Data Lineage Tools: Locate Data Errors and Optimize Data Governance Strategies

The essence of data governance is to help companies create data policies and ensure that people can comply with those policies. These policies address a range of data-related processes, including guidelines for data protection, verification and use. Data stewards must solicit data requirements from business users and work with data governance council members to agree on common data definitions, specify data quality metrics, articulate relevant policies, and develop methods to measure compliance.

Data Lineage Tools

Data Lineage Tools

However, building a bridge between defining data governance policies and implementing them is often a formidable challenge. The purpose of these strategies is to control and monitor the quality of data assets across business workflows, but data stewards with key data quality management responsibilities are often not properly trained or qualified.

This is where the data lineage tools come in. Metadata-based data lineage information documents the journey of a data object through an organization’s systems. Lineage records can help data analysts and other end users understand the data they work with, but it also simplifies two key data governance processes: analyzing the root cause of data quality issues and the impact of changes to datasets in source systems.

Data Lineage and Data Governance

Without knowing where in the data management environment data errors have been introduced, it can be difficult for data stewards and data quality analysts to identify and fix them. The consequence of this: If data flaws continue to propagate through the system, organizations can often be plagued by inconsistent or inaccurate analysis and reporting, leading to bad decisions in business operations.

During root cause analysis, data lineage tools provide visibility into the sequence of processing stages—through the data flow being examined. The quality of data can be checked at each stage, enabling data governance and data quality teams to find where data errors are occurring.

From where the error was first discovered, data stewards can insert controls at an earlier point to monitor whether the data meets the expectations defined at the time or whether errors have occurred. By determining at which stage of processing data comes in compliant but exits flawed, data stewards and other staff involved in the data governance program can focus on eliminating root causes rather than just correcting erroneous data.

Data lineage tools can also help data stewards perform impact analysis to understand problems caused by changes to the format and structure of source data in a data management environment, which is generally more dynamic than in the past.

When source data changes, there can be unintended consequences downstream. By working forward from the point of view of data creation or collection, data stewards can rely on data lineage documents to help track data dependencies and identify processing stages affected by data changes. This enables data governance and data management teams to redesign affected stages to accommodate changes and ensure data remains consistent across different systems.

How to choose data lineage tools?

Manually collecting metadata and documenting data lineage requires a significant investment of resources and is prone to errors, which can be a big problem, especially in organizations that rely on data analytics to drive business operations. Therefore, data governance requires finding tools that can manage the lineage representation of data and automatically map them across the enterprise. During your technology assessment, you should look for data lineage tools that meet these requirements:

  1. Able to natively access numerous data sources and data products, investigate the metadata they contain, and collect metadata for data governance use.
  2. Able to aggregate captured metadata into a centralized repository.
  3. Able to infer data types and match common usages of referenced data with data elements from different systems.
  4. Able to provide a simplified representation of aggregated metadata to various end users and to support collaboration to validate metadata descriptions.
  5. Able to record an end-to-end mapping of how data flows through an organization’s processing streams.
  6. Able to generate visual representations of data lineage.
  7. Contain APIs for developers to use when building applications that can query lineage records.
  8. Able to create inverted indexes that map data element names to their use cases at different stages of processing.
  9. Provide a search function that can quickly trace the flow of data, from origin to downstream destination.
  10. Enable users to monitor data flow forward and backward.

Data Lineage Products:

There are many products on the market today to choose from. Data management platforms sold by major IT vendors, including IBM, Oracle, SAP, and SAS Institute, often include tools for documenting and managing data lineage. Smaller software vendors focused on data integration, quality, and governance also offer corresponding offerings. Additionally, data lineage capabilities are sometimes built into data catalog software, as are vendors of various BI and analytics tools.

If you’re looking for data lineage tools for your organization, why not give Gudu SQLFlow a try. As one of the best data lineage tools available on the market today, Gudu SQLFlow can not only analyze SQL script files, obtain data lineage, and perform visual display, but also allow users to provide data lineage in CSV format and perform visual display. (Published by Ryan on Jul 22, 2022)

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