Understanding where data comes from, evaluating its quality, and determining its accuracy is critical for organizations that rely on it to run their operations. Data lineage is a record of how data came to a certain location, as well as the intermediate stages and transformations that occurred as it went through business processes, basically, the data’s “line of descent.”
In this article, you will see:
- What is Data Lineage?
- Why is Data Lineage Important?
- Examples of Data Lineage
- Implementing a Data Lineage Strategy
This is part of our extensive data governance guide.
What is Data Lineage?
Data lineage reveals the data life cycle by displaying the entire data flow from beginning to end. This lineage covers all the data’s changes, including how the data transformed, what changed, and why.
In a nutshell, data lineage enables businesses to do the following:
- Keep track of data processing errors.
- Exact changes to the process with less risk.
- Complete system migrations with certainty.
- Construct a data mapping framework, combine data discovery with a comprehensive metadata view.
Why is Data Lineage Important?
The entire organization may benefit from data lineage from IT to the business. Organizations can use these data lineage advantages:
Improved Data Comprehension and Confidence
The business user benefits from data lineage because it provides the required context for an organization’s data. The source of your data, how data sets get produced and aggregated, the quality of data sets, and any alterations along the data journey are all displayed in data lineage. This improvement ensures that company decisions are based on accurate, comprehensive, and trustworthy data.
Spend Less Time Manually Conducting Impact Analyses
When making a data update, data lineage allows IT teams to undertake impact analysis at a granular level, allowing them to see any changes to downstream systems. This feature eliminates approximately 98% of the time spent by IT on manual analysis.
Comply with Rules and Regulations
Data traceability for regulatory reasons is difficult to map. It can take a long time, and if done incorrectly, it can lead to fines and penalties. Data lineage assists the Risk Management and Data Governance teams by documenting how data moves through various systems from source to destination and allowing risk management to observe the audit trail for all data transformations.
Examples of Data Lineage
A few standard strategies for data lineage on strategic datasets are listed below.
From the moment of entry into the BI ecosystem to reporting and analytics, cross-system lineage delivers end-to-end lineage at the system level. This form of lineage gives you a high-level view of the data flow, showing you where data comes from and where it goes.
The following are some of the most common applications for cross-system lineage:
- Projecting the impact of a process modification
- Examining the consequences of a faulty method
- Finding parallel processes that do the same thing
- Visualizing data flow at a high level
End-to-End Column Lineage
Or the bigger picture, cross-system lineage is ideal. From the moment of entry into the BI environment to reporting and analytics, the end-to-end column lineage details column to column-level succession between systems.
The following are some of the most common applications for end-to-end column lineage:
- Impact analysis of a change to a column in the source system
- Conducting root cause analysis to find the source of reporting inaccuracies
- Data flow visualization at the column level
- Preparation for a regulatory compliance audit
You may need to delve even further into the intricacies of a given system on occasion. The column-level lineage within an ETL process, report, or database object gets detailed in the inner-system lineage. No matter how complex the process, report, or object is, knowing each column’s logic and data flow enables visibility at the column level.
The following are some of the most common applications for inner-system lineage:
- Showing the logic of a report, ETL, or database object data flow can be shown
- Identifying and locating dependencies within a report
Implementing a Data Lineage Strategy
There comes a time when it makes sense for every BI team to be more systematic about their data lineage. Whenever BI teams believe that it is time, chances are, it is truly time to put a data lineage solution in place.
The following is a step-by-step guide on how to do just that.
Step 1: Determine Your Priorities and Use Cases
The first step in systematically implementing data lineage is to determine how you presently use or want to use data lineage. In this regard, you might consider the following:
- Impact analysis
- Root cause analysis
- Regulatory compliance
- Business insights
Step 2: Obtain Management Approval
It is time to go to management and acquire the green light for data lineage solutions once you have clarified how to use them. Make sure you are ready. Explain the function of a data lineage solution in each use case and how deploying it will save the firm time and money.
Step 3: Look into Data Lineage Options
Once you have received the management’s approval, it is time to select the best option for your firm. Allow your priorities and use cases as the initial criterion for weeding out the available technologies. If you are primarily concerned with answering questions from business users and auditors, the tool’s speed may be more significant. Look into an agency specializing in obtaining insights for planning future business strategy if that was your major use case.
It is also a good idea to think about the following factors:
- What technologies are compatible with it?
- Is it designed to work best in a particular BI environment?
- What data lineage dimensions does it display?
- How easy is it to use?
Step 4: Pick a Solution and Put it into Action
But once you have gotten over that hump and implemented the best data lineage procedure for your needs, the real fun begins. Prepare to reap the benefits of a Data Lineage implementation.
Data is growing exponentially, making it even more difficult to track its origins and how it has evolved. As a result, monitoring data lineage is an important part of building a fully data-intelligent company.
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