Organisations have information stored in different places. These may include hardware infrastructure (e.g., a local server, a hard drive and other storage on a PC/laptop), software programmes and apps (e.g., information in a spreadsheet or database), and maybe even physical files on paper. Data mapping is like creating a roadmap that shows what information is located where. Different units or processes in an organisation will have different types of information in different places and it is crucial for an organisation to track and understand the processes not only within the context of the business unit/process but also within the context of the whole organisation.
The concept of data flows refers to how information moves or gets transferred from one place to another. Data flows are like tracking the journey of your information as it moves through different processes or systems. It's like watching how data travels from one point to another, showing the path it takes. Understanding data flows helps you see the entire process of how data is moved, transformed, or updated.
While data mapping is about creating an inventory that maps information in different locations, data flows show the journey of information as it moves through various processes or systems. Together, they help ensure that information is accurately and efficiently managed across different parts of an organisation and its system.
What data mapping methods are out there?
Data mapping can be performed with digital tools or simply by manually drawing or documenting the locations and systems. Organisations that choose to use technical tools may use various techniques to map data:
1. In manual data mapping, users manually code the links between the data source and destination architecture. Although this method is still a practical mapping methodology for small organisations, where the database is modest or not very complicated, it is generally deemed ineffective for large businesses operating in contemporary commercial environments due to the vast quantity of data presently accessible.
2. The semi-automated data mapping method necessitates requires the user/team to switch between conventional manual and automated information/data mapping methods. Data mapping software builds a link between the data sources, and an IT specialist manually adjusts the connections as required. This method is beneficial if your company is working on a limited budget. When dealing with a small quantity of data and looking to execute various migrations, integrations, and transformations, it is also recommended to employ this strategy.
3. In the automated data mapping methods, a tool – often drag-and-drop – handles all aspects of the data mapping. All procedures are performed by software, which eliminates the need for a programmer and enables the mapping of more extensive datasets.
4. The schema mapping method is a semi-automated technique that uses software to link comparable schemas together with little manual effort. To create links, the program compares the sources of data and the destination schema. Then, a developer examines the design/map and makes any necessary revisions. After the data map is finalized, the schema mapping software creates the code (often in C++, C#, or Java) to add the info.
When using data mapping tools, the metadata (or, data about data) may be located on-site or in a cloud storage, and may use proprietary or open-source tools. All these aspects have privacy risks that should be considered, such as who can access the information.