Data is like air today. You’re surrounded by it, and every aspect of your business depends on it in some way or another.
We’re also always emphasizing effective data management, but we don’t really appreciate how well the data is working for our company.
Get this: Your data should always work for your company round-the-clock. Period.
But to ensure this, you have to understand its nuances—how it originated, how it got in your system, and how it travels through the business. Data lineage can help you dig into the origins of your data goldmine, interpret it, and help you ensure it ends up exactly where it should be.
Are you wondering what we mean by that? In this guide, we’ll tell you everything you need to know about data lineage and how you can implement it in your business for better results.
What Is Data Lineage Anyway?
Data lineage traces your data’s life cycle: its origins, destinations, and characteristics. It strives to show the entire data flow—from the beginning to the end.
Think of it as a process to understand, report, and visualize data as it flows through the various data sources towards the final consumption point.
The main reason why data lineage is so important is how it affects and helps organizations. It has the following benefits:
- It helps organizations comply with regulations
- It helps organizations automate data mapping efforts
- It helps organizations make sense of and trust their data
- It helps organizations save time while doing manual impact analysis
Furthermore, data lineage also includes all transformations and alterations that a specific set of data underwent along the way, including how the data was transformed, which aspects changed, and why.
How Data Lineage Works
The whole idea behind data lineage is to enable companies to perform specific tasks, such as:
- Identify and check errors in data processes
- Carry out system migrations
- Create a data mapping framework by combining data discovery with metadata
- Implement process changes with lower risks
It allows users to verify the data is coming from a trusted source, has been altered accurately, and is loaded to the correct location. As a result, company owners can make strategic decisions based on accurate data.
On the other hand, if data processes aren’t tracked accurately, the effective use of data will become impossible—or at the very least, data verification will become time-consuming and incredibly costly.
To understand how data lineage works in more detail, you need to know the five double W’s of data lineage. Data Lineage gives us answers for any specific dataset, such as:
- Who created the data?
- Why does the data exist?
- Where is the data located?
- When was the data created?
- What information does the data contain?
Read on as we explain them in greater detail below.
1. Who Is Using the Data?
You’ll obviously have several questions while analyzing the data—one of which is who is using the data and from where.
With the help of a data lineage graph, you’ll be able to find out and verify who is using this data. You see, when you have visuals of the data lineage, it makes it easier to find out the answers to these questions.
2. When Was the Data Created or Updated?
As a data owner, it’s your responsibility to store the data in the right location and ensure you grant access to only the authorized people.
Now, imagine a situation where you don’t have any idea about who the data owner is. You won’t know who maintains the data and who you should contact to correct a specific part of the data.
Precisely why knowing the data owner is so important for greater clarity.
3. What Information Does the Data Contain?
Defining access policies related to the data is always crucial. But before you do that, you have to understand what information does the data contain.
Doing this will simplify classification, allowing you to understand which data policies you would need to define against the data. In turn, this will help you protect your sensitive data.
4. How Is the Data Being Used?
Organizations often use data to create various reports that can be used to make decisions for the company’s betterment and long-term survival.
To create these reports, however, you need several data sets that are generated within the organization. Having a data lineage diagram will show you which data sets are being used. So, if you find any errors in your reports, you can use the diagram to trace the source of the error.
5. Why Is the Data Stored or Used?
“Why does this data exist?” This is one of the most important questions because if you don’t need any data, you should simply delete it to prevent it from getting into the wrong hands.
Plus, unnecessary data will only lead to unnecessary time and money spent. That’s why you should know about every data said that ends up becoming a part of your system.
Figuring out the answers to these five questions is how you get started with data lineage and do it right.
Data Lineage Use Case #1: System Migration and Upgrades
You can consider using advanced data lineage to enable data teams to achieve complete visibility into their BI environment to streamline and simplify migrating from a legacy BI tool to a modern man or upgrading a system to the new version.
With lineage capabilities becoming automated, team members can visualize which ETL processes or reports are duplicates and which one of them relies on obsolete, questionable, or non-existent data sources. Due to this, they’ll be able to reduce the number of data items that need to be migrated as migrating duplicates or obsolete reports makes no sense.
It’s why lineage visualization can not only reduce time, effort, and error but also accelerate the whole migration process.
Data Lineage Use Case #2: Identifying the Root Cause of Reporting Errors
BI managers are nearly always called in whenever the sales team and the finance department get into a bit of an argument about deals.
In this case, the BI has to figure out why the sales numbers are different from the finance numbers. Thanks to data lineage, the manager can visualize the entire data flow and identify the root cause and impact analysis within seconds.
With automated data lineage, BI teams don’t have to worry about proving data accuracy in their report. Instead, they can use data lineage to pinpoint the data in question and explain where it came from and if it went through any modifications.
BI professionals can feel confident in their explanation and provide answers quickly, irrespective of whether there’s an error. Even business owners can rest easy knowing all the data is accurate, verified, and understood.
How to Get Started With Data Lineage Implementation
Below, we’ve explained a step-by-step of how you can get started with data lineage in your organization.
Step 1: Get the C-Level Executives on Your Side
Although it’s likely for senior management to approve your data lineage initiatives considering the incentives it offers, ranging from efficiency boost to increased revenue, you must be ready for an opposite reaction as well.
If that’s the case, try to make the board understand how implementing data lineage can improve the quality of your company’s analytics and insights, improving the overall functioning of the organization as a whole.
Step 2: Identify Your Main Business Reasons
Your key business reasons can be anything, including changes in business drivers, data quality projects, or regulatory or audit requirements. Your job is to go through all business records thoroughly and carefully to identify yours.
Step 3: Chalk Out the Requirements of Your Data Lineage Project
For this, you’ll have to select datasets that should be tracked in your team’s opinion and decide the critical elements to include within every set.
Step 4: Decide Which Data Lineage Documentation Method to Use
Generally, the two main methods of documenting data lineage are descriptive and automated. You should try to select one that you think is most relevant according to your organization’s requirements.
Step 5: Pick the Right Data Lineage Software
While it’s true that you can implement data lineage with Excel, having a specialized data lineage tool and application will completely eliminate any manual burden on your IT staff‘s part. Plus, you’ll get access to more robust features that can help you improve the effectiveness of implementing data lineage.
Remember, you have to collect the metadata after every data transformation to capture the data lineage. You can use the metadata collected on each stage and stored in the media data storage for lineage representation down the line.