By Steve Grimes, director of development analytics and strategy, Jazz at Lincoln Center
I hate being late to the party. In a literal sense, being late means the good food is gone, prime seating is occupied and you’re slotting yourself awkwardly into conversations you have little context to. If you’re anything like me, you understand that being early is the way to go. You’re able to fill up on prime appetizers, stake a claim to the best spot in the house and be the harbinger of conversation, rather than a spectator.
I try to follow this mantra in my professional life, also. Imagine my disappointment when I learned my organization had a very influential, philanthropic billionaire attend a concert we held at Jazz at Lincoln Center a few years ago. In this case, being late to the party meant we were not in a position to make this patron’s concert experience the best it could be. More importantly, we lost an opportunity to immediately engage that patron as a possible donor. We were late to the party. We would now try to engage this prospect when the experience with our organization (good or bad) had gone cold.
I asked myself, “How can I put our organization in a position to not have this happen again and be early to the party?” Simple — by cultivating a list of high-net-worth individuals and creating alert triggers in our CRM, so when they purchase a ticket or engage with us in any other way, we know immediately and can act accordingly. There are great vendors out there that can provide that information, but all I wanted were names of high-net-worth individuals. Forbes was a good source, but they were limited in their names and the format of how they provided that information was arduous for my purposes.
Something out there had to exist that provides an exhaustive list of high-net-worth individuals, in a format that allows me to upload that information to our CRM in an efficient way. Just my luck, there was! In my search, I came across the world of open data.
What is Open Data?
The Open Knowledge Foundation, a nonprofit dedicated the global distribution of open data, defines open data as “data that can be freely used, re-used and redistributed by anyone - subject only, at most, to the requirement to attribute and share alike.” This has born a movement from governmental agencies, companies, organizations and civilians to provide data and share the insights from that data for all sorts of things.
For example, data.gov provides data sets on all matters of topics such as agriculture, education, manufacturing, public safety and so on. From an analytics perspective, these data sets are super useful when one needs to append a personal data set with additional information, such as using Census data to append geodemographic attributes at the record level within a data set.
So, how does this help in our day-to-day duties as prospect researchers? Beyond the access to interesting databases such as the Consumer Complaint Database, U.S. Hourly Precipitation Data and the Lottery Mega Millions Winning Numbers, open data sources provide access to federal and state political contributions, legally owned businesses, property valuations and of course, a nice list of global billionaires.
Using Open Data: A Case Study of Billionaires
I heard a great analogy once of how data is the new oil. Not solely because of the value that both data and oil have, but because both need to go through involved processes to provide that value.
For example, oil needs to go through a ton of chemical procedures to be usable. For data, data cleaning and manipulation is usually necessary before the information can be utilized. The value that both offer comes from how you are able to change its original makeup for your needs. The following reviews the data cleaning and manipulation needed to take advantage of the information in the global billionaire data set.
Depending on your state’s available open data repositories (not to mention the data available at the federal level), you will likely deal with a large amount of information in different formats that will have to be cleaned, depending on your needs. Programs like R, Python and even SPSS are great for dealing with this level of data. If you are not familiar with those programs, however, the learning curve can be steep. It may not be worth the time if you are looking to do a quick one-off project with open data.
As accessibility is concerned, Excel is the next best tool to clean and utilize data from these repositories. For larger data sets, I suggest looking into Excel’s Power Query Editor. This option can be cumbersome with files containing more than 300,000 records, but fortunately, the data set that includes the world’s billionaires only contains 2,600-plus records. The cleaning and manipulation of this data within Excel should not be resource heavy.
Click here for a step-by-step tutorial and video showing how I have approached cleaning and using this data set for my needs at Jazz.
As the brain trusts of our departments and organizations, we have to be constantly vigilant toward researching as many aspects of our constituent pool as possible. We also need to be inventive in how we can provide the same value to our respective organizations with minimal cost. The availability of open data provides an excellent avenue toward both. With open data, the possibilities are endless to supplement our current wealth screenings with additional information and, in some cases, forgo them completely.
The case study I presented here is once such way to go about this. While it may take some fussing around with Excel, R, Python or SPSS to get the records in a way that works for your needs, the return on that investment of time is worth it just to not be late the party.
This article relates to the Data Science domain in the Apra Body of Knowledge.
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