Your data is the foundation for your organization's operations, and your data infrastructure directly impacts how efficient those operations will be. Your data infrastructure should be organized, accessible, reliable, and clean. A clean data infrastructure is free from inconsistencies and redundancies, allowing you to access accurate, relevant, and complete data at all times. This directly affects decision-making, speeds up the transformation of raw data into valuable insights, and makes it easier for teams—especially nontechnical ones—to utilize those insights.

Data lineage is an important component of clean data infrastructure. It provides a traceable path of your data processes from source to destination. In this article, you'll learn more about the importance of clean data infrastructure and how your organization's data lineage makes it easier to manage and utilize data.

Why is data lineage important?

Your data lineage maps the entire lifecycle of your data, showing how it's created, moved, and used from inception to its final destination. It also tracks transformations and interactions, providing transparency and control over your data processes.

A comprehensive view of your data and its interactions helps you document and catalog your data assets more effectively while also enabling a better understanding of your data's origins and usage within your infrastructure. This gives you the visibility you need to ensure data quality and integrity within your systems and detect and solve issues quickly. When you have an accessible data lineage, you can explore the relationships between your different data sets, track changes, and analyze their impact on your entire data ecosystem.

The transparency that data lineage provides also plays a crucial role in your data management and governance efforts. It helps you easily identify who accessed your data, how it was transformed, and why it may have changed. This holistic view enables you to better comply with regulations like GDPR or CCPA, conduct audits, and enhance the security of sensitive information. Your data lineage is not merely a tool; it's your data.

The following are just a few examples of why data lineage is so important:

• Transparency and traceability: Data lineage enables clear visibility into the flow of data, ensuring your stakeholders understand how data is transformed, moved, and used across systems.
• Compliance and regulation: Data lineage helps you meet regulatory requirements by providing proof of data origin, transformations, and access for audits and compliance checks.
• Impact analysis: Data lineage helps you evaluate the potential effects of changes to data, such as identifying which reports, applications, and data assets will be impacted by updates or modifications.
• Data quality and troubleshooting: Data lineage facilitates the identification and resolution of data quality issues by tracing back errors to their source and understanding how they affect downstream processes.
• Team collaboration and onboarding: Data lineage improves team efficiency by providing clear documentation of data flows, making it easier for new team members to understand the data environment and for teams to collaborate quickly and effectively.

Where things can get ugly

While data lineage is the backbone of your data infrastructure, data lineage alone does not make a data infrastructure clean. It shows you how messy things are.

For instance, say you're working at an organization that has seen an increase in scale in the last few years. As a member of the data team at a data-backed company, your team gets called on to check the viability and support for most decisions. Over time, your organization has added different departments (marketing, sales, finance, and operations) that all rely on insights and reports from your data infrastructure. Ideas and requests for new data pipelines, dashboards, and metrics pile up. Keeping track of who uses what data, how it's transformed, and ensuring it's accurate quickly becomes overwhelming.

At this point, your team decides to invest in a data lineage and documentation tool, believing that it will provide the visibility and organization needed to regain control. You eagerly connect the lineage tool to all your data sources, transformations, and outputs across various systems. However, instead of simplifying your data infrastructure, the result is a visual mess: a tangle of data flows that span multiple environments. Because data lineage is a visual representation of your data, there's no value in the visualization if it's too complex to interpret.

How to clean up your data infrastructure

So, what other strategies can help you regain control and improve your data's clarity? Let's take a look at a few processes that can help clean up your data lineage and your overall data infrastructure.

Model your data properly

Proper data modeling is foundational for creating a clean and efficient data infrastructure. Your data models organize your data assets and structure their relationships with one another and other business entities. When data is poorly modeled, you can have ambiguous data relationships and redundant or inconsistent data. This leads to unnecessary complexity and confusion, particularly when it comes to understanding data lineage. Well-structured data models aligned with your business needs optimize data flows and improve interpretability within your data infrastructure.

There are multiple data modeling approaches, and your architecture can have multiple data models showcasing different perspectives, levels of detail, and expected data interactions. The following are a few examples:

• A conceptual data model is the highest level of abstraction in data modeling and is focused on aligning the data structure with business requirements. It shows what entities need to be stored and how they are related at a broad level, providing a big picture of your data infrastructure without diving into technical or implementation details.

• The logical data model dives deeper into data structure by defining the attributes, data types, and relationships between data variables in more detail. It focuses on how your data assets are logically organized and interrelated without exploring how they will be physically stored. This model helps establish structure and data consistency.

• A physical data model deals with the actual data storage and database-specific details. It helps determine how the tables and columns are stored, as well as their formatting, data types, and any constraints within the data assets.

