Build trust in your data with data lineage
If you don’t know where your data came from, where it’s been, and how it’s changed over time, you might become suspicious about its relevance, completeness, and quality.
So what’s one way you can develop trust in your data? We’ve got two words for you: data lineage. The most useful data lineage tools give you a view of the past and the future, providing greater confidence your data can deliver benefits throughout your organization.
Until recently, most data lineage tools required too much effort to set up, weren’t as accurate as they needed to be, and could only provide a limited overview of data dependencies. Today, sophisticated data lineage tools make it easier to see:
- A comprehensive visual graph (commonly known as a DAG, a directed acyclic graph) of sequential workflows with depth down to the column level
- Downstream usage of columns and specific data points, including impacts of upstream changes
Businesses that prioritize data lineage are better equipped to navigate the intricate web of data dependencies, address data quality issues, and make better decisions. They’re also better positioned to mitigate risks, optimize operational efficiencies, and foster a culture of transparency.
Spotify and Capital One, for example, are two diverse companies that track data lineage to improve data quality and make better business decisions. The audio streaming service has been able to make more relevant recommendations to listeners, and the financial services firm has been able to make better lending decisions.
In this blog post, we’ll show you how data lineage improves your data quality and gives you more trustworthy, actionable insights.
Data quality and data lineage: Two sides of the same coin
Data quality and data lineage are intrinsically linked: The better the lineage, the higher the quality. Understanding your data lineage is important, because it’s how you substantiate quality as data moves across your business.
Would you build a house without a foundation? Would you pick up a box marked “Vegetables” you found on the street and feed the contents to your family or friends? Certainly not.
The same logic applies to your business: The only way to develop a truly data-driven organization is to use high-quality data. Think of it as building your house on a solid foundation. Or eating beautifully ripe, colorful, organic vegetables you bought directly from the farmer who grew them.
When your data is quality data, it better meets the needs of your business. And the people (or machines) that use it to solve problems will keep coming back for more because it’s so useful and valuable.
The ecommerce cannabis company Dutchie is just one of Datafold’s customers that hasn’t experienced a single production breakage or outage since implementing Datafold’s column-level lineage into its pipeline. This kind of confidence is critical to success for the fast-growing company.
Data lineage allows you to see how data flows through your organization, from its source to its destinations, and how it’s transformed and consumed over time. Best of all, you can use this information to identify and correct errors, and ensure compliance to maintain data quality so more people can benefit from it.
Data lineage helps organizations of all sizes
To improve data quality, data lineage can help organizations:
- Track data flow from inception to destination: Identify data pipeline problems, such as data loss or corruption, as well as understand how data moves through an organization’s data ecosystem
- Prevent breaking differences: By knowing how a BI dashboard is built and the upstream tables that impact it, use lineage to notify stakeholders of potentially breaking changes
Data matters no matter how big or small an organization may be. Many data professionals have made small, seemingly insignificant code changes only to find out that a downstream dashboard broke as a result of the code change. Whether there’s one user or a thousand users downstream, we need to ensure high data quality from source to final destination.
The importance of comprehensive data lineage tools
You can’t manage data quality without having total clarity on data flow, where errors happen, and where data may be used inconsistently. The best data lineage tools allow you to:
- Identify root cause issues: Sometimes a report or a data visualization contains incorrect data. Data lineage tools can help you identify exactly where and how that data went wrong
- Reveal data flow and dependencies: As businesses grow, they often accumulate data sources, leading to an overwhelming amount of dbt models and data warehouse tables. Data lineage clarifies the data flow from each source and identifies the processes and reports that depend on each data point, and allows you to wade through complex data pipelines.
- Address domain challenges: For highly regulated industries, such as healthcare and finance, data lineage tools can map the flow of data from its source (e.g., electronic health records or stock transactions) to its usage (e.g., medical research or financial reporting)
- Mitigate risks: Compliance programs often require detailed records of how data is stored, accessed, and processed. Data lineage tools can mitigate compliance risk by demonstrating in detail which systems can and can’t (or will and won’t) access sensitive information
These are baseline characteristics for choosing the right data lineage tool for your business. When making a selection, it's best to understand any limitations involved, and the impacts of those limitations as you continue to invest in data quality.
