When Project Evident begins an engagement, one of the first activities we engage in is creating a data map. A data map is a simple tool that serves several purposes:
It usually turns out that no one at the organization has a complete view of the existing data systems, and that collecting this information is a valuable exercise in itself. Presenting a complete picture of the data flow gives management a new appreciation for the data team, and is a helpful way to get everyone on the same page in strategizing around the data processes.
A data map is a process flow diagram, a visual representation of different steps in how data is processed. The example below show a data map for a simple program. We usually arrange the diagram in columns left-to-right, beginning with data collection, then storage, processing, and reporting.
Project Evident uses lucidcharts to make create data maps, but you can use Microsoft Visio, draw.io (free!), or others to make your own diagrams. Slide-making apps like PowerPoint or Google Slides work too, but you are encouraged to try one of the options above because of the simplicity and functionality they offer. There are a few common conventions to follow for data maps (inputs are ovals, databases are cylinders, reports have the wavy bottom). You can read about common symbology here, but don't get hung up on technicalities – it's better to simply jump in!
Organizations of different sizes will have different needs for data maps – create them at the level that you think will be most helpful. For some, this will be a single, org-wide data map focusing on the big picture. For others, it may make sense to make detailed data maps for individual programs.
The data map is a jumping off point to strategize improvements for your data system. Annotate your data map calling out pain points, such as labor-intensive steps that could be improved or even automated by creating or optimizing connections. Some specific questions to think about:
Not all answers to these questions are problems that need solving immediately. Awareness of weak links in your data architecture can help you plan improvements. Separating needs into the Collection/Storage/Processing/Presentation categories can help align thinking about solutions as different software solutions exist in each of these categories.