CoolData blog

11 February 2014

Teach them to fish: A view from IT

Filed under: IT, Training / Professional Development — Tags: — kevinmacdonell @ 5:52 am

Guest post by Dwight Fischer, Assistant Vice President – CIO, Information Technology Services (ITS), Dalhousie University

(When I read this post by our university’s CIO on his internal blog, I thought “right on.” It’s not about predictive modelling, and CoolData is not about IT. But this message about taking responsibility for acquiring new skills hit the right note for me. Follow Dwight on Twitter at @cioDalhousieU – Kevin)

I recently recommended OneNote to a colleague. OneNote is a venerable note-taking and organizational tool that is part of the Microsoft Office suite. I spoke to the merits of the application and how useful and versatile a tool it is, particularly now that it is fully integrated with mobile devices through the cloud. I suggested that she look online and find some resources on how to use it.

Busy as she is, she asked her administrative assistant to look up how to use OneNote, who in turned called the HelpDesk looking for support. The Help Desk staff need to know a lot of information, but software expertise is not the type of thing they can and are able to provide deeper-level support. Unless they were to use the software on a day-in, day-out basis, how could they? As it was, the caller did not get the support she expected.

If that individual instead had gone to Google (or Bing, Yahoo, YouTube, whatever) and asked the question, they would have received a torrent of information. All she needs to understand is how to ask or phrase the question.

  • “Tips on using OneNote”
  • “OneNote quick Tutorial”
  • “Help with OneNote”

It occurs to me that we have provided support to our clients for so long, they have developed an unhealthy dependence on IT staff to answer all their issues. Meanwhile, the internet has developed a horde of information and with it, many talented individuals who simply like to share their knowledge. Is it all good information? Not always, but if you just do a little searching and modify your search terms, you’ll certainly find relevant information. Often times you’ll find some serendipitous learning as well.

We need to help our clients make this shift. Instead of answering their questions, coach them on how to ask questions in search engines. Give them a fish and they’ll eat for a day. Teach them to fish and they’ll eat heartily. And save the more unique technology questions for us.

P.S. I used to go to the bike store for repairs. I could do a lot of work on my bikes, but there were some things I just couldn’t do. But with a small fleet, that was getting expensive. I started looking up bike repair issues in YouTube and lo and behold, it’s all right there. I might have bought a tool or two, but I can darn near fix most things on the bikes. It just takes some patience and learning. There are some very talented bike mechanics who put out some excellent videos.

23 December 2013

New from CASE Books: Score!

Filed under: Book, CoolData, Peter Wylie — Tags: , , , — kevinmacdonell @ 9:39 am

CASE_coverAs the year draws to a close, I’m pleased to announce that the book I’ve co-written with Peter Wylie will be available in January. ‘Score!’ joins a host of fine publications in CASE’s new catalog. I’m looking forward to having a look through this catalog for new books for the office. (‘Score’ is featured on page 12.)

So what is this new book about? The full title is Score!: Data-Driven Success for Your Advancement Team, and as a recent of issue of BriefCASE notes: “Kevin MacDonell and Peter Wylie walk readers through compelling arguments for why an organization should adopt data-driven decision-making as well as explanations of basic issues such as identifying and mining the pertinent data and what operations to perform once that data is in hand.”

You can read the rest of that article here: Ready to Score!?

2 December 2013

How to learn data analysis: Focus on the business

Filed under: Training / Professional Development — Tags: , , , — kevinmacdonell @ 6:17 am

A few months ago I received an email from a prospect researcher working for a prominent theatre company. He wanted to learn how to do data mining and some basic predictive modeling, and asked me to suggest resources, courses, or people he could contact. 

I didn’t respond to his email for several days. I didn’t really have that much to tell him — he had covered so many of the bases already. He’d read the  book “Data Mining for Fund Raisers,”  by Peter Wylie, as well as “Fundraising Analytics: Using Data to Guide Strategy,” by Joshua Birkholz. He follows this blog, and he keeps up with postings on the Prospect-DMM list. He had dug up and read articles on the topic in the newsletter published by his professional association (APRA). And he’d even taken two statistics course — those were a long time ago, but he had retained a basic understanding of the terms and concepts used in modeling.

He was already better prepared than I was when I started learning predictive modeling in earnest. But as it happened, I had a blog post in draft form (one of many — most never see the light of day) which was loosely about what elements a person needs to become a data analyst. I quoted a version of this paragraph in my response to him:

There are three required elements for pursuing data analysis. The first and most important is curiosity, and finding joy in discovery. The second is being shown how to do things, or having the initiative to find out how to do things. The third is a business need for the work.

My correspondent had the first element covered. As for the second element, I suggested to him that he was more than ready to obtain one-on-one training. All that was missing was defining the business need … that urgent question or problem that data analysis is suited for.

Any analysis project begins with formulating the right question. But that’s also an effective way to begin learning how to do data analysis in the first place. Knowing what your goal is brings relevance, urgency and focus to the activity of learning.

