If you’re considering a cloud migration, it’s a good idea to start by looking at your data lineage. Having your data lineage in order can help fast-track your migration and ensure success. In this blog post, we’ll dive more into data lineage and why it’s important for cloud migrations. 

A Brief Overview

In brief, data lineage is the process of understanding how data moves through an organization. Data lineage gives you insight into how your data flows, which in turn can give you visibility on how data should move from legacy systems to cloud environments.

Moving data to the cloud has numerous benefits, including cost savings, flexibility, accessibility, and more. While the benefits of cloud migration are clear, there are also many challenges to consider when moving things to the cloud, such as security, data integrity, and system downtime.

Knowing your data lineage and having it in order can help address some of these challenges and help organizations help understand how their data is processed, managed, and secured before migrating it to the cloud.

What Is a Data Lineage?

As mentioned, data lineage essentially shows you how data moves through your organization. It gives you visibility into the life cycle of your data by tracking its flow and identifying the systems, applications, and processes that handle the data.

Data lineage can give businesses a graphical representation of data flow, including its origin, transformations, and destinations. With this information, companies can understand how data is sourced, integrated, and analyzed, as well as learn how it contributes to business outcomes.

As data sources and data management become more complex, data lineage has become more important. Let’s take a look at some of the reasons why this is the case.

Why Is It Important?

Data lineage is important for the cloud migration process, but it’s also an important element of data management in general. Here are some of the primary reasons businesses implement data lineage tools and best practices in their data stacks:

• Compliance - Organizations need to trace data for regulatory purposes, and data lineage tools make this process much simpler. Data lineage creates a simple audit trail to make reporting and security checks easier.
• Trust - Data lineage provides more context for data and allows organizations to see every stage of data’s life cycle. This makes it easier for organizations to verify data is accurate and reliable, leading to more trust in the data they collect and the data-driven decisions made based on this data.
• Data Quality - Data lineage makes it much simpler to track the root of data errors and inaccuracies, allowing organizations to improve data quality over time. 
• Impact Analysis - Data lineage helps organizations save time on manual impact analysis, allowing IT users to conduct granular analysis and see downstream changes automatically, reducing disruptions when changes occur.

In short, data lineage offers numerous benefits that organizations can leverage with the right tools and strategies.

The Role of Data Lineage in Cloud Migration

We understand a little bit more about data lineage and how it relates to the cloud but let’s do a deeper dive into data lineage’s role in cloud migration. One of the biggest challenges of moving data to the cloud is ensuring the data remains accurate, complete, and consistent from one environment to the transfer environment. This is where data lineage can help. 

For one, data lineage can help keep the data platform agnostic, so migrating from system to system is simplified. With data lineage, organizations can get granular visibility into how data is stored, how it can be accessed, and how it needs to be transformed to fit in a new system.

Data lineage can also help streamline the cloud migration process and reduce the resources needed for the process. Data lineage can help divide data up into smaller groups, minimizing the downtime needed for migration and helping to minimize external dependencies by grouping the right data pieces together.

Data lineage can also help organizations consolidate data before migration and leave out data that is no longer needed. Rather than dragging this obsolete data through the migration process and adding it to the workload, data lineage can give you visibility into these unneeded elements and save you the effort. By mapping your data with lineage, you can create the most efficient and effective cloud migration strategy possible without wasting resources.

In short, data lineage plays a critical role in cloud migration by providing a clear understanding of data movement and dependencies. This information is essential to ensure that data is migrated correctly and is fit for purpose in the new environment.

Key Steps To Follow When Migrating Data To a Cloud Environment

Migrating data to a cloud environment requires a well-thought-out plan to ensure that data is transferred securely and effectively. Here are some key steps that organizations should follow when migrating data to the cloud.

• Understand the scope of your migration - Cloud migration can be a complex and involved process, and it needs careful planning to pull off. You will want to take a comprehensive look at your data systems and determine what needs to be moved to the cloud, what can be moved, and how you will conduct the migration.
• Choose your provider - Most organizations will migrate to a third-party provider or use a hybrid cloud model. Determine what model is best for you and start comparing your provider options. You will want to consider your budget, the services they offer, their customer support, and other factors to determine which provider best fits your needs.
• Audit your data - Using data lineage and other data management tools and techniques, conduct a thorough audit of the data you plan to migrate. Look for inaccuracies, inconsistencies, duplicates, and other errors that may hinder the migration process. 
• Assess application compatibility - Make sure the applications in your data stack are compatible with the cloud environment you choose. Determine if you will need to make any adjustments or configurations to integrate your applications with the cloud.
• Outline your strategy - Once you’ve chosen your provider and defined the scope of your migration, you will need to outline a detailed and comprehensive data migration strategy. This strategy should include expected costs, timelines, resources, goals for migration, dependencies, and other factors important to your company.
• Test your plan - Run tests to ensure your migration will run smoothly and seamlessly. By testing your plan ahead of time, you can reduce friction in the process and address any potential issues that might arise. Do a trial run with a small data set to see if your strategy is sound and if anything needs to be adjusted before the full migration.
• Implement and monitor migration - Make sure to monitor the migration process, keep track of errors, and address them as the migration takes place. Monitoring the process closely will help prevent data loss and keep downtime to a minimum.
• Perform post-migration checks - Have your post-migration checklist ready to ensure the migration was a success. Audit your new environment, monitor performance, check to ensure users and adopting the new system, and perform any other post-migration measures to make the cloud transition as friction-free as possible.

Once your migration is complete, you can start reaping the benefits of the cloud. The process can be a time-consuming and resource-intensive one, but the advantages are typically well worth the effort invested.

Data Lineage Best Practices

Now that you understand the importance of data lineage and its role in cloud migration, it’s good to keep in mind best practices for data lineage to ensure your processes remain up-to-date and efficient. Here are some key best practices to follow when implementing data lineage in your organization:

• Automation - Manually tracking data lineage, especially in organizations with large amounts of data, is borderline not feasible in today’s fast-paced business environment. Automating data lineage with data tools can make the process much simpler, increase accuracy, and save costs.
• Review metadata sources - The owners of data lineage and data sets should make sure to review and verify metadata sources to address bugs and errors and ensure these sources are accurate and trustworthy.
• Trace multiple data lineage sources - There are many types of data lineage, and tracking these different types can give businesses even more context, visibility, and insight into their data. If you want to make the most of your data lineage, it’s best to track multiple sources like operational lineage, descriptive lineage, business lineage, and more.
• Utilize your data lineage - Make the most of your data lineage investments and use the information you glean from data lineage to optimize other areas of your business.

By following these best practices, you can make the most of your data lineage tools and ensure your investment into data lineage is worthwhile.

Try Secoda for Free

Secoda is the ultimate tool for automated data lineage. With Secoda implemented, you can get end-to-end visibility into your entire data stack. Some tasks that Secoda can automate include automating metadata documentation, automating data mapping, notifying users of the impacts of changes, data quality test visualizations, and more. Secoda easily connects to most databases or anything with a REST or API.

Along with automating your data lineage, Secoda is also an all-in-one platform with tools for data cataloging, data discovery, data documentation, data sharing, data ticketing, data access management, and more. Ready to see how Secoda can help your organization with automating lineage? Schedule your demo or try Secoda for free today.