How to Choose the Level of Lineage Granularity for Your DataOps Strategy
Table of Contents
- What is Data Lineage?
- What is Data Granularity?
- What is Lineage Granularity, and Why is it Important for DataOps?
- Benefits of Using Data Lineage for DataOps
- Exploring the Different Layers of Lineage Granularity
- Determining the Right Level of Granularity for Your DataOps Strategy
- Discover How Data Lineage Can Help Your Business
When evaluating your DataOps strategy, you need to make sure that your data pipelines are healthy. If you fail to do that from the beginning, the rest of your DataOps projects are at risk. But, with all of the different types of lineage that can be layered together to create the whole picture, you might have questions about where to start and how deep your lineage needs to run.
In this post, we’ll break down how data lineage factors into your strategy and how to make sure you have the right layers or levels of lineage to help you meet your business goals.
What is Data Lineage?
Data lineage is tracking how your data flows through all your processing systems—where the data originates, its destination, and what changes and transformations are happening along the way.
But is that it? Not really.
Data lineage uncovers data entity dependencies, offering insight into data history, quality, context, and impacts on other entities. Using graphical representations, it visualizes data movement and relationships. Automated data lineage gives you a real-time roadmap to locate and troubleshoot errors through missing or broken data, optimize performance, and ensure ethical, compliant data management. It's your tool for complete visibility and clarity, eliminating data blind spots in your systems.
But to get the most out of it, you need to have a high level of detail or data granularity in your data flow.
What is Data Granularity?
Data granularity is all about zooming in or out on your data, allowing you to choose the right level of detail, from a high-level overview to granular specifics, impacting data collection, storage, and processing. Think of it like Google Maps. You can choose a high-level overview, seeing the very basics of road names and locations of buildings – or you can choose the satellite or street-view so that you can see important details like which roads are dirt vs. paved, which buildings are unoccupied, and which empty spaces are fields vs. parking lots.
Data granularity is a trade-off: finer granularity means more precise analysis but complex data lineage, while coarser granularity simplifies lineage but sacrifices precision. For example, daily sales data offers richer insights but requires handling more lineage details, whereas yearly data simplifies lineage but provides fewer insights – It's all about finding that sweet spot!
Why does it matter? It's the key to precision in understanding and utilizing your information to facilitate informed decision-making, anomaly detection, and process optimization in data operations.
Wondering about the purpose of data granularity?
- Improved Precision: Choose fine-grained data granularity to uncover hidden insights.
- Streamlined Lineage: Opt for coarser granularity to reduce complexity.
- Optimized Insights: Strike the right granularity balance, aligning data operations with business goals.
However, managing finer data lineage can be challenging in complex, ever-changing data settings. That's where Manta, an automated data lineage tool, comes in, offering end-to-end data lineage for multiple platforms and technologies, making discovery, mapping, and monitoring a breeze.
What is Lineage Granularity, and Why is it Important for DataOps?
In the world of DataOps, challenges like elusive data trustworthiness, compliance risks, and troubleshooting delays can cause missed opportunities. Inefficient workflows and overlooked dependencies can hinder process optimization, resulting in slower delivery, missed SLAs, higher costs, and lost prospects.
Sound familiar? This is where Lineage Granularity comes in.
Lineage granularity in data operations is the extent to which data lineage information is tracked, offering insights into the data's source, transformation, and destination at a specific level of detail.
Leveraging lineage granularity in your DataOps offers:
- Data Quality Management: Proactively assures data quality by pinpointing and addressing issues at a finer level.
- Efficient Troubleshooting: Accelerates root cause analysis, minimizing downtime and ensuring reliability.
- Performance Optimization: Identifies bottlenecks and areas for improving data pipelines.
- Data Transparency: Improves DataOps workflows by revealing data flow, transformations, and usage.
- Impact Analysis: Assesses changes' impact on data sources, transformations, and pipelines for risk assessment and troubleshooting.
- Compliance Assurance: Proves compliance with data regulations through granular lineage documentation.
Benefits of Using Data Lineage for DataOps
If your data practices jeopardize integrity, your laborious manual error identification and quality maintenance are inexcusable. This blatant negligence directly erodes stakeholder trust and undermines decision-making.
Implementing data lineage in DataOps enhances transparency, quality, and compliance while neglecting it leads to inconsistencies, bottlenecks, and auditing complexities. An automated data lineage platform streamlines data tracking, providing up-to-date information in a dynamic data environment, improving data comprehension, impact assessment, retention policy decisions, and compliance support while saving time and resources.
Improved Data Quality
Data lineage can pinpoint the origins and resolve data quality issues by showing a clear and detailed map of how data moves and changes. It can help you:
- Trace the data to its source and verify its accuracy and validity.
- Identify the root cause and fix any errors or inconsistencies in the data.
Enhanced Impact Analysis
Data lineage, specifically design time lineage, helps you anticipate and avoid data change impacts on downstream applications or reports and test and validate your changes before production. It achieves this by showing the movement and transformation of data with high granularity and accuracy in an environment that is not yet live (unlike runtime lineage, which can impact live environments).
For example, changing the bonus calculation algorithm in the sales data mart impacts the weekly financial forecast report and other dependent data systems. Data lineage shows the data change effects on the transformations before the report, enabling proactive issue mitigation and risk management.