Reflect on your own learning experiences over the years: Your schooling, courses you’ve taken, books and manuals you’ve worked your way through. More than likely, this third element was mostly absent. When we were young, perhaps relevance was not the most important thing: We just had to absorb some foundational concepts, and that was that. Education can be tough, because there is no satisfying answer to the question, “What is the point of learning this?” The point might be real enough, but its reality belongs to a seemingly distant future.

Now that we’re older, learning is a completely different game, in good ways and bad. On the bad side, daily demands and mundane tasks squeeze out most opportunities for learning. Getting something done seems so much more concrete than developing our potential. 

On the good side, now we have all kinds of purposes! We know what the point is. The problems we need to solve are not the contrived and abstract examples we encountered in textbooks. They are real and up close: We need to engage alumni, we need to raise more money, we need, we need, we need.

The key, then, is to harness your learning to one or more of these business needs. Formulate an urgent question, and engage in the struggle to answer it using data. Observe what happens then … Suddenly professional development isn’t such an open-ended activity that is easily put off by other things. When you ask for help, your questions are now specific and concrete, which is the best way to generate response on forums such as Prospect-DMM. When you turn to a book or an internet search, you’re looking for just one thing, not a general understanding.

You aren’t trying to learn it all. You’re just taking the next step toward answering your question. Acquiring skills and knowledge will be a natural byproduct of what should be a stimulating challenge. It’s the only way to learn.


4 November 2013

Census Zip Code data versus internal data as predictors of alumni giving

Guest post by Peter Wylie and John Sammis

Thanks to data available via the 2010 US Census, for any educational institution that provides us zip codes for the alums in its advancement database, we can compute such things as the median income and the median house value of the zip code in which the alum lives.

Now, we tend to focus on internal data rather than external data. For a very long time the two of us have been harping on something that may be getting a bit tiresome: the overemphasis on finding outside wealth data in major giving, and the underemphasis on looking at internal data. Our problem has been that we’ve never had a solid way to systematically compare these two sources of data as they relate to the prediction of giving in higher education.

John Sammis has done a yeoman’s job of finding a very reasonably priced source for this Census data as well as building some add-ons to our statistical software package – add-ons that allow us to manipulate the data in interesting ways. All this has happened within the last six months or so, and I’ve been having a ball playing around with this data, getting John’s opinions on what I’ve done, and then playing with the data some more.

The data for this piece come from four private, small to medium sized higher education institutions in the eastern half of the United States. We’ll show you a smidgeon of some of the things we’ve uncovered. We hope you’ll find it interesting, and we hope you’ll decide to do some playing of your own.

Download the full, printer-friendly PDF of our study here (free, no registration required): Census ZIP data Wylie & Sammis.

8 September 2013

Blogging from the Tableau Customer Conference: Why you should care about BI

Filed under: Tableau — Tags: , — kevinmacdonell @ 6:56 pm

I am in Washington DC this week for the Tableau Customer Conference. I’ve mentioned Tableau on CoolData before, and I am a fan of not only the software but the ethos behind the software. Tableau is not a predictive analysis tool, so I don’t write about it much.* But there is a deep and important connection between predictive analytics and business intelligence tools such as Tableau (and Advizor, Spotfire, QlikView …). It’s a connection that has taken me a long time to fully appreciate.

Tableau is a great tool for visualizing your data, and an amazing tool for putting a certain level of analysis into the hands of business users. It can play a role in analysis, for sure, but not statistical analysis or modelling.* So what’s the connection? Well, there’s a connection on two levels.

The first I grasped immediately: Blending predictive model scores with actual results (fundraising results, phone contact results, event attendance results, and so on), for continuous, real-time reporting on model performance post-deployment. End-users wouldn’t get much out of these reports, but I certainly do. (See: Evaluate models with fresh data using Tableau heat maps.)

The deeper connection, the one that has taken me longer to realize, goes like this …

Analytical talent in our sector is as likely, or more likely, to be fostered from within than hired from without. As I see it, the predictive analysts of the future are currently wasting their talents, toiling away at extracting data and reports for end users, often employing Excel in repetitive and error-prone ways. Getting to the point of providing real insights based on data is only a once-in-a-while thing so long as employees are having to spend so much time generating the most basic of reports.

Better tools have arrived, and Tableau is one of them. Let’s start freeing up the creativity and ingenuity of our own employees in the higher education and nonprofit fields.

(BTW, any CoolData readers attending the conference can email me at I would love to learn how your institution is using Tableau.)

* Late-breaking update from the conference: Today (September 9) Tableau announced a range of new features and functionality for all its products in versions 8.1 and 8.2, including integration with the powerful, open-source statistical package R. So much for Tableau not serving up statistics and modelling! New viz options such as box-and-whisker plots will as well add some functionality more associated with stats software.

20 August 2013

A book cover for “Score!”

Filed under: Book, Peter Wylie — Tags: , — kevinmacdonell @ 4:45 am

It has been a long time since I’ve offered an update on “Score!”, the forthcoming book I have co-authored with Peter Wylie. I apologize for that.  I do hope that readers who have known about this project for some time will feel that it is worth the wait. The revised date of availability is sometime this fall. (If you like instant gratification from your work, I would suggest you avoid the world of book publishing.)

We do have a cover image to show you. I like the funky colours.


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