ANSWER

Data Flow Summary

From a business or systems analysis perspective a data flow represents data movement from one component to another or from one system to another.  Another way of describing it: data flow is the transfer of data from a source to a destination.  If we get more technical, an ETL (extract, transform, load) process is a type of data flow.

Components of a data Flow

• Data Source(s) - These are the places where the data comes from.  Each data flow has at least one, but it could have many sources of data.  Some of the types of data sources an analyst might encounter include: database tables, XML files, CSV (comma separated values) files, spreadsheets, flat files, 

• Data Mapping - The process of matching fields from the source to the destination.  Some may argue that data mapping is really data transformation but if the data is not being changed but only moved from one structure to another as part of the data flow, it’s just a mapping. 

• Data Transformation(s) - These are the activities which modify the source data before it reaches its intended destinations.  The types of data transformations common in data flows include: merging of data, updating/enhancing the data, and splitting the data.

• Data Sorts - These are processes/logic which change the order in which the data records arrive at the destination.  For example the source table may contain the records in the order in which they were inserted/created but the destination needs them in alphabetical order by a given column.

• Data Filters - Processes and logic which reduces the amount of data so that only the desired data records arrive at the data flow’s destinations.

• Data Joins - The practice of correlating and combining data in separate tables or sources into a cohesive set, enhancing the meaning and richness of data as compared to the data from just once source.  For example if one table contains customer ids and their names and another table contains customer ids and their phone numbers.  By joining the two tables using the customer id as the correlating data element,, one can create a new data set which contains the customer id, customer name, and their phone numbers.  Now I can actually phone the customer and call them by their name.

• Data Destination(s) - These are the places where we want to put the data.  The destination types in a data flow can be as diverse as the source types discussed above.

The data flows vary in type and complexity depending on their purpose/function:

Data Flow Diagramming

One useful tool in understanding the flow of data within a system or an organization is a diagram which shows the key elements (primarily sources and destinations) of the movement of data.  The most common such data is the Data Flow Diagram (DFD) and it’s more specialized form, the Context Diagram.

Here are some useful resources:

• Data Flow Diagram with Examples & Tips

• An Introduction to Data Flow Diagrams

• Verifying Use Cases, Data Flow Diagrams, Entity Relationship Diagrams, and State Diagrams via State Linkages

• What is a Context Diagram and what are the benefits of creating one?

• Introduction to Context Diagrams

• Putting Systems Analysis “Into Context” using the Context Diagram