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Data Catalogs and Metadata Management

Data Lineage

Comprehensive visibility into data flow and transformation is the cornerstone of innovative data architecture, providing the visibility and traceability needed throughout the data lifecycle. Our customers build a robust, reliable, and flexible data ecosystem by integrating the data path with data management practices.
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How to implement Data Lineage in enterprise organizations?

C&F considers data lineage to be a key element of the data catalog, providing information about the origin of the data and its flow through all stages to its destination. It helps to understand where the data comes from, any data transformations, and who is using it. Identifies the origin of attributes in a table or API and how they were calculated. Provides information about the impact of changes to source systems or even a specific target table in the data product. It is a source of data observability and data quality information about problems with the data in the source system or data product. Finally, it enables the tracking of the most critical data for business operations to ensure continuity, consistency, and appropriate quality.

Ensuring data quality and consistency

Highlight the benefits of tracking all the transformations that data undergoes from source to final form. By understanding these transformations, you can ensure that data remains accurate and consistent across systems and applications. The ability to trace data lineage to the root cause of data problems is a significant advantage. It allows you to quickly identify and correct data quality issues, ensuring the integrity of your analytical and reporting processes.

Accelerating impact analysis

Map data relationships between systems and processes to understand the impact of changes to data sources, transformations, or targets to anticipate and mitigate potential disruptions. When you implement changes to data systems or processes, use data lineage to predict the impact of those changes and minimize implementation risk.

Increasing operational efficiency

Visualize data flows to identify inefficiencies and bottlenecks in data processing. This helps you identify opportunities for optimization to improve data processing performance and reduce latency. By providing a clear map of data movement and transformation, you can simplify the data integration process, enabling smoother and more efficient data integration.

Enabling advanced analytics and machine learning

Ensure feature traceability for machine learning models, as it is critical to understand their origins and transformations. Track data provenance throughout the data lifecycle to improve model transparency and reliability. Accurate data provenance helps you ensure that training data for machine learning models is correctly sourced and processed. This proof is essential for building reliable and unbiased models.

I have been involved in the implementation of data lineage in enterprise organizations for many years. During my projects, I have encountered several recurring issues that we at C&F have developed a comprehensive approach to. The first is the complexity of data environments, so we use advanced data lineage tools that support a wide range of data sources and formats. At the same time, we implement a data catalog to provide an inventory of data assets and their origins. A classic problem we encounter is inconsistent data definitions and standards. To resolve this, we create data dictionaries and ensure that all systems adhere to these standards.

Many of our projects start with a lack of automation tools, so we use technologies such as Informatica, Collibra, and Alation that can continuously capture and update data lineage information. Where necessary, we develop custom scripts using APIs to automate the extraction and recording of data lineage information from systems that do not support it natively. Integration with legacy systems is typically required. We use custom connectors to bridge the gap between legacy systems and modern data lineage tools to facilitate the extraction of the necessary metadata. Finally, there are scalability issues, which we address by using microservices architecture and cloud-native solutions to ensure that the system can grow with your data needs.

We also implement data partitioning strategies to manage large volumes more efficiently and ensure that data provenance information is handled in manageable chunks. A final concern is the high cost of implementation, so we focus on the most critical areas of data lineage first to ensure a high return on investment, which helps to demonstrate value and secure further funding. Where possible, we also use open source tools and frameworks that we customize.

Overview

Data lineage shows the complete flow of data, including where it originated, how it has been transformed, and where it’s moving in the data pipeline. By providing a full record of data flow, from data sources to data transformations, data lineage tools make it easy to track errors, ensure data is coming from a trusted source, and make strategic decisions based on accurate information. Without using a data lineage tool to track data processes, it can be difficult to see how data elements have been changed and verify if data is reliable. Our Data Lineage solutions help your enterprise maintain data integrity, quality, and consistency, improve operational efficiency, and ensure data used for analytics and machine learning is reliable and unbiased.

Experts in the field

Krzysztof Białkowski
Senior Solutions Architect/PGS UDH Team Leader
Go to Expert Spotlight
Michał Majkowski
Principal Software Architect
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Piotr Drozd
Senior Director
Go to Expert Spotlight

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