In today’s data-driven world, the ability to trust and understand data is paramount. Modern data environments are characterised by their distribution, diversity, and dynamic nature, making data lineage a critical component for ensuring data trust and reliability. This blog will summarise the key points from the document “Modern Data Lineage: Increasing Trust in Data and AI Outcomes” and explore the importance of data lineage, its benefits, and practical use cases.
The Importance of Data Lineage
Data lineage refers to the tracking of data’s origin, movement, and transformation throughout its lifecycle. It is essential for several reasons:
- Trust and Transparency: Knowing the lineage of data helps organisations trust their data by providing transparency about its origins and transformations.
- Regulatory Compliance: Data lineage is crucial for regulatory reporting, ensuring that organisations can trace sensitive information and comply with data protection regulations.
- Troubleshooting and Impact Analysis: Understanding data lineage aids in identifying the root causes of data issues and assessing the impact of changes in data structures or processes.
Benefits of Modern Data Lineage Solutions
Modern data lineage solutions offer several advantages that address the challenges of managing complex data environments:
- Automation: These solutions automate the capture of data lineage, providing an up-to-date and accurate view of data origins, transformations, and flows.
- Code-Level Lineage: Unlike traditional methods, modern solutions capture lineage from application code, enabling tracking across various environments, including legacy systems and data lakes.
- Visualization and Impact Analysis: Visualisation tools help users understand complex data relationships, while impact analysis features allow organisations to assess the effects of changes proactively.
- Interoperability: Modern solutions support open standards and integrate seamlessly with other data intelligence platforms, enhancing overall data management practices.
Use Cases of Data Lineage
Data lineage has numerous practical applications across different industries:
- Financial Services: Ensuring compliance with regulations like GDPR and tracking the flow of sensitive financial data.
- Healthcare: Managing patient data and ensuring compliance with health data regulations.
- Retail: Optimising supply chain management by understanding data flows from suppliers to consumers.
- AI and Machine Learning: Enhancing the accuracy and reliability of AI models by providing transparent lineage information.
So a lot of this is fine in theory but how do you go about implementing lineage in a business?
Implementing data lineage in a business involves several key steps to ensure data transparency, trust, and compliance. Here is an outlined structured approach to help you get started:
- Identify the potential Data Sources and Destinations
- Catalog all data sources: Include databases, data warehouses, applications, and external data feeds.
- Map data destinations: Identify where data is stored, processed, and consumed.
2. Consider the Data Flow
- Document data movement: Track how data flows from source to destination, including all transformations and processes it undergoes.
- Create data flow diagrams: Visual representations help in understanding complex data relationships.
3. Consider Data Lineage Tools
- Select appropriate tools: Choose tools that automate lineage capture and provide visualisation capabilities. Examples include IBM’s Manta (our preferred solution) but likes of Apache Atlas, Collibra, Alation and Informatica have gained some traction.
- Integrate with existing systems: Ensure the tools can seamlessly integrate with your current data infrastructure. We can help advise here if you are unsure of the steps
4. Try to Establish Metadata Management procedure or policy
- Centralise the metadata: Maintain a centralised repository for metadata to ensure consistency and accessibility.
- Automate metadata capture: Use tools that automatically capture and update metadata as data flows through the system.
5. Implement Data Governance Policies
- Define governance policies: Establish clear policies for data management, including data quality, security, and compliance.
- Assign roles and responsibilities: Designate data stewards and governance teams to oversee data lineage and ensure adherence to policies.
6. Visualise Data Lineage
- Use visualisation tools: Leverage tools that provide graphical representations of data lineage to make it easier to understand and analyse.
- Perform impact analysis: Assess the potential impact of changes in data processes on downstream systems and users.
7. Monitor and Iterate
- Continuous monitoring: Regularly monitor data lineage to ensure accuracy and address any issues promptly.
- Iterate and improve: Continuously refine your data lineage processes based on feedback and evolving business needs.
Given there are a number of tools out on the market IBM had their own tools, but partnered with Manta (see IDC review**) and then acquired them some 12-28 months ago so really worth looking at should this subject matter be of interest to you and your organisations business.
Some of IBM’s Manta Lineage solution offers several key benefits that can significantly enhance your DATA management and governance within the business:
- Complete Visibility: Manta provides comprehensive visibility into data flows through automated lineage mapping, helping organisations understand the origins, transformations, and destinations of their data
- Enhanced Data Trust: By identifying potential risks and supporting compliance, Manta ensures data integrity and trust across all systems, which is crucial for making informed business decisions
- Improved Efficiency: The solution reduces manual effort and significantly decreases the time spent on impact analysis by automating the process of collecting and illustrating data interconnections
- Scalability and Granularity: Manta handles large data volumes and intricate transformations, ensuring scalability and detailed insights even in complex, large-scale environments
- Integration and Flexibility: With support for over 50 technologies, programming languages, databases, and modelling tools, Manta offers flexibility and adaptability, making it suitable for diverse data ecosystems
- Regulatory Compliance: Manta helps organisations comply with regulatory requirements by providing detailed audit trails and documentation of data processes, which is essential for data protection regulations
Additionally, IBM’s Manta Lineage solution integrates with various tools to provide comprehensive data lineage and metadata management. IBM’s Manta achieves seamless integration by:
1. Out-of-the-Box (OOTB) Connectors
IBM’s Manta offers OOTB connectors to several third-party systems, allowing for easy export and import of metadata. These connectors support tools like:
- Alation
- Collibra Data Governance Center (DGC)
- IBM Knowledge Catalog (IKC)
- Informatica Enterprise Data Catalog (EDC)
- Plus many, many more
2. API Integration
IBM’s Manta provides robust APIs that enable integration with various data management and governance tools. This allows organisations to embed Manta’s lineage capabilities directly into their workflows and CI/CD pipelines
3. Custom Integrations
For systems not supported by OOTB connectors, IBM’s Manta offers a generic export format. This format generates CSV files containing the exported metadata, which can then be processed and integrated into other systems as needed
4. Comprehensive Data Source Support
Manta supports a wide range of data sources, ETL tools, and BI platforms, including:
- Databases: Oracle, PostgreSQL, MS SQL, etc.
- ETL Tools: Talend, DataStage, SSIS, etc.
- BI Tools: Tableau, Power BI, Qlik Sense, and many more leading tools in the marketplace
5. Column-Level Lineage
IBM’s Manta extracts detailed column-level lineage from various sources, providing a granular view of data transformations and flows. This level of detail is crucial for accurate impact analysis and data governance
To read more details simply go to these links on our website or contact us via our contact page;
https://www.smallnetconsulting.co.uk/contact-us/
OR
https://www.smallnetconsulting.co.uk/idc-spotlight-on-data-lineage-2/ (**IDC Review)
In conclusion, modern data lineage solutions are indispensable for organisations who are aiming to achieve trusted data and reliable AI outcomes. By automating lineage capture, providing detailed insights, and supporting regulatory compliance, these solutions empower organisations to make informed decisions and drive greater business success.
