I have talked about the importance of individual relationship mapping in my previous blog but what I found most interesting with this whole data visualization business is when I link all the individual maps together into a relationship database. For those of you who are lucky enough to have a good fundraising database, you could perhaps export data from your existing constituency as well.
In this constituency mapping, individuals do not only have first degree connections, but second or third degree connections. The visualization tool I’m using is NodeXL, a free Excel plugin. It works as an excel spreadsheet for the most part but generate a template that visualize the data for you. NodeXL enables users to extract “Sub-graphs” for individuals at different degrees level. See examples below: Figure 1-4 demonstrate prospect’s connections when degree of connection was selected from 1st to 4th degree from a constituency map:
The sub-graph with the desired degree of connections can then be exported to a new NodeXL workbook for further analysis as follows:
Figure 5
We can then see that our prospect has a path to one of our volunteer (labeled in blue) which we won’t be able to find by just doing individual relationship maps. With this visualization tool, the process of mapping a path and generating a relationship map for a complete new prospect (who already exists in the relationship database) can be done within minutes. You can also start with a volunteer and generate a list of individuals that has some degree of connections to him/her and hence provide a list of prospects for that volunteer to review. Suddenly, our confidence in that list of prospects we provide to our volunteer just increased exponentially since we know how the connections could exist. Using sub-graph function is already helping us in planning solicitation strategies as well as qualifying prospects.
For Major Gift purpose, the ability to extract sub-graphs for any individual or organization (or what we call Node or Vertex in data visualization lingo) is for me the most valuable function.
Another interesting function offered by NodeXL is the ability to score each vertex (in our case, our individual or organizational prospect) with metrix that measures their importance in a social network. To understand how this works requires a little background on Social Network Analysis. One great way to illustrate is the Kite graph:
Figure 6
What the Kite Graph tells us is the Degree Centrality, Betweeness Centrality and Closeness Centrality of individual nodes. Here, Diane has high Degree Centrality because she has the most direct connections within her own network. Heather, on the other hand, has fewer direct connections but she is connecting two important constituencies and is the gatekeeper of information flow between Diane’s network and Ike and Jane. Therefore Heather has a high Betweeness Centrality. Fernando and Garth has fewer connections than Diane. However, they have the shortest route to everyone in this network hence grant them higher Closeness Centralities.
In the fundraising world, someone like Diane who has high Degree Centrality could be an industry influencer; someone like Heather could be a connector between industries. NodeXL calculates these metrix for you automatically as added columns in the “Vertex” tab of the Excel workbook as illustrated below:
| Vertex | Degree | Betweeness Centrality | Closeness Centrality | PageRank |
| Dennis | 22 | 62657.347 | 0.000 | 7.343 |
| Diana | 7 | 19574.289 | 0.000 | 2.429 |
| Gordon | 8 | 31976.101 | 0.000 | 2.547 |
Note that it can’t calculate “Closeness Centrality” for some reason. Also aside from Centrality calculation, it also calculates PageRank. “PageRank is a link analysis algorithm, named after Larry Page and used by the Google Internet search engine, that assigns a numerical weighting to each element of a hyperlinked set of documents, such as the World Wide Web, with the purpose of "measuring" its relative importance within the set. Here it calculates the relative importance of an individual or organization within the network” (source: wikipedia http://en.wikipedia.org/wiki/PageRank).
When we are assembling campaign cabinet for a major campaign or looking for board members, we could use this tool to evaluate potential volunteers and their role in various funding priorities.
In the next blog, I'm going to summarize a few problems and considerations I encountered when dealing with relationship mapping as well as reviews of a few commercial relationship mapping sites.
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