Automated Data Lineage vs Data Traceability: What’s the Difference?
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According to some research, 75% of people believe that leveraging data will help businesses move to the next level.
One way to do that is through automated data lineage, but how is that different from data traceability?
We have all the answers here, so make sure you keep reading!
What is Data Traceability?
Data traceability is the ability to make sure that your data can be tracked and traced across any landscape. This will let you follow the data back to the origin point.
If you want to make clear assumptions and see accurate insights, you’ll need to be able to track every single data point. That’s why data traceability is just as important as reports.
It’ll account for every piece of data from the source to the target. Without this, you wouldn’t be able to know if the data is correct which could impact your company in the future. It’ll also make sure that the data is valid, and you can see where all the information has traveled.
What is Data Lineage?
However, in order to enforce this, many companies will have to implement automated data lineage. This will allow teams to trace the data and understand where it’s flowing. If the data stops, they’ll investigate to see where it went.
They can look at data to understand how migrations work, if your system needs any upgrades, if you need to consolidate anything, or if there are any report errors. It can also implement process changes without a lot of risks and perform any data discovery to create a framework.
Data lineage is the life cycle of the data. It shows the complete flow from start to finish. Data lineage is understanding, recording, and visualizing the data and where it goes. This includes a visualization of how the data transforms on its journey and why it does.
This will help ensure that users are getting their data from a good and reliable source. It also ensures that the data was transferred safely and loaded into one location.
If you’re going to be sending sensitive information, data lineage and tracking is very important. Without it, tracking the data becomes impossible and very time-consuming, which costs you more money.
There are some differences between data lineage and data traceability. The main difference is where it comes from and how it’s used. Sometimes people refer to technical lineage as the data lineage and business lineage as the data traceability.
The data lineage shows how the data moves and transforms between columns, tables, and systems. These diagrams look like a flow chart and non-technical users will even be able to understand them.
That’s because the data that built the diagrams show how the transformation takes place. Doing this helps people understand where the data is coming from, what policies are being used, and what standards apply to the data.
Traceability, on the other hand, is just the ability to be able to pull that data out to put it in a lineage. This is designed for people who want to see the overall view of the data before they implement a policy that affects it.
For example, if an analyst wants to see a high-level overview of the data, they’ll ask someone to trace the data. Then they’ll look at how the systems and business processes are impacted by that data. The traceability is more for people who have a technical understanding of the data.
Traceability can also be used to learn how adding a new asset would affect the rest of the business, which is why it’s meant for analysts and managers.
How to Implement Data Lineage
Now that you understand the difference, you can learn how to use data lineage tools to implement data lineage at your business.
However, this will depend a lot on what your organization’s culture of data is currently like. If you already have a good data management framework, this will help you build a good collaboration with other professionals for the implementation to be successful.
There are a few main steps to take to ensure that the process is successful. The first step is to figure out who the important business drivers are in this process. You’ll need to figure out why they want data lineages, like business changes, authority requirements, or even data quality initiatives.
Next, you’ll have to get senior management on board with your project. This will let you get the resources and time to be able to implement this process. When senior management is on board, they will help you move the project until it’s complete.
Try and convince them by bringing up all the benefits and showing that it can help be compliant with any regulations in the industry as well.
Next, you’ll want to prepare different requirements. There will be many different stakeholders, and they will all have their own expectations. Plus, the business and the technical stakeholders will also have different anticipations because of how they can understand the data.
Business people will want to know more about the value, but the technical stakeholders might be more interested in the data traceability.
Next, you can find a method to document the lineage. There are many tools and software out there that should align with your goals and what you want to accomplish. From there, you can implement the software and present it to management!
Learn More About Automated Data Lineage and Data Traceability
These are only a few things to know about automated data lineage and data traceability, but there are many more differences to consider.
We know that trying to implement one of these strategies and run a business can be overwhelming, but we’re here to help you out.
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