CoolData blog

12 May 2011

Good with numbers

Filed under: Data, Training / Professional Development — Tags: — kevinmacdonell @ 11:59 am

Are you “good with numbers”? Occasionally someone will give me credit for having that skill. Sometimes they’re offering a compliment, and sometimes I think they’re excusing themselves from needing to understand what I’m trying to say. “Predictive modeling? Ha ha, sorry mate, I’m not good with numbers.”

The truth is, although I’m not afraid of numbers, I’m no whiz. I imagine number people are good with Sudoku puzzles — I don’t like the look of them and have never even tried one. Simple math is not a favourite activity either; when I play any kind of game that requires keeping score, my wife does it — I’m just too slow. I also have no memory for numbers. I know my Phonathon stats pretty well, but if it’s something I don’t have my hands on every day, such as our overall totals in Annual Giving or our capital campaign figures, it’s in one ear and out the other. I’m easily impressed by people from other institutions who “know their numbers.”

I’m having these thoughts as we sift through the Annual Giving results from the fiscal year just ended, trying to nail down what happened, not just overall (which we already know) but split along important distinctions such as donor status (renewed vs. acquired) and solicitation method (mail, phone, etc.).

Important work, to be sure. But getting the sum of the last column to agree with the sum of the last row is one of my definitions of Hell.

I’ve written about the difference between “everyday” data and “insight” data. Year-end annual fund reporting falls squarely in the “everyday” category. By everyday I don’t mean trivial. “Everyday” means immediate — the forms and usages of data that are most familiar, the current data used for today’s purposes. Everyday data must meet a high standard: if it’s not up to date, has missing values or is in any way “messy,” then it’s invalid. If the sum of the last column fails to agree with the sum of the last row, there’s a problem somewhere, and you can be expected to beat your head against the keyboard trying to figure it out.

“Insight” data — that’s different. It may be messy, full of holes and old, but that’s okay. When I’m cobbling together a dependent variable for a Phonathon giving model, I don’t fret over whether I’ve managed to capture every last dollar from every last donor who ever gave in response to phone solicitation. I do my best to put together a decent variable that looks like a reasonable representation of “giving by phone” and leave it at that. Striving to meet the standards of an accountant in Finance is not going to make one bit of difference in a model that assigns a score between one and ten.

Yes, there are ways to make serious errors in predictive modeling, and I am not careless. But what is most important, what excites me, is not the numbers and the math. What excites me is the search for patterns and tendencies in a swirl of messy data that to other eyes appears formless, and turn those patterns into something actionable. It’s my understanding that x-rays and other forms of medical imaging are difficult to interpret — it takes skill and a knowledge of what to look for, and it’s something no machine can do. Data mining is a bit like that.

Are you good with numbers? If you are, I admit you’ve got a leg up. The question is, are you also good with meaning?



  1. Actually, Sudoku puzzles don’t require you to be “good with numbers”. You should give them a shot.

    The numbers are merely markers, with no mathematical significance. You could use nine different letters, symbols, or colors and the puzzle would work essentially the same way.

    Now KenKen puzzles, on the other hand…


    Comment by JJ — 12 May 2011 @ 4:44 pm

  2. Excellent post.

    I loved reading your honesty about Sudoku and some numbers going in one ear and out the other and being impressed by people that “know their numbers”. I can empathize with you on all fronts, and I think most people would tag me “good with numbers”. I always felt like I was failing in some regard for not memorizing all those aggregates that some folks seem to effortlessly spew off in meetings.

    I also loved reading this because it gives me more courage to dive into that messy world of modeling and finding meaning. I’ve spent many years taking an accountant’s approach to data integrity. Not just the money, but all data. I learning that this isn’t always necessary to start modeling and gaining that “insight” you mention. Thanks again. What a great boost for a Friday blog read. Have a terrific day.

    Comment by jacob campbell — 13 May 2011 @ 12:32 pm

  3. Thanks for the post, Kevin. I don’t consider myself a “numbers guy” either – I kind of stumbled into the data mining / data analysis world – so it’s comforting to hear that there are others out there.

    And, whereas this is my first post on your blog, thanks for all the great stuff you’ve published. This is a great blog – perfectly relevant to the work I do.

    Have a good one.

    – Mark

    Comment by Mark Macdonald — 13 May 2011 @ 6:29 pm

  4. Great topic Kevin. I like to think what makes you really good (and others too) at this work is that you’re careful. Careful with the numbers, careful with the difference between correlation, causality and understanding propensity.

    So you’re finding the patterns that are really there in the mess of data and turning them into actionable items with measurable results. That’s what matters.

    Comment by Marni — 15 May 2011 @ 9:48 pm

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