Every online store generates a steady stream of information. Customer browsing behavior, cart activity, payment and shipping details, product inventory levels, supplier updates, and marketing analytics all circulate through different systems.
That’s a lot to juggle. If even one flow breaks down, it can ripple through your entire operation, leading to delays, inaccurate inventory counts, or a poor customer experience.
Data flow diagrams, or DFDs, help you cut through the noise by mapping out how information enters, travels through, and leaves your organization. Instead of guessing where a failed order update went wrong or how customer data gets from your storefront to your customer relationship management (CRM) software, you can follow the path step by step.
Here’s how DFDs work, what they represent, and why they can help.
What is a data flow diagram?
A data flow diagram (DFD) is a visual representation of the data passing through an information system. It illustrates how your company collects, processes, stores, and delivers data. A DFD gives teams a shared understanding of how information flows between departments, software systems, and stakeholders, simplifying complex systems for analysis and improvement.
For example, a retail company might use a DFD to see how customer order data moves from online checkout to warehouse, billing, and shipping systems. Similarly, a financial services firm could create a DFD for a clear overview of how client payment data moves throughout its entire system—including various verification, approval, and reporting stages—to ensure compliance and data integrity.
DFD vs. flowchart
A DFD shows how information moves through a system: where data comes from, where it goes, how it’s stored, and how it transforms. You might use a DFD if you’re tracking a customer’s order information through checkout, fulfillment, and reporting systems, for instance.
A flowchart, or flow diagram, on the other hand, is a diagram that maps sequential steps and decisions in a process. It shows the actions people or software take—like “route to warehouse” or “send confirmation email”—and the decision points that guide what happens next. This is more useful if you want to understand the workflow itself, rather than the data that will move through that workflow.
Here’s a more thorough breakdown:
Similarities
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Process mapping. Both DFDs and flowcharts analyze and document how work or information moves within business systems.
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Sequential logic. Both models use arrows to show the direction of movement, documenting a logical flow between system components.
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Documentation. Both are used for system design, training, and audits, to help stakeholders understand complex software system processes.
Differences
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Main focus. DFDs focus on where data comes from, where it goes, and how it transforms through that process—in other words, it accounts for what the system does with data. Flowcharts focus on the sequence of steps, decisions, and logic—in other words, how the system performs a given task.
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Labels and symbols. DFDs label processes (circles or ovals), external entities (rectangles), data stores (open-ended rectangles), and data flows (labeled arrows). Flowcharts label processes (rectangles), decisions (diamonds), start and end points (ovals or terminals), and inputs and outputs (parallelograms).
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Use cases. DFDs define overall system functionality, including the information requirements and the system’s scope. For instance, a DFD might track the movement of a customer order record from its entry to storage and final output. Flowcharts focus more on specific technology, operational sequences, and programming logic. As one example, they might map “if/then” logic for verifying a customer’s credit card during the order process.
Components of a data flow diagram
A DFD has four main components, each represented by a specific symbol to help visualize how data moves and changes within a system:
External entity
An external entity—also known as a terminator, source, or sink—is one outside the boundaries of the system. It either supplies data to the system or receives data from it. External entities are the data’s initial sources or final destinations.
Use rectangles or squares, labeled with nouns, to represent these external entities. Examples include “Customer,” “Bank,” “Supplier,” and “Inventory System.”
Process
A process—or transformation—shows how data changes within the system once it has been input. For instance, it shows how you might take credit card information from the point of being entered by a user to the step of verifying it. Symbolize these processes with circles or rounded rectangles, and label them with verbs to describe their action, like “Verify Order” or “Calculate Total.”
Data store
A data store is where you hold or store a data file for a period of time. Data stores represent “data at rest,” from where it can later be retrieved. This is usually a database, file, or manual ledger. Use an open-ended rectangle (a rectangle missing one side) labeled with a noun like “Customer Records,” “Product Inventory,” or “Order History” to represent this part of the process.
Data flow
This represents the movement of information from one part of the system to another. Mapping data flows means showing data passing between a process, an external entity, or a data store. Label arrows showing the direction of data movement (e.g., “Order Info” or “Payment Data”) to signify data flows.
Data flow diagram rules
Data flow diagrams follow a set of general guidelines to maintain logical consistency and prevent modeling errors. Here are the essential rules to follow when you create data flow diagrams:
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Data must move via a process. In the same way you can’t move a box from one storage room to another without picking it up and carrying it, data can’t just jump from one place to another on its own. For example, data can’t go directly from a “Customer” (an external entity) to an “Order Database” (a data store) without a “Process Order” (a process) step in between.
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A process must have both an input and an output. Every process must transform data. Incoming data can come from multiple sources, and sometimes output flows to multiple entities or stores. Each process has at least one input and at least one output.
