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4 Examples of Data Lineage

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Data lineage is metadata that explains where data came from and how it was calculated. In a big data environment, such information can be difficult to research manually as data may flow across a large number of systems. As such, organizations may deploy processes and technology to capture and visualize data lineage. The following examples illustrate how data lineage may be useful.


A unit in a bank purchases a large number of shares in a company before it is acquired by a larger firm. A regulatory body launches a preliminary investigation and contacts the firm. The firm states that a trader purchased the shares because a proprietary algorithm suggested the stock was about to experience upward momentum. The regulator asks for an explanation of the data and calculations used to make the decision.

Risk Management

A credit team seeks executive approval to provide a large unsecured credit facility to a client. An executive questions the team's risk ranking on the client as she has heard bad things about the company in recent months. The team is given until the following morning to explain where the data for the client risk profile came from and to be ready to explain the calculations.


A fashion company produces sales forecasts for each season based on a large number of factors including historical trends, market conditions and product development research. An executive questions the forecast for women's shoes sales in Europe and asks how the number was calculated.


An organization publishes an industry statistic that is challenged in the media as being wrong. The team that generated the information is given hours to confirm their findings before the CEO of the firm is scheduled to attend a media event. The CEO needs to know if there is any chance at that the statistic is indeed flawed.
Overview: Data Lineage
Data Governance
The practice of collecting an audit trail for data sources and calculations and displaying such information in a format that is human readable.
A capability that supports decision making, risk management, compliance and data improvement.
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