Data lineage is a process that enables you to track where data has come from and where it has been used. It's an essential component of any organization's data governance strategy, offering insights into how your company's data is managed and giving you clearer insight into risk. But, according to McKinsey & Company1, only about half of organizations have a formal program for managing their data—and this approach can cost an average of $5 million per year2. Automating your end-to-end data lineage can save you time and money in the long run by helping your team:
What is Data Lineage?
Data lineage is the process of tracking data from its source to its destination. In other words, it's a way for you to know where your data comes from and where it goes. It's important to know this information because you need to know that your data is reliable and trustworthy. If you're using someone else's data in an analysis, you don't want there to be any chance that the numbers are wrong or misleading.
Data lineage can help with this by documenting exactly when and how a piece of information was collected and used. This makes any errors easier to find if they happen along the way—you can see exactly what happened when the original source was put together, so if something looks wrong at some later date (like if a calculation was incorrectly done), then this will show up in your documentation as well as give an idea of what might be causing any anomalies further down stream like with calculations based off those sources/sources-of-sources..
An important part of the data lifecycle is tracing where the data that the business relies on comes from.
An important part of the data lifecycle is tracing where the data that the business relies on comes from. Data lineage is a process of tracing the data back to its source, which helps you understand the quality and trustworthiness of your information.
Data lineage tools can be used to automatically generate data lineage diagrams, which help you identify issues with your data sources. ER/Studio Data Architect is an example of such a tool.
Why Data Lineage is Important to Data Teams?
Data lineage helps to understand the data flow. It helps to identify the source of the data, it helps to identify the quality of the data and it also helps to identify the data flow between different systems and departments.
As a result, it provides us with a better understanding of what happens with our data as it flows through different systems (or people) when we integrate or interact with one another. This in turn can help us make better informed decisions about where our business needs are, how they’re being met, who’s responsible for what and ultimately ensure that we have an efficient system in place which is able to handle large amounts of information effectively and efficiently
Data Lineage vs. ERD Diagrams
Data Lineage and ERD Diagrams are two different things.
An ERD is a graphical representation of your database schema. It shows how tables relate to each other, as well as what fields each table contains. While an ERD can be useful for designing and documenting your database schema, it's not a data lineage tool since it doesn't tell you where your data came from or went after being used in the application.
Data Lineage tracks movement in terms of rows within tables (or any other data container). For example, when you insert data into a table through an API call or web form submission, Data Lineage will track these changes back to find out what sources they came from such as an API call made by another team member's application code or even another system altogether! This is why we say "data before its time" because if there wasn't already some method for tracking where this information comes from then we wouldn't know where else this could go wrong!
Advantages of using Data Lineage Diagrams and ERDs together
Data Lineage Diagrams and ERDs are used together for analysis and resolution of data quality issues. Data lineage diagrams show the flow of data from one system to another, while ERDs show the flow of business processes from one system to another.
The two complement each other because they show different views of data flows in an organization: Data Lineage Diagrams focus on how information is created, used, stored, and disseminated within an organization; ERD focuses on how information moves between organizations (such as suppliers) when it is being incorporated into a company's business processes.
Step 1: Understand your business processes and data flows.
In order to decide what you want to automate, you need to know what the problem is. As we mentioned earlier, this isn't always easy. If you're not familiar with your business processes and data flows, it can be hard to see where automation will help.
The first step in automating end-to-end data lineage is understanding your business processes and data flows. You'll want to look at everything from how salespeople interact with customers through their CRM system, all the way up through finance's use of metrics for performance reviews—and anything else that touches your company's most important data. By taking a big picture view of this process flow, you can begin identifying areas where automation could save time and money without sacrificing quality or accuracy of results or insight into performance metrics (or whatever other goal you're trying to achieve).
Step 2: Map manual processes and flows.