Clear modeling layers—like data sources and staging, intermediate, and core models—also simplify complexities and create a modular, efficient data flow from raw assets to business-ready variables. A platform to map models and flexibly add new assets and relationships as your business grows is essential. For example, Secoda is an AI-powered data catalog, observability, and governance platform, which provides a single source of truth with embedded data discovery capabilities that integrate with your infrastructure to help trace data models and flows.

Leverage a semantic layer

Building on your data models and modeling layers, a semantic layer serves as a contained system for your metrics with standardized definitions and formatting. It can be used across your organization as a reliable bridge for translating data assets into business metrics for your reports and dashboards.

When implemented well, a semantic layer clarifies data lineage by filtering out redundant or unclear transformations across your infrastructure and provides a collaborative metric store. This improves the accuracy of your metrics and visibility, making it easier to understand how data flows through your organization.

dbt, a data transformation platform, offers a semantic layer that integrates well with business intelligence (BI) tools, allowing you to query easily accessible and consistent metrics across your infrastructure. dbt's semantic layer can also be integrated into Secoda, enhancing data discovery, cataloging, and automated documentation.

Identify critical business assets

Not all data is created equal, and not all raw data should be a priority for your business. Without clear prioritization, clutter builds up, slowing processing and increasing storage costs. Focusing on critical business assets improves efficiency and makes your data lineage more meaningful.

Start by working with stakeholders (both technical and nontechnical) to tag important assets, such as key reports, pipelines, or data sets, that have the highest business impact. Secoda provides powerful tagging and search functionalities that make it easy to categorize and prioritize business assets. Highlighting critical paths in your lineage helps you deprioritize less important flows and create a clearer, more focused view of your data infrastructure. This also supports better data management and governance decisions.

Automate deprecation workflows

After you've prioritized and categorized critical business assets, you have the context you need to remove unnecessary data and data workflows. Outdated or unused data assets can clutter your data infrastructure and make your lineage difficult to understand. Additionally, manually checking and deprecating these assets is time-consuming and prone to oversight, especially without automated reminders to flag them after periods of inactivity.

Thankfully, platforms like Secoda offer automated workflows that can help you deprecate stale or redundant data assets based on user-defined triggers or specified time intervals. Once you set up these workflows, you can be confident that your unused assets are properly flagged and removed from active lineage maps. This helps keep your lineage up to date while also letting your data management team focus on other important tasks, like ensuring your data is usable, consistent, and compliant.

Use metrics to refactor and enforce good lineage practices

A well-maintained data lineage is essential for a healthy and efficient data infrastructure. Metrics enable quantitative analysis and allow you to evaluate the performance and health of your data infrastructure. These metrics help you pinpoint areas that require optimization, identify maladaptive components, and enforce effective lineage practices by giving you objective data points to monitor.

Monitoring your data infrastructure operations provides insights into data asset popularity, usage frequency, query volume, costs (compute and storage), and error rates. This data helps you prioritize optimization efforts, identify areas for improvement, and ensure your lineage reflects reliable data paths. Frequently used data sets should be optimized, while rarely accessed ones could be restructured or deprecated. High error rates indicate potential pipeline issues that need to be addressed to boost data quality and simplify flows.

Your data management platform should offer these monitoring and observability features. For example, Secoda helps track key metrics and provides programmable alerts. Its dbt integration leverages tools like the dbt project evaluator to visualize and warn against deviations from best practices in your data models, lineage, and infrastructure.

Cases of model fanout, where you have multiple data models nested and dependent on another model, can indicate some redundancy or complexities in your data transformations. These redundancies can lead to a data lineage that is harder to understand and maintain.

You can view and set alerts for your data model metrics to guide refactoring and optimize your data lineage. Performance metrics within your data pipeline or infrastructure operations can also be used to highlight bottlenecks or unreliable transformations.

Incorporating metrics into your data management strategy not only helps keep your lineage up to date but also fosters a culture of continuous improvement. By continuously monitoring and optimizing your data infrastructure, you can enhance data accuracy, reduce operational risks, and effectively use your data assets.

Conclusion

A cluttered data infrastructure burdens your team and hinders the organization, preventing data democratization. Cleaning up your data infrastructure isn't just about buying tools—it requires consistent effort, controlling data assets, and aligning them with your business goals. In this article, you learned how to clean up your data infrastructure by categorizing and prioritizing data assets, deprecating unused assets, and using metrics and automation to continually optimize your data models and flows.

If you're trying to clean up your data infrastructure, consider Secoda, a data management platform offering data lineage, AI, automations, and observability features. With superior search functionality, it extends your visibility across your entire data infrastructure. Book a demo today to discover all that Secoda has to offer!