Digital transformation has made a significant change in the way businesses operate.
Business data and its utilization are considered to be one of the most crucial aspects of any business and its digital presence. The evolution of big data has transformed data management.
With the implementation of compliance laws such as GDPR & CCPA, it is paramount to keep track of data sources and hygiene.
We will be comparing two of the most important techniques in data management, “Data Lineage and Data Provenance” in this blog. Let’s start with brief info on the two.
What is Data Lineage?
Data lineage is the process of maintaining the journey of data from its originating sources to its ultimate destination.
Data lineage is beneficial in keeping track of data usage and maintains data hygiene and best utility practices.
In short, it provides you the data lifecycle management overview.
What is Data Provenance?
Data provenance is the historical tracing of data from its originating source to its final stage. And the scope of data provenance goes beyond that with the following factors:
- Factors influencing data initiation
- Data sources
- Input methods through which data entered the system
Data provenance is useful in maintaining data hygiene and data compliance.
In short, data provenance specifically focuses on data sources and their stages.
Data Lineage vs. Data Provenance: Difference Explained Across Various Parameters
We can see over the years; Data Lineage has been more popular than data provenance in Google search terms.
The key goal of a data lineage tool is data lifecycle management right from the data origination to the data exhaustion.
On the other hand, the key goal of data provenance is to specifically track the data origination and segregating data in three key stages. These stages are data-in-motion, data-in-process, and data-in-rest.
The key components of data lineage include a web portal, data capture sources, and data nurture methods. These components also include data qualification systems, CRM systems, and an ERP system.
While on the other hand, the key components of data provenance include all the data lineage components and some more. These additional components are tracking the capture sources and data input methods.
Key challenges of data lineage include managing large volumes of data. It also includes maintaining data lineage, tracking cross channels, and unifying disparate promotional systems.
While the key challenges of data provenance include the data lineage challenges and few more. The additional challenges include large and complicated workflows, and reproducing the execution for data retention.
Also Read: Data Provenance Challenges
Data lineage tools are more sophisticated in nature and help you to submit data for regulatory compliance, whenever required readily.
On the other hand, data provenance tools are less sophisticated, and it is a little difficult to produce mandatory compliance data readily.
Some of the key data lineage tools include:
- Talend Open Studio
- Jaspersoft ETL
- ASG Data Management
Some of the data provenance tools include:
- Linux Provenance Modules
- Open Provenance Model
Most of the data lineage and data provenance tools are open source tools, and you can customize them as per your requirements. But, there are a few paid options in the market too.
Typically, data lineage tools offer an annual subscription or user-based pricing model. Though, for detailed costing, you will need to contact the vendors individually.
Typically, the data provenance tool also comes in the term-based pricing model and a user-based pricing model. Just like data lineage tools, you will need to contact the vendors for a detailed quote separately.
Tabular Comparison between Data Lineage & Data Provenance
Even though the terminologies data lineage and data provenance sound very similar, there are a few key differences in both.
In simple words, we can conclude that a data provenance system is a combination of data lineage and input sources, input methods, and channels.