Stuffed Animals and Too Much Data

I have a 4-year-old daughter who owns a truly staggering number of stuffed animals, which she calls “stuffies.” Probably literally 60 or so. She sleeps in a full-sized bed, and if she puts all of her stuffies on her bed – some of which are bigger than her – there’s very little room for her to actually sleep. She likes having them on her bed, though, and after kicking a few off the end and shoving the others to the side, she can find a nice child-sized pocket to sleep in.

When she does get settled in to go to sleep, she will grab 2 stuffies and snuggle with them, leaving the rest relegated to the cold expanse above the covers, along the wall or at the foot of the bed.

How is this relevant to education and data? Well, I think the way my daughter interacts with her stuffies is a surprisingly good metaphor for how educators interact with data. 

That is, it’s incredibly common for people to want to see all of the data, for it to be “there,” curated into some unwieldy spreadsheet that they can let their eyes scroll across. They want this, even though when they need to make a decision, they probably only actually need a few pieces of data.

It’s probably worthwhile to start by taking a step back and thinking about why we want data in the first place. There’s certainly something fulfilling – empowering, even – about having a gigantic dataset that contains everything you might want to know about all of your students – their attendance, referrals, grades, test scores, last year’s test scores, what interventions they’re receiving, their demographics, how many pairs of Jordans they own, their Instagram handle, the last song they played on Spotify, etc. etc. I get it! When you can conjure up these massive datasets, it’s like you know the truth is in there somewhere, if you can just have enough time to find it…

We want data to help us make decisions. That’s it. Maybe there are some cases where knowing some tidbit of data is intrinsically valuable, even if it won’t factor into an immediate decision. But these cases are the exception rather than the rule. Simply knowing that little Bruce Dickinson scored in the “Pass Advanced” band on last year’s statewide math assessment means nothing if you’re not using that piece of data making a decision about his math progress.

Data that doesn’t help you make a decision is worthless. Actually, it’s probably worse than worthless. It’s distracting. It’s the mass of stuffies taking up all of that space in your bed. With too much data, we end up spending more time on all of the little techno-administrative tasks of managing data than we spend on actually using the data (which is itself a nebulous and probably-unhelpful bit of jargon). Plus, as much of the working memory research (see e.g. Cowan, 2001 or Diamond, 2013)  would tell us, there’s basically no way we can cram that much data into our monkey brains all at once and actually do anything with it. So, even if you are able to create a sustainable data warehouse where data is automatically updated and it doesn’t require oodles of weekly person-hours to maintain, it’s unlikely you’re using the breadth of that data to make a given decision about a student, teacher, program, etc. The obvious (?) exception here is if you’re feeding all of that data into some sort of statistical model that spits out a decision for you, but that’s a topic for another day.

More likely, you’ll prioritize a few of the most relevant pieces of data and ignore the others. This might happen explicitly, or it might happen implicitly, but it’s probably what happens. These pieces of data are the stuffies that get snuggled with.

So why do we bother curating all of this data? Well, probably for a few reasons. First, it’s comforting and it feels productive. It’s akin to making a very detailed to-do list. You feel accomplished simply because you made the list, even if you haven’t done anything on the list.

Second, we don’t always know what decisions we want to make, so we gather as much data as we can “just in case,” then when we need to make a decision, the data we want is right there, ready for us. Hopefully. There’s logic to this, and this exact work composes a non-negligible portion of my job. But it can get out of hand easily, and the data itself can become the objective, until we’re adding more data because we feel like we need more data, just like my daughter asks for more stuffies just because she wants more stuffies.

In most cases, I think educators – teachers, principals, and school-district administrators alike – are better served by thinking about what question they want to answer and using a few – like, literally 1 to 3 – pieces of data, combined with their professional expertise, to arrive at an answer. Trying to meticulously curate more and more data is like accruing more and more stuffies to line the edges of your bed, even though you know your arms are only big enough to snuggle with 2 or 3 of them.