About Data lineage

Data lineage is the practice of keeping track of where certain pieces of information have been and where they’re going within an organisation. A company’s data lineage is its documented, chronological account of where each piece of information originated and where it was used. Data lineage tools make it much easier to discover where data has been and where it has been stored. This method investigates the origins of the data, the history of any changes made to it, and the identities and motivations of everyone using the data. It answers the “What,” “Where,” “Who,” and “Why” questions about the origin, transformation, and consumption of the data within your organisation. With this added contextual information, security teams may better protect sensitive data from being accessed by malicious actors or misused by authorised users.

Traditional data protection technology classifies data that is sensitive by matching patterns in the content. Thus, like regular expressions and keywords, user-applied tags and fingerprinting, which cover a limited range of data types. Data lineage is an entirely new way to classify sensitive data that classifies more data types while reducing false positives. It has substantial implications for improving how companies identify, investigate, and report on data security risk and incidents.

This article will highlight real-world use cases that show the application of data lineage in business.

Elements Of Data lineage

Data lineage is a graphical representation of the entire data lifecycle, from collection to final use. Compliance with rules, data quality, and data security are guaranteed when organisations can monitor, analyse, and audit the data flow. Data lineage typically consists of the following elements:

  • Data Sources: Databases, files, application programming interfaces, and other places where data is stored are common places to begin tracing its history.
  • Data Transformation: During its journey through the various processing steps, data may undergo several changes, including cleansing, aggregation, and enrichment.
  • Data Movement: Information can be transferred between computer networks, workplaces, and countries.
  • Data Consumers: The final node in a data pipeline typically consists of software, reports, or analytics used to make decisions based on the data.

Applicable Use Cases For Data lineage

The following are applicable use cases for data lineage:

  • Regulatory Compliance: Companies in highly regulated sectors, like healthcare and financial industries, must follow stringent data governance rules. These organisations can use data lineage to monitor private information collection, storage, and dissemination.
  • Data Quality Administration: Maintaining high data quality is an ongoing struggle for companies. Tracing back data usage helps pinpoint the source of poor data quality. For instance, data lineage can be used to identify the origin of inaccurate product prices in an online store’s catalogue.
  • IT Maintenance and Repairs: Complex data infrastructure is a common issue for IT teams to tackle. It can be extremely helpful for diagnosing system performance or data pipeline problems.
  • Analytics and Business Intelligence: Data lineage is essential in analytics and business intelligence. Analysts and data scientists must have a deep and thorough understanding of the data they use. Data discovery is aided by knowing the history of the data, and it is more likely that conclusions drawn from the data are correct.
  • Data Access Management and Protection: Organisations place a premium on keeping private information safe. Access control mechanisms can be set up and kept in working order using data lineage.
  • Update and Transfer of Data: It can be used as a compass when businesses update or switch to new data platforms or data infrastructure. It lays out a plan for updating and modernising information systems without jeopardising existing data or disrupting operations.
  • Impact Analysis: Companies seek to know how a change in data or system architecture will affect subsequent steps in the business process. Impact analysis is made possible through data lineage, which aids firms in making upgrades and changes with minimal downtime.

Conclusion

Hence, data lineage is more than just a notion; it’s a tool that helps businesses maximise the value of their data while minimising potential threats. It enables compliance, quality management, troubleshooting, analytics, security, and more by giving transparency and traceability to the data’s journey. Data lineage will continue to be an invaluable resource for businesses in the digital era. Thus, for want to make data-driven decisions and stay ahead of the competition. It is essential to guarantee data accuracy and is a staple tool in fields where reliable information is essential for making important choices. With the right tools and procedures, keeping tabs on information may be quick and easy.

About the Author:  Mosopefoluwa is a certified Cybersecurity Analyst and Technical writer. She worked as a Security Operations Center (SOC) Analyst, creating relevant cybersecurity content for organizations and spreading security awareness. Volunteering as an Opportunities and Resources Writer with a Nigerian based NGO she curated weekly opportunities for women. She is also a regular writer at Bora.
Her other interests are law, volunteering and women’s rights. In her free time, she enjoys spending time at the beach, watching movies or burying herself in a book.
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