The spreadsheet described here is a textbook case of data trapped in the wrong format. A user has the raw information, which sites connect to which, but the matrix of TRUE, FALSE, and "Diag Fail" values makes it nearly impossible to see the actual network. This is not a failure of effort. It is a failure of the tool.
Traditional spreadsheets treat every cell as an independent container. They are excellent for calculations and linear lists, but they are terrible at revealing relationships. When you have to scan row by row, column by column, looking for the word TRUE, you are not analyzing data. You are performing manual labor that a machine should handle. The user knows what they want: dots for each site, lines for each connection. That is a network diagram. It is a visual representation of relationships that the spreadsheet already encodes. The problem is that the spreadsheet cannot translate its own logic into a picture without significant manual effort or external plugins.
This is where an AI-native approach changes the game. Instead of forcing the user to learn charting tools, write formulas, or copy data into a third-party app, the tool itself should understand the intent. When the user says "I want a diagram of these connections," the software should parse the matrix, identify the TRUE values, map them to nodes, and render the graph. The "Self" and "Diag Fail" entries are not noise; they are metadata that a smart system can either filter or use to flag diagnostic issues. The user should not have to ask how to build the diagram. They should only have to ask for it.
Our opinion is plain: the spreadsheet is a relic when it comes to relational data. The user's request is reasonable, even modest. What they are describing is a standard adjacency matrix, and turning that into a graph is a solved problem in computer science. The fact that it remains a struggle in everyday tools tells you how far the industry has to go. We believe the next generation of spreadsheets will not just store data. They will interpret it, visualize it, and let the user interact with the meaning behind the cells. That is the transformation worth exploring. Stop hunting for TRUE in a sea of text. Start asking your data what it looks like.