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Each data flow needs clear labels. Arrows need to have meaningful names that describe the data being transferred (e.g., “Customer Information,” “Invoice Details”). Use clear, functional names with obvious meanings to your stakeholders. Use nouns for external entities, data stores, and data flows, and use verbs for processes.
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The entire diagram should maintain logical consistency. Inputs, outputs, and processes should make sense together. For instance, you can’t output a “Payment Receipt” if no payment data enters the process.
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The DFD should be clean and easy to read. Keep the diagram visually clean and organized so data relationships are easy to follow. Avoid crossing lines whenever possible so readers can quickly look over the DFD and see the system design and data transformations throughout.
Logical DFDs vs. physical DFDs
A DFD can take one of two forms: a logical data flow diagram and a physical data flow diagram. Logical DFDs and physical DFDs both illustrate how data flows through a system, but from different perspectives.
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Logical DFD. A logical DFD focuses on what happens to the data involved—the business processes, inputs, and outputs. It’s a high-level overview of system requirements and processes, without concern for how those processes are implemented.
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Physical DFD. A physical DFD shows how the system is actually built, detailing the hardware, software, databases, and users that perform each function.
Put simply, you use the logical DFD for a broad overview of how the system functions, and the physical DFD to work through the implementation details.
For example, if your ecommerce store creates a logical DFD, you might see a process labeled “Process Order” connected to data flows like “Customer Information” and “Payment Details.” This diagram focuses on what data is necessary and what results will be output.
If you created a physical DFD, that same process might be broken down into real-world components—such as an online checkout form, a payment gateway, and a MySQL order database—showing where each action takes place.
How to make a data flow diagram
You don’t need to be a computer scientist to make a data flow diagram for your organization. Here are five simple steps for creating a DFD from scratch:
1. Identify the main functions and data flows you want to map. Start by determining the major processes, business events, and data entry points in your system. Identify the trigger actions, what information is collected, and where it goes.
2. Define your key DFD components. Map out processes, data storage solutions, data flows, and external entities that send or receive data. Each single process should represent a distinct transformation or activity.
3. Start with a context diagram. Begin with a high-level DFD, known as Level 0 or context diagram, that shows how the system interacts with external actors. This gives you a structured view of how the system fits into your organization or the surrounding business environment.
4. Deconstruct into lower-level diagrams. Break the main functions into smaller, more detailed processes (called Level 1 or Level 2 DFDs), going from a broad overview to more granular process models.
5. Verify data connections. Check that each data flow connects logically between processes and data stores, and that all components follow DFD rules (e.g., data flows are uniquely named, all processes have inputs and outputs). Remember that DFDs don’t map control flow or decision logic; those belong in flowcharts, not DFDs.
Benefits of using a data flow diagram
Data flow diagrams are versatile tools that provide a clear, logical, and graphical representation of information movement, making complex systems understandable to a wide audience. Here are the advantages of leveraging DFDs:
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Clear visual overviews of major processes. A DFD is an intuitive interface for understanding how data moves through an information system—most commonly a computer system—making the data easier to grasp, even for readers without a technical background.
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Breaks complex systems into manageable parts. By breaking multiple processes into smaller components, a DFD can help analysts model system architecture and identify inefficiencies or redundancies.
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Helps clarify data origins and destinations. Bird’s-eye level context diagrams can provide a basic overview of where data enters and exits a system. This can help teams track how external entities provide data, as well as how systems both store and receive data.
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Facilitates better communication in agile development. DFDs can be modified. Because of that, they fit into agile development workflows, where they can help teams refine requirements before committing time and resources to actual implementation.
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Allows analysis from multiple perspectives. You can use DFDs for a top-down approach, creating a high-level single process that represents the entire system and its main functions. You can then break this into lower-level diagrams that show business events and processes in detail. This zooming in and out can help understand inefficiencies and identify how changes in one process will impact other workflows.
Data flow diagram FAQ
What is a data flow diagram?
A data flow diagram (DFD) is a visual tool that shows how data moves through a system, illustrating where information comes from, how it’s processed, and where it’s stored or sent.
What are the four components of a data flow diagram?
The four components of a data flow diagram are external entities (data sources or destinations), processes (transformations of data), data stores (where you hold information), and data flows (the movement of data between components).
What are the four steps of data flow?
The four steps of data flow are data input, data processing, data storage, and data output, representing how information enters, moves through, and exits a system.
What is the purpose of DFDs?
The purpose of DFDs is to visually represent how data moves and is processed within a system. This empowers businesses and developers to undertake a structured analysis of their organizations, understand information flows, and improve internal processes.
