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:

• Cataloging organizational data assets.
• Visualizing how data is collected, stored, processed, and presented.
• Helping to identify inefficiencies – weak links, isolated systems, duplicate processes, underused resources.

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.

Making a data map

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!

1. Begin by listing all the ways you collect data. This may include surveys, intake forms, referrals, session notes, etc. Group these by type and method of collection, and add them to the left side of your data map.
2. List the places data is stored. Maybe you have a database, or shared hard drives, or maybe you rely on storage from a provider (e.g., the surveys are stored by Survey Monkey, and you download them to be analyzed or to be part of a report). Add the storage systems to the second column of your data map, and draw a line connecting each piece of collected data to where it is stored.
3. List the ways data is reported. Regular internal reports, board reports, performance summaries, dashboards, etc. Put those on the far right side of your data map.
4. How does data get from storage to the reports? If there is intermediate software or applications for analysis, like Excel, Salesforce Lightning, Tableau, SPSS, etc., include these tools between the storage and the reporting, and then, once again, connect the dots, drawing lines from storage to reports, through the analysis tools, when applicable.

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.

Using your data map

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:

1. What are the most time-consuming links in the data process?
2. In each section (collection, storage, processing, presentation), are there underutilized resources? Are there are reports that could be broadcast more widely, a particular data source that could be included in reporting, or an effective processing tool that is only used for a few tasks that could be more widely utilized?
3. Are there unneeded resources? Are you doing work importing data from a source that never makes it to the presentation, or doesn't add much value? Are you doing the same data cleaning process with multiple tools (and maybe in slightly different ways, introducing inconsistencies)? Are some of your reports duplicative?

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.