Enhanced Transparency and Trust
Transparency in data processes shows how data is managed and used. Trust among stakeholders is the confidence and belief in the data, providers, and users. Transparency and trust are positively related, mutually reinforcing and can be improved by:
- Reducing data uncertainty, ambiguity, and bias
- Allowing stakeholders to provide feedback, input, and suggestions
- Providing clear and accessible data information
- Enabling stakeholders to verify, validate, and audit the data
Lineage accelerates issue resolution in data processes by tracking data movement, pinpointing errors, and utilizing data granularity for comprehensive insights. It helps teams find and fix issues quickly by tracing them to their source, reducing downtime and business impact.
Exploring the Different Layers of Lineage Granularity
Data lineage has different layers that show various data flow aspects. A multi-level data lineage tool like Manta can show how data moves, changes, is used and quality-checked across these layers. This can help get a complete and clear view of the data flow for different needs and goals.
The Performance Layer boosts system efficiency and optimization and improves data quality, reliability, and observability. Comprising three sub-layers—Database-Level, System-Level, and End-to-End Lineage—it shows the data lifecycle, aiding in error identification, bottleneck resolution, dependency tracking, and impact analysis.
For example, a data engineer can troubleshoot delayed or corrupted data pipelines by tracing data back to its source, while a data analyst can validate report accuracy by observing applied transformations.
The Usage Layer understands data consumption by end-users, applications, or systems with two sub-layers: Application-Level Lineage (tracks lineage within applications) and Cross-Functional Lineage (tracks lineage across departments). These sub-layers offer insights into data usage patterns, preferences, and dependencies.
For example, a data analyst can optimize business reports by analyzing user and application data consumption. In contrast, data governance teams can foster collaboration and alignment among teams and stakeholders by sharing usage insights.
Data Quality Layer
The Data Quality Layer improves data accuracy, reliability, and consistency by tracking data origins and history and rectifying data anomalies, errors, and issues. Comprising three sub-layers—Column-Level Lineage, Table-Level Lineage, and Business Process-Level Lineage—it offers a comprehensive perspective on data quality and integrity, enabling monitoring, auditing, and improvement.
For example, a data scientist can validate a machine learning model by scrutinizing input data sources and transformations, while a data auditor can ensure compliance by documenting data provenance and quality.
Determining the Right Level of Granularity for Your DataOps Strategy
Data granularity affects data analysis and consumption’s level of detail, flexibility, and performance. The appropriate granularity level depends on business requirements, data volume, source systems, and quality.
Striking a balance is key to avoiding complexity from excessive granularity or data loss from over-aggregation within your DataOps strategy.
Evaluating Your Organization's Needs and Objectives
Data lineage needs vary for different stakeholders. Data engineers and analysts require granular details to debug, optimize, and validate pipelines; data governance teams and auditors need comprehensive lineage for compliance; and data consumers and business users look for contextual information to build trust and understanding in the data. Aligning lineage granularity with organizational needs is vital for enhancing data lineage information's usability, performance, and value for stakeholders and scenarios.
Analyzing Compliance and Regulatory Requirements
For compliance, choose the correct lineage granularity by:
- Understand the data regulations and compliance requirements.
- Working with experts to learn about data compliance.
- Mapping compliance needs to lineage components (sources, transformations, controls, trails).
- Assess and prioritize the compliance impact on lineage granularity. [Pro Tip: Consider sensitivity, complexity, and audit frequency.]
- Customizing, testing, and documenting lineage for compliance and auditing. [Pro Tip: Add metadata, tracking, or controls, and use access controls and encryption.]
Also, stay proactive, monitor and adapt to changing regulations, educate your data teams, and seek feedback from compliance experts and stakeholders for continuous improvement.
Balancing Complexity with Scalability
To balance complexity with scalability to suit current and future growth:
- Define the purpose, scope, use cases, stakeholders, and requirements.
- Standardized the method for data lineage granularity.
- Use a common metadata model and vocabulary for clarity in documentation.
- Automate and visualize data lineage analysis using appropriate tools and techniques for efficiency, scalability, and performance.
- Explore options like distributed databases, cloud services, and caching/indexing to optimize query speed and responsiveness.
- Regularly revise and align your data lineage strategy with goals and feedback.
Considering Resource Constraints and Technical Capabilities
Assess your resources, such as team size, expertise, budget, tools, and business objective, to decide a lineage granularity level. Choose a granularity level that matches the complexity of your data environment, technical capabilities, tools, stakeholders’ needs and expectations, and lineage tools and platforms.
Collaborating with Stakeholders and Data Users
Understand stakeholders’ data needs, challenges, and preferences. Identify the key data elements, systems, and processes. This helps you determine the optimal level of detail and depth for data lineage information, ensure quality and usefulness, and avoid conflicts while building trust and confidence.
Discover How Data Lineage Can Help Your Business
Your business has unique data needs, and you deserve a unique data solution. Data lineage is the key to unlocking the power of your data for better decisions, compliance, and efficiency.
But how do you achieve data lineage in a complex and diverse data environment? The answer is Manta, the data lineage tool that does it all. Manta automates and visualizes your data flows across all technologies and platforms so you can understand, optimize, and govern your data efficiently and accurately.
Rise above “good enough”. Schedule a demo now and discover how Manta can take your data lineage to new heights.
P.S. This post was written by a human!