Data Lineage vs Data Flow Diagrams: Demystifying Them for Effective Data Management
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Data lineage reveals where data has come from and how it has evolved through its lifecycle. On the other hand, Data Flow Diagrams (DFDs) are graphical representations of the “flow” of data through an information system.
In this blog, we will see the biggest differences between data lineage vs. data flow diagrams.
Table of contents #
- Decoding the power of data lineage and data flow diagrams
- Data lineage vs data flow diagram: Comparing them in a tabular view
- Data lineage vs. data flow diagrams: Understanding them better with use cases
- Summary
- Data lineage vs. data flow diagrams: Related reads
Unraveling the data puzzle: Exploring data lineage and data flow diagrams #
Let us first understand what is data lineage.
What is data lineage? #
Data lineage traces back to the sources from where the data was derived and the transformational steps it went through. It provides a clear visual flow and contextual information for each step helping the user understand the entire data process from source to destination.
It helps provide visibility into the analytics pipeline and simplifies efforts to trace errors back to the root cause in a data analytics process.
Data lineage is crucial in diagnosing data-related problems, understanding how changes in upstream data can affect downstream applications and reports, and ensuring data governance, privacy, and compliance.
Now, let us better understand data flow diagrams.
What are data flow diagrams (DFD)? #
DFDs can also be used for the visualization of data processing (structured design). They provide a detailed view of how data is inputted, processed, stored, and outputted in a system.
They help to identify how the system functions, and how data moves and transforms within the system. However, DFDs do not usually capture the detailed history or transformations of a particular data element, unlike data lineage.
While both data lineage and data flow diagrams aim to improve the understanding and management of data within a system, they offer different perspectives and serve slightly different purposes:
- Data lineage is more about tracking the history and lifecycle of specific data elements in the system. It’s like a detailed “biography” of the data, focusing on “what has happened” to the data.
- Data flow diagrams, on the other hand, are more about understanding the overall structure and operations of the system. They are like a “map” of the data, showing “how things work” within the system.
Both data lineage and data flow diagrams are important. Understand the overall flow of data (DFDs), but keeping track of specific data elements’ history and transformations (data lineage) for troubleshooting, impact analysis, and compliance is critical for effective data management.
Data lineage vs data flow diagram: Comparing them in a tabular view #
Now, here’s a tabulated comparison between data lineage vs data flow diagrams:
| Data Lineage | Data Flow Diagrams | |
|---|---|---|
| Purpose | Tracks the life-cycle of specific data elements, providing a history of their movement, transformation, and use in the data system. | Provides a visual representation of how data moves, is transformed, and processed in the system. |
| Focus | Primarily concerned with ‘what has happened’ to the data. It provides a ‘biography’ of the data. | Mainly focuses on ‘how things work’ within the system. It serves as a ‘map’ of the data. |
| Details | Provides granular detail of transformations, processes, and movements a specific data item has undergone. | Details the general path and processes that data undergoes within the system But, it may not capture specific transformations for individual data items. |
| Used for | Diagnosing data-related problems, tracing errors back to the root cause. It ensures data governance, privacy, and compliance, performing impact analysis. | Understanding how the system functions, how data moves and transforms within the system. It identifies possible bottlenecks or vulnerabilities in the data processing. |
| Representation | Data lineage could be represented as a graph where nodes represent entities (data, processes) and edges represent relationships (movement, transformations). | Usually depicted as a network of “black boxes” (processes), data stores, and data flows. |
| Relationship to the other | Data lineage is part of the broader view given by a data flow diagram. It provides more detail on the individual transformations and movements that take place within the processes illustrated by the DFD. | A data flow diagram provides the structure in which data lineage exists. It gives an overview of where and how data lineage takes place within the system. |
In a nutshell, data lineage and data flow diagrams are intertwined. They are different perspectives of the same system, each providing crucial information for understanding and managing your data effectively.
Data lineage vs. data flow diagrams: Understanding them better with use cases #
To differentiate between data lineage and data flow diagrams further, let us understand their applications in different scenarios and domains:
1. Data governance #
Data lineage plays a crucial role in data governance. It ensures transparency in processes and enables organizations to track how data gets transformed and moves across the system.
This becomes even more essential with regulations like GDPR and CCPA, where you need to know where all your data comes from and how it’s being used.
2. Data quality #
Data lineage helps track data quality issues back to the source, making it easier to resolve them. If a quality issue arises in a report, for instance, you can trace back through the lineage to find where the problem originated.
3. Impact analysis #
With data lineage, you can see what would be impacted by a change to a particular data element or system. This helps when considering system upgrades or changes, or when troubleshooting issues.
On the other hand, data flow diagrams are extremely useful in:
1. System analysis and design #
DFDs help system analysts and designers understand how data moves and is transformed within a system. This is crucial when designing new systems or when trying to improve existing ones.
2. Communication #
DFDs serve as a good communication tool between various stakeholders (e.g., business users, system analysts, developers) since they provide a visual representation of how the system works.
3. Identifying bottlenecks or vulnerabilities #
By mapping out the entire data flow, you can identify areas of the system that are slowing down the process, causing inefficiencies, or are vulnerable to security threats.
Understanding these use cases will help you decide when to use data lineage or data flow diagrams. Remember, they are not mutually exclusive and are often used together to get a holistic view of the system’s data landscape.
In addition, you might want to explore tools that help with creating data lineage and data flow diagrams. Some data catalog tools, data governance solutions, and ETL tools provide features for tracking data lineage and creating data flow diagrams. Understanding these tools will also help you better differentiate between the concepts and their practical applications.
Summary #
While both data lineage and data flow diagrams aim to improve the understanding and management of data within a system, they offer different perspectives and serve slightly different purposes:
- Data lineage provides a granular view of data transformations and is key for tasks like impact analysis, troubleshooting, and compliance.
- Data flow diagrams, on the other hand, offer a broader view of how data is processed in the system as a whole, which is valuable for system design, communication between stakeholders, and identifying potential system issues.
Both data lineage and data flow diagrams are interconnected and often used together to provide a comprehensive understanding of a system’s data landscape. Various data governance, data catalog, and ETL tools provide functionalities to track data lineage and create data flow diagrams, which are beneficial for effective data management.
Are you evaluating automated data lineage solutions for your organization? Learn more about Atlan and how it helps automate data lineage and deploy best-in-class data governance without compromising on data democratization
Data lineage vs. data flow diagrams: Related reads #
- 5 best open-source data lineage tools to consider in 2023
- The 6 biggest benefits of data lineage in data governance
- Understand your data better with automated data lineage
- Data lineage for Snowflake and BigQuery
- Types of data lineage: Understand all ways to view your data
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