The next step is to identify all of the manual processes and flows that are in place. To do this, you will need to draw a diagram of each flow; use a tool such as Visio or OmniGraffle for best results. You should include all steps involved in the process, including data sources and data sinks. For example, if your team has been manually entering data into an Excel spreadsheet after collecting it from another source, then both sources must be included on your map (the manual process) along with any other related information (such as who is responsible for entering which data).
Once you’ve mapped out all of these manual processes and flows visually using diagrams or drawings (or even mind maps), it’s time start thinking about how we can automate them using software tools like Tableau!
Step 3: Identify your internal data sources
The third step is to identify your internal data sources. This can be done by using a data discovery tool, like Secoda's Data Discovery platform. Data discovery tools are designed to find all the data and applications within an organization so that you can start building a comprehensive picture of your current state of data lineage.
Data discovery tools can help you find where your sensitive and important information is stored, who has access to it, and how it’s being used by teams across the enterprise. In addition to identifying what kind of information exists in different locations (like databases or spreadsheets), they also give visibility into how that data is being used – who has accessed it recently? How often have they accessed it? What are their roles within the company?
Step 4: Document manual processes and flows with data lineage diagrams.
Data lineage diagrams are used to document the flow of data through a process. They can be used to identify the source of data and the transformation that has occurred, as well as where it is stored, who owns it, and how long it is retained.
It's important to note that data lineage diagrams don't just describe the technical details; they also show which parties have access to specific pieces of information at any given time. This can help you determine where potential leaks or breaches may be occurring in your company or organization.
Now that you know what a data lineage diagram looks like, let's take a look at some examples!
Step 5: Automate data lineage diagrams with an automated data lineage tool.
Automating the data lineage diagram process is essential for both eliminating manual tasks and ensuring that your organization is following best practices. An automated data lineage tool will ensure that you are automating each step of the process, from extracting data from source systems to populating your data lineage diagrams to analyzing them for potential issues.
Automated tools can also be used to map business processes to their corresponding data lineage diagrams, making it easier for organizations to understand how one process affects another (and vice versa) within the context of an enterprise-wide system.
With the tools and approaches outlined here, you can automate your company's end-to-end data lineage efficiently.
When it comes to compliance, you need to know where your data is, who has access to it, and how that information flows throughout your organization. With automated end-to-end data lineage, you can answer these questions easily and quickly.
Automated end-to-end data lineage also helps improve the quality of your environment by helping identify errors in the process of creating information assets. This allows organizations to identify issues with data quality before they become problems or mistakes that affect their business operations or reputation.
It is possible to automate end-to-end data lineage using tools like Secoda.
It is possible to automate end-to-end data lineage using tools like Secoda. Automation is the key to efficiency and cost savings, as well as a means of identifying gaps in your lineage and providing remediation recommendations.
Automation can be done at all levels of the data lineage process: from gathering operational elements such as folders, files, users and groups; through processing them into metadata tags; through enriching and linking those tags with external sources such as asset management systems; right up until delivering those enriched results back into production systems for use by analysts or business users.
The key takeaway is that it is possible to automate end-to-end data lineage using the right tools. This allows companies to avoid manual processes, reduce the risk of errors, and save time and money in the long term.
Try Secoda for Free
Automating data lineage brings numerous benefits to data teams. By implementing automated data lineage with tools like Secoda, data teams can gain a clear understanding of the origin, transformation, and movement of data throughout their organization's systems and processes. This visibility into data lineage provides valuable insights into data quality, dependencies, and the impact of changes. Automated data lineage helps teams identify and resolve issues promptly, such as data inconsistencies or errors, by quickly tracing back to the root cause. It also aids in compliance and regulatory requirements by providing a transparent audit trail of data flows. Furthermore, automated data lineage enables data teams to make informed decisions about data governance, data integration, and data management strategies, as they have a comprehensive understanding of the data ecosystem. Overall, automating data lineage enhances data team productivity, improves data quality, and facilitates better data governance practices.