Understanding and Utilizing Enterprise Data with Smart Data Lineage

Understanding and Utilizing Enterprise Data with Smart Data Lineage

Modern IT landscape throughout industries and applications is filled with data and its potential uses. Our dependence on data has brought many challenges with handling data and one such major challenge is tracking the data lifecycle.

Every data point goes through a journey of inception, multiple changes, and movement while in use and in storage. As the complexity and size of data stores increase, governing the data becomes increasingly necessary. This is where the concept and practice of data lineage comes into play.

What is Data Lineage

Data lineage is the detailed record of data’s journey from origin, transformations, and movements through its lifecycle. It provides the ability to understand data sources, intermediate steps while data is in use and the processes involved in those steps, and its storage history by capturing and documenting metadata.

Data lineages can be tracked in multiple ways like:

  • Source to Destination Tracking – a high-level view of data journey from source systems to final locations like data warehouses. It gives a general overview of data transformations and is useful for analyzing and improving processes like ETL (extract, transform, and load)/ELT and reporting.
  • End to End Tracking – a granular view that provides detailed information about transformations, processes, connections, and flow throughout the systems. It can be used in multiple forms of analytics and testing.
  • Dependency Mapping – a mapped view of data and its connection with other data elements, applications, processes, and systems. It is useful for conducting audits on data infrastructure and understanding the impact of changes in it.

There can be custom mapping and tracking methods for specialized needs depending on the use cases of data lineages. So, let’s take a look at the primary advantages of data lineage.

Key Areas Utilizing Data Lineage

Managing Operations with Smart Data Quality

Data lineage can be used to understand the transformation of data throughout its lifecycle and improve the overall quality of data by improving its processing. It can be used to debug systems and processes while reducing inconsistencies, errors, and gaps, increasing the overall reliability and accuracy of both data and applications.

Operations like infrastructure management, data integration, access management, data generation and capture, virtualization, test data management, etc., can utilize data lineages directly to improve performance and security.

Data Governance and Compliance

Compliance with regulatory practices and privacy laws can be a tricky and costly process. However, data lineages make it easier for organizations to analyze and, if required, demonstrate compliance through audits on data sourcing, processing, and uses.

Data lineages also help track security-related issues, incidents, and access by providing detailed information on data flows and data owners. Automated data lineage tracking can also enable monitoring tools for real-time governance, audits, and reporting.

Data Architecture and Lifecycle Management

One of the critical factors in IT infrastructure performance is its data architecture and integration ecosystem. Implementing the latest and emerging technologies like IoT, AI/ML, blockchain, etc. depends heavily on data architecture for efficient utilization of data. Data lineages also help document data journeys between old and new systems during migration. New Gen Data Hubs that effectively marries the industry leading frameworks like Data Mesh, Data Fabric needs effective and efficient Data Lineage for enabling various types of analytics.

Data lineages help perform various analyses on the data ecosystem from a bird’s eye to a granular level, helping organizations balance their efficiency with performance. Dependency mapping is one such analysis that can be used to understand the complete lifecycle of data elements and devise optimization strategies for storage and migration.

Machine Learning and AI

Advanced technologies like machine learning and AI are heavily dependent on data quality and are also responsible for generating and transforming data. Data lineage is also a helpful metadata required to train and improve AI/ML systems throughout their lifecycle. AI/ML also suffers from a black box design, thus capabilities enabled by data lineages help audit these systems to an extent as well.

AI/ML systems can also utilize data lineages to help perform analytics and optimization on the data ecosystem. Governance, monitoring, audits, and performance optimization can also utilize these systems for automation in real-time with data lineages.

SmartDataGovernance by SLK is our in-house solution designed to simplify enterprise data governance. It brings together capabilities like self-service metadata, lineage, profiling, quality checks, compliance checks, exploratory data analytics, cataloging capabilities and semantic analysis for a holistic data governance throughout the organization. Contact us to learn more about SmartDataGovernance.

Smart Lineage tracking as a scientific method has helped us understand and explore many avenues throughout history. Understanding enterprise data is one of the most critical commercial applications of lineage analysis which can help organizations achieve better performance, operational efficiency, and security.


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