This article was first published as “Marianne’s Mining Minute” in the APRA Upstate New York chapter’s newsletter, 2012.
The world around us is talking about Big Data and how to use it – see this Wikipedia article for the description http://en.wikipedia.org/wiki/Big_data.
I keep talking to clients and friends who are still struggling with the idea that they have very little data, if any at all, especially if they’re not an academic or healthcare organization. So what does all this mean?
First, Big Data is captured by places like Amazon.com and Facebook – they are watching transactions, including every page or entry that I click a “like” button on and every person that I’m connected to on Facebook. My Twitter reactions are also tracked, along with key words that I write about.
Non-profits are trying to figure out how to capture all of this Big Data and use it. More and more shops are hiring social media staff (Cornell was the first that we know of) to both make a social media presence and to bring in good information. What if your biggest fan is someone who Tweets about your programs, and you don’t know him?
Here’s the issue with that so far: my Twitter handle has no relationship to my ID number in my alma mater’s database. How do we connect all that? And what poor suffering server is going to collect it? (P.S. Cornell is doing it by hand right now.)
Right now, I struggle with what data is really relevant. That someone is hooked up to Cornell in some way – as an event attendee, a fan on Facebook, a volunteer, a donor, an admissions counselor, a current staff member, etc. – matters to me as an analyst. However, I have to live realistically about both what I can get and what I can use.
To look at this another way, consider how much we rely on real estate values as researchers. It’s not that the value of real estate is a reliable measure of wealth. We see that over and over. It’s that real estate values are pervasively available. So we use it because we can get it.
So, what’s a little data shop to do?
I still believe that at the core of our work lies the clarity of audited giving data. However we want to see someone’s engagement, we ultimately measure giving. So any shop that accepts gifts has somewhere to start. After that, it’s a matter of understanding what I have that’s clean (clean is a relative term!) and what I’m trying to know. If I want to figure out whether an event in specific place creates new donors, I just need those event attendees and the audited donor data. I don’t need Twitter feeds, but having whether people got excited on some social media would certainly enhance my understanding of how the message spreads.
Second, I believe that we can append data to our database, and that we often get stuck about which data to append. It happens most often that top management dictates the product based on their gut or on the salesperson’s presentation. The Research team then has to adapt the chosen tool to its needs. I hope that we move to allowing the Research team to figure out the tool. If an organization needs to boost its participation rates, then a screening will not be the most impactful data to buy – instead, the data needed would inform on lifestyle and community involvement. We have wonderful examples of that at work at MIT and at Brown, where Prospect Development is engaged at a high level on understanding prospects.
Where does this leave you? It leaves you with more questions, which is a good thing. What does your organization need? More exposure? More volunteers? More donors? More money? More mavens to spread the word? The answer to that will point you the data you need, big or small.