A robust data culture is critical for modern businesses. With the ability to store, process, and analyze massive volumes of data, organizations can make informed decisions that guide their future product and service offerings. As companies try to get as much value as possible from the data they collect, they need to maintain its integrity and implement appropriate data access controls. Loading...Data lineage does exactly that; giving organizations visibility into their data flows across all systems so that they can optimize their data use and achieve the full value of their data.
What Is Data Lineage and Why Is It Important?
It is a set of processes and tools that track the data lifecycle and help data analysts understand, record, and visualize data flows. As a discipline within metadata management, data catalogs often feature its capabilities to help users understand the data’s context, including why the data was changed, who changed it and what changes they made.
More specifically, it enables data-driven businesses to:
- Develop citizen Loading...data science programs
- Establish key performance indicators (KPIs)
- Manage hybrid business intelligence (BI) environments
- Develop products, services and workflows that improve data quality
- Implement data governance programs and processes
- Support Loading...machine learning models
- Enhance data security
How Does Data Lineage Work?
The data lineage process relies on metadata (the data about data) to map and visualize data flows. Metadata tells users about data assets (like Extract, Transform, Load (ETL), data warehouse, and reporting tools) and the relationships between data assets (like dependencies between datasets and locations of specific data elements).
It works in two directions:
- The horizontal level, which gives high-level overviews of the way data flows between systems
- The vertical level, which shows column-to-column or column-to-report details
Primary techniques:
- Manual lineage: Mapping data flows in spreadsheets based on interviews with application owners, data integration specialists, data stewards and other data users
- Pattern-based lineage: Technology-agnostic evaluation of metadata across tables, columns, and business reports
- Lineage by data tagging: Tracking transformation engine tags or marks from start to finish
- Self-contained lineage: Using a data environment that provides storage, processing logic, and master data management (MDM) that provides lineage for all data within the controlled environment
- Lineage by parsing: Reverse engineering the data transformation logic for comprehensive, automated, automated end-to-end tracing across all data tools
Why Should Your Organization Track Data Lineage?
As organizations use more data for business intelligence, they get more visibility into sources and locations across the ETL, files, reports, and databases so that they can maintain data quality.
With data lineage, organizations learn:
- Who made changes
- What processes were used to change the data
- When the changes were made
- Where the person making the changes was located
- How data was updated
- Whether the data remained protected throughout the process
The 4 Benefits of Data Lineage
To use data effectively, organizations need to ensure data quality, integrity, availability and confidentiality. As companies acquire and accumulate more data, it gives them the visibility necessary to meet their needs and gain significant benefits.
1. Improved Compliance and Risk Management
Data lineage supports various data protection compliance initiatives. With visibility into the way data flows across systems from source to destination, organizations gain insights into data security and privacy risks. Using this information, they can implement the appropriate administrative and technical controls.
Similarly, it enables them to trace all data sources to ensure data integrity, which is needed to comply with consumer data protection regulations like the General Data Protection Regulation (GDPR) and California Privacy Rights Act (CPRA). With data lineage, they can more efficiently comply with these requirements, ensuring their data’s integrity no matter where it resides or how it’s used.
2. Better Data Governance
Data governance is the set of policies and processes that control how people use and access data. Data lineage enables organizations to monitor data flows, modifications and usage so that data governance managers and data stewards can implement key practices and processes such as:
- Defining common data descriptions across various metadata formats
- Specifying data quality metrics
- Analyzing issues arising from changes to source data formats and structures
- Avoiding data duplication
- Ensuring accurate, complete, and trustworthy data through visibility into context like data set design, quality, and transformations
3. Save Time on Manual Impact Analysis
As organizations use more data, changes to a single data set or data point can have a larger impact downstream. Data lineage gives organizations visibility into how changes impact business intelligence dashboards and other assets. With visibility across columns, tables and business reporting, the IT team saves time and reduces burdens associated with impact analyses.
4. Discover Problems With Data
When a data-related error occurs, data lineage enables organizations to quickly identify the source of the mistake. Since it visualizes data flows, organizations can get at-a-glance visibility into the upstream root causes of errors that led a report to break.
Further, it identifies incorrect assumptions and offers explanations for why errors might have occurred. This capability enables users who rely on downstream reports and tables to communicate and to be notified when data may be inaccurate and to be updated when issues are resolved.
Use Cases
By mapping data flows and data assets, data lineage can be used in several different ways. Two examples include data migration and data modeling:
Data Migration Strategies
As organizations migrate legacy data systems to the cloud or add new cloud-based data sources, they need visibility to help them break down systems into manageable objects without breaking anything. Data lineage enables organizations to streamline their migration projects by providing visibility into dependencies.
Additionally, it provides insight into opportunities, like consolidation or component removal, that makes the migration strategy more efficient.
Data Modeling
Data models use standardized schemes and formal techniques to create visual representations of information systems, communicating connections between data points and structures. Organizations can use data lineage to illustrate dependencies and their changes across the data ecosystem over time.
Track Your Data Lineage with Exasol
Exasol’s analytics database gives organizations the speed and visibility needed to build a data culture and gain crucial insights at the speed of now. Whether working with business-critical applications or Loading...advanced analytics, organizations can use Exasol as a database solution that integrates into their data warehouse as an analytic layer with a seamless connection to existing business intelligence tools. In doing this, they gain visibility across all their structured and unstructured data sources, ensuring that they establish and maintain data lineage and integrity.