

A quick win for companies is to look for systems that serve a duplicate purpose. One such area is to take the time to go through the process of BI rationalization. BI rationalization consolidates and optimizes an organization’s business intelligence (BI) systems. The average number of BI solutions is 3.81. In addition to reducing the number of BI solutions, lineage can help detect duplicate reports to save development and maintenance costs.

Preventing costly outages

Lineage helps data producers proactively use impact analysis to identify crucial reporting assets and collaborate with the owners to prevent service disruption when making changes to data assets. In addition to preventing downtime, lineage is essential during the planning and execution of cloud migration. Knowing the critical data assets and relationships makes understanding all the dependencies between the assets and reports easier.

Decreasing root cause analysis time

When issues arise, it is vital to have the means to diagnose and find the root cause promptly. Data engineers can save time using lineage to find process and quality errors in data pipelines quickly. Data engineers can spend more time fixing the issues and ensuring trusted data is delivered.


Alation Data Lineage enables organizations to trust their data by capturing how the data flows and relationships to have complete transparency of critical items such as quality and the handling of sensitive data. In addition, lineage enhances companies’ ability to mature their data and business processes while reducing costs.

To truly serve everyone in the organization, it is essential to be able to view lineage information in the context of what each individual needs. Alation achieves this by providing technical lineage (table-level, column-level, transformations, impact analysis) and business lineage (data quality, trust check flags, business metadata overlays).

Seeing is believing – for more information, schedule a demo today!

1. Source: https://explodingtopics.com/blog/bi-stats

  • Maturing data processes
  • Increasing transparency for trusting data
  • Reducing costs
  • Conclusion
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