Remember, you need more than a 30,000-foot overview of your data lineage. In addition to comprehensive clarity inside and outside your data warehouse (through integrations with apps like Looker, Mode, Hightouch, and Tableau), you want to be able to dig down deep into the columnar level to address all your data quality challenges.
Without both comprehensiveness and granularity, you won’t achieve understanding at every level. If you really want to enhance data-driven decision making, build greater trust in your data sources and analytics results, and rest easy about governance and compliance, you need a data lineage solution that delivers breadth as well as depth.
Broad data lineage gives you the big picture
For example, on the broad side of the spectrum, when your data lineage tool plays nicely with your visualization system you gain:
- Visibility into where your data is used, and which BI tool assets are potentially impacted by upstream dbt code changes
- Traceability of your dbt models and data warehouse objects, as well as BI dashboards
- Early identification of dbt model changes and their impact on downstream data
Deep data lineage gets down to the details
On the deep end of the spectrum, column-level lineage reveals a dataset's path from ingestion to downstream visualization, displaying how each column of data is transformed and used throughout the data pipeline.
A column-level lineage feature lets you:
- Trace root cause analysis of data quality issues
- Conduct impact analysis of changes to the data pipeline
- Improve data governance by tracking who has access to data and how it’s being used
- Make better decisions with data
- Understand the dependencies of data after it leaves the data warehouse
The synergy between data lineage and BI tools
Integrating data lineage into BI tools gives users more precise and insightful reports. And BI systems enhance data lineage tools by making them more user-friendly and accessible.
More than just software, BI tools are strategic assets. They weave disparate data sources into a coherent narrative and transform raw data into interactive dashboards, reports, and charts that drive action.
The real value of BI tools is their ability to bridge the gap between data and decisions. But if you want your BI tool to be a good guide, you’ve got to feed it quality data. If you don’t, your data story may be more fiction than fact, which can lead to some expensive mistakes.
Benefits of integrating data lineage tools with BI platforms
Integrating data lineage tools with BI platforms provides a range of benefits, such as:
- Improved trust in BI reports: Businesses often take it on faith that the data they receive is authentic and trustworthy. They shouldn’t. Incorrect data ruins reputations, a trust that is hard to earn back. Businesses that rely on BI reports to make decisions need to have visibility into their data lineage
- Streamlined troubleshooting of data issues with BI tools: Manually searching data to troubleshoot data issues is a waste of time and money. It's an even worse endeavor when it involves BI. Data lineage tools provide the visuals needed to see where problems occur
- Quick ID of data anomalies in BI sources: Relying on human capacity to identify data that’s inconsistent, out of date, or otherwise erroneous is a sure-fire way to fail the customer satisfaction test. Data lineage tools help identify data quality issues by tracking the origin, transformation, and destination of data
Ultimately, the synergy between data lineage tools and BI platforms not only fortifies data integrity, it enables businesses to save time, effort, and money.
Datafold Cloud’s column-level lineage feature supports native integrations with BI tools like Looker, Mode, and (coming soon) Tableau, so you never have to get that “I think my dashboard is broken” DM again.
Datafold bridges the gaps
Advanced data lineage tools, like Datafold Cloud, include:
- Value-level diffs
- Continuous integration (CI) diffing and impact analysis
- Robust column-level lineage that integrates with your data warehouse, dbt project, and data apps (Looker, Tableau, Hightouch, and Mode)
- Strict security and compliance standards
Data quality and lineage go hand in hand. Data quality is the intrinsic value of the data, while data lineage reveals the history and trajectory of its creation, transformation, and use.
Measuring quality is important because it ensures that your data is accurate, complete, and consistent. Understanding lineage is important because it helps you track the different ways data flows through your business. Organizations need lineage information to identify data quality issues, troubleshoot problems, and comply with regulations.
While data lineage is essential for ensuring high data quality, the right tools are critical for navigating the ever-evolving challenges of data lineage.
Datafold’s automated data lineage platform bridges the gap between your dbt project, data warehouse, and BI tools, and acts as a complete solution to help you identify and resolve data quality issues at every level. With Datafold’s data lineage tooling by your side, you build a trustworthy and transparent foundation for your data work.
Datafold is the fastest way to validate dbt model changes during development, deployment & migrations. Datafold allows data engineers to audit their work in minutes without writing tests or custom queries. Integrated into CI, Datafold enables data teams to deploy with full confidence, ship faster, and leave tedious QA and firefighting behind.