EU wants to ensure consumers have better access to their data – Copyright AFP/File AMY COOPES
Too many business systems fail because of poor design. One design element that must be taken seriously is data flow. It refers to the understanding of the movement of data through a system consisting of software, hardware or a combination of the two.
Data flows are an important design element for business information systems. Proper data flow means that complications can be avoided for the user. Additionally, a properly sequenced data flow allows data security and data integrity issues to be addressed. In some sectors, such as pharmaceuticals, compliance with data integrity principles is a core requirement of Good Manufacturing Practice (GMP).
The concept of data flow is not new (Tom DeMarco put forward the concept diagrammatically as part of structured analysis in 1979); however, many businesses neglect to map their data sufficiently before purchasing, designing, validating and implementing their technologies.
When developing a new computer system, data flow mapping should be one of the first activities undertaken. This allows the system to be built around optimal data flow. The flow should be a dynamic document, updated as the system is configured. Once the system is established and in use, any updates to the device that may later affect the data flow must be subject to an appropriate controlled change.
A data-flow diagram is the best way to represent the flow of data through a process or system. Such a diagram will provide information about the outputs and inputs of each entity and the process itself. A diagram is a form of flowchart.
In creating such diagrams, simplicity is key. Therefore, for complex systems it is best to avoid doing everything in one dataflow. One, complex data flow will make the data transformation aspect of the computer system design higher, it will also make it difficult to understand and reuse the dataflow. Therefore, it is recommended to break your dataflow into multiple dataflows.
Risk assessment tools can also be useful for understanding data flows and what the different implications are at each stage. A mature data management system adopts a ‘risk management’ approach to all aspects of the quality system.
Checking and rechecking is also necessary, because an incorrect diagram can create serious errors. Examples of mistakes include forgetting to include a data flow or pointing an arrow in the wrong direction.
A key reason why keeping data flow diagrams up-to-date is to address system growth issues. As the amount of data continues to grow, so does the challenge of wrangling that data into well-structured, actionable information. Without clear data flow maps, and where flows are not updated, this leads to situations where analytics become more challenging and computer systems are not challenging enough to capture the data with value.
Another reason why data flows are important is in relation to emerging privacy laws, such as GDPR in the European Union and the increasing number of US states adopting consumer-facing legislation. To comply with such regulations, understanding how data moves around business systems is essential.