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Data Lineage – Visualizing Complex Enterprise Data

Posted by: Vishal Saxena On January 21, 2015 11:05 AM

Enterprise data typically doubles every four years. Along with the explosion in volume, there is a huge increase in the complexities through which today’s Enterprise data passes during its lifecycle. There are multiple sources, structures, transformations and movements that data has to go through at various stages. As business complexities increase, so does the associated data- dependency and business risk making even a small error in data reconciliation across various stages have a snowballing effect on the business outcomes. To make matters worse, at times it is extremely difficult to pinpoint the stage, or action causing the error.

Data Lineage

Data lineage helps to track a data from its origin to its destination. Data lineage methodology works like a snapshot of data flow in an organization. It captures information about data from source to destination along with the various processes and rules involved showing how the data is used along its journey. This knowledge about data can be helpful for a better data governance process, data quality, master data management and overall metadata management. Data Lineage helps in knowing the original source of the data, and understanding what happens to the data as it finally flows into a report. This helps in better management of data, improving the overall business intelligence infrastructure and ultimately to better decision making.

In simple terms, Data Lineage answers questions like, “Where did the data come from?” or “How did we arrive at this number?” etc. The solution to data integrity, uniformity and correctness is matured Data Governance. And the first step to achieving it is to get a visual on the existing Data Flow and Data Lineage.

Data lineage can also be used to safeguard data and reducing risk. The large amount of data collected by organizations also exposes them to legal and business liabilities should the data be compromised in the event of any possible security breach. Data lineage techniques can be useful in identifying potential vulnerabilities at various stages and help data managers take necessary corrective measures proactively.

How It Works

Data Lineage works by tracing the data path upstream from a target system or file or report. It tries to track all the sources and expressions that might have influenced the data during its course and can help in explaining its final value. The most important value that Data lineage systems provide is the visual depiction of complex data paths that often traverse cubes and database views, ETL processes, intermediate staging tables, shells and FTP scripts, and even legacy systems on the mainframe. When presented in a visual format, this complex lineage can be understood by both business and technical users in a much better way enabling faster and better decision-making. The various tools and technologies available today allow for a summary of the data lineage to be presented visually with options to drill down into specific sections for better understanding.

Importance of Metadata Management in Data Lineage

One of the most crucial inputs to effectively capture and showcase Data Lineage is Metadata Management. It consists of metadata collection, integration, usage and repository maintenance. Metadata Management captures enterprise data flow and explains data lineage through the Metadata Abstraction layer. Though the metadata captured is not specific to any particular ETL system flow, it is recommended to have a metadata repository for all the data that flows from Source to Target.

Benefits of a Good Data Lineage Solution

A good Data Lineage solution helps by enabling the path that a data takes from its origin to its current or final destination to be observed through a visual representation. It helps in better understanding the different operations that are performed on data at different stages, and helps in better understanding their impact. Some of the salient benefits of a Data Lineage Solution are as listed below:

  • An end-to-end view facilitates easy diagnosis of business rules discrepancy and data completeness issues
  • An end-to-end view improves Data Compliance by giving a holistic view of data
  • Data Lineage clearly outlines data accessibility and can identify access vulnerabilities and help in avoiding data security breaches
  • Helps in identifying redundant data flows and reporting systems and makes the systems more efficient
  • Helps Data Stewards make quick decisions and react to business issues proactively instead of reactively
  • Ensures introduction of new systems is properly controlled and complies with the various Data Governance criteria, as well as effective reuse of existing information
  • Helps in defining Data Quality Improvement Strategies

Tags: Data Management Services, Thought Leadership
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