Democratizing Data Lineage as a Data Enablement Strategy

There’s no shortage of data - organizations collect a lot of it. And with tools such as dbt and Databricks, there’s no shortage of modeled data that is already available for more refined needs, and more importantly, can be used by different parts of the organization referencing the same data sets. However, keeping track of all of these tables, files, models, Spark jobs and whatnot, and providing enough context for a person to operate autonomously based on the data is still a huge challenge. 

The concept of data democratization aims to make data accessible to everyone, regardless of their position within the organization or technical expertise, of course while ensuring necessary permissions and governance. This shift to enable data is crucial for fostering a data-driven culture where different business groups can leverage data into products and decision-making, and don’t have to struggle to add a new KPI, or understand how a specific data set is defined. A key component of this transformation is data lineage, which provides for better transparency into how the data is collected, processed, and consumed.

What is data democratization?

We can informally define data democratization, sometimes synonymous with data enablement, as the ongoing process of enabling every individual within an organization to work with data comfortably. “Good” data democratization helps break down various barriers that traditionally limited data access to a select few, and no less important, allow the broader group to get more context and understanding of how the data is generated and what sources is it collected from. Ideally, we’d want every person in the organization to be able to look at a dashboard or even create one from the various data sets we already have–”Already” being the key term here. If we, as the organization, are able to do this, we can empower the broader workforce to make informed decisions and create data-powered experiences whether they are internal or customer-facing.

Role of data lineage in data democratization

Data lineage is the process of tracking the flow of data from its origin through its various transformations and uses within an organization, from every source and all the way to every destination. Data lineage should provide a unified view of the data's journey, highlighting how it has been processed, transformed, and consumed. Data lineage is a key component in data democratization for several reasons:

Transparency

Data lineage offers transparency into the data lifecycle, allowing users to see where data comes from, how it has been modified, and where it is used. This transparency builds trust in the data and enables users to make informed decisions confidently. For users who want to create new data sets, tracking the origins of each data sets is crucial, and rich, intuitive data lineage can be a huge enabler.

Accountability

By providing a clear record of data transformations, data lineage helps identify who is responsible for data changes. This accountability ensures that data quality is maintained and any issues can be traced back to their source for quick resolution.

Data Quality Assurance

Data lineage helps in maintaining data quality by tracking the data's journey and identifying potential issues at each stage. This proactive approach ensures that data remains accurate, reliable, and consistent, which is essential for effective data democratization.

Regulatory Compliance

Many industries are subject to stringent data regulations that require detailed records of data processing activities, and are more sensitive to data sharing and access controls. Data lineage ideally provides the necessary documentation to demonstrate compliance with these regulations, while also allowing the organization to manage these efficiently on an ongoing basis.

How can we democratize data lineage?

Now that we’ve established why data lineage is important for data enablement, how can we democratize data lineage in itself? At Foundational, this is a topic that is at the heart of our philosophy for software and data development. It consists of the following three principles:

• Easy to access: We want data lineage to be accessible to everyone, of course within the allowed access. Easy to access can mean that data lineage should exist everywhere developers work, for example in GitHub and GitLab, or the CI tool. Easy access means that if someone outside of the data organization wants to look at a certain data lineage path, it should be easy for them to do so, for example through shared links and customized views.
• Easy to understand: Navigating through a complex lineage graph is not always intuitive, and understanding complex transformations and the code that defines them is not trivial. Our goal is to make the lineage exploration experience as intuitive as possible, as if you were navigating your favorite maps app (which isn’t always trivial as well, we know).
• Easy to work with: Traditionally, setting up and maintaining updated lineage across the entire stack was a long painful process. In fact, for most organizations today that is still the case. Ideally, lineage should be fully automated, both the setup and/or deployment phase as well as the ongoing work. The approach of traditional data catalogs that require ongoing maintenance is unfortunately not sustainable any more against the speed of delivery that modern organizations require.

Steps to Achieve Data Democratization

Implement User-Friendly Data Tools

To make data accessible, organizations need to invest in intuitive data tools that cater to users of all technical levels. For example, business owners can benefit from an intuitive BI tool that does not always require SQL. Analysts can benefit from BI tools that offer enhanced visualization and customization options. Analytics engineers can benefit from tools such as dbt and SQLMesh which make it easy to create data pipelines. These tools should simplify data modeling, visualization, exploration, and analysis, allowing users of various technical levels to operate autonomously.

Documentation

Maintaining documentation for data is hugely important for developing data literacy across the organization. While Q&A channels and one-on-one support can help employees build their data skills and confidence, there’s no substitute for comprehensive documentation that allows for employees to learn on their own. Tools such as Foundational can also enforce that new data assets are always created with supporting documentation, and in some cases automate document-creation for existing assets.

Foster a Data-Driven Culture

Leadership plays a vital role in promoting a data-driven culture. By championing data use and encouraging data-based decision-making, leaders can set the tone for the rest of the organization. Celebrating successes that result from data-driven initiatives also reinforces this culture.

Data Governance and Security

While democratizing data, it's essential to maintain robust data governance and security measures. This ensures that data is accessible yet protected, with clear guidelines on data usage and privacy. Implementing role-based access controls is crucial for effective management of data access, in particular in highly-regulated industries that handle PII and PHI.

Leverage Data Quality Tools

Data democratization is only effective if the data is reliable and accurate. Utilizing data quality tools helps maintain data integrity, ensuring that employees can trust the data they are using. Regular data audits and quality checks are vital components of this process. Tools that take a shift-left approach to data quality are therefore critical for doing this at scale.

Summary

Data democratization, supported by robust data lineage practices, is transforming the way organizations operate. By enabling everyone in the organization to work with data comfortably and ensuring transparency and accountability through data lineage, companies can harness the full potential of their data assets. Implementing user-friendly tools, ensuring appropriate documentation, promoting a data-driven culture, ensuring data governance, and leveraging data quality tools are key steps to achieving data democratization. Organizations that fully embrace this process, and adopt the appropriate tools to support it, can get a lot closer to democratizing data (and yes - Foundational can help you democratize data! Reach out to learn more).