Data Lineage

Data Lineage

Is the lack of Data Lineage causing your company to lose full-spectrum visibility?

This client is part of a larger financial corporation with banking divisions in all 50 states. They provide commercial banking solutions from lending to leasing and mergers and acquisitions.

Like most companies in the financial industry, the bank was faced with strict regulations to follow, making data lineage even more important. In such a fast-paced environment with strict rules and regulations, it can be easy to lose visibility of the full picture of a data landscape.

Visualize Data Flows Create Consistency with Business Terminology Root Cause Analysis

Visualize Data Flows For Operational Intelligence

Create Consistency With Business Terminology

Perform Root Cause Analysis To Find Data Errors


The first challenge was driven by the client's growing data pipelines, it became nearly impossible to quickly assess the data's demographics and determine which data sources were providing the greatest downstream effects. The second was strict regulatory control,  put in place after the 2008 financial crisis and GDPR placed a larger focus on compliance. In addition, two previous mergers had resulted in multiple business terms holding as many as 5 definitions across all departments.  


The client needed to quickly overcome those challenges and implement a data lineage strategy that delivered:
  • Complete operational intelligence.
  • Reports that all stakeholders, from customers to auditors, could trust to properly reflect the data.
  • Defined data glossary. 


The bank needed a solution that could “Create a world-class process and strategy to automate the data forensics and resolve regulatory requirements across the organization.” Approaches explored included mainframe testing, a distributed environment and migrations, and conversions. The bank’s IA team executed the provided solution from the tech firm to reach the “speed of results and overarching ramifications” required to meet its goal.


By achieving data lineage, the company significantly reduced the costs of application usage from $1,200,000 to $150,000. In addition, the company saw a direct correlation to faster project resolution. What once took 10,000 manual hours to complete certain projects saw a significant reduction to around 100 hours.

Data lineage saved the bank time, resources, and increased efficiency. By sourcing lineage from different tools, it was easy to piece together different elements with various bridges. Having every employee understand the data – not just the IT or data science departments- is a critical step to faster decision-making.



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