Lying With Data Is Easy - Telling The Truth Without It Is Hard

I have to share this cool visualization of data regarding the typical American day.  Be sure to put the Simulator on “Fast” for the full effect.  Go on, I’ll wait.  Neat, right?  It really got my data-juices flowing again!

Full disclosure – I drank the data Kool-aid a long time ago.  I believe in data’s power to predict and help us analyze things we can’t see without it. 

That said, data has just danced around the edges of usefulness for most colleges and universities over time.  We have MOUNDS of data, right?  We have historical data going back decades.  Granted, it’s mostly on paper, in cabinets, locked away in a storage room we only open every seven years, brushing off dust and cobwebs like an Indiana Jones film so as to cherry pick the best data to show Accreditation visitors…but it’s there. 

Let me pause quickly to note the kind(s) of data I’m talking about.  I’m talking about “big” data.  I know, I know…it’s a buzzword from five years ago that has reached semantic assimilation, like when you say your dog’s name 19 times because he’s digging in the backyard.  By name call number 3, the word has lost all meaning.  But let’s revisit what big data is for just a sec anyway.  (Indulge me!)  Big Data is facts / figures is too large to be handled / analyzed by  traditional database protocols (like SQL). But it’s not just about size.  It’s also about risks and ethical considerations related to how you get it, how you analyze it, and how you interpret it.   

I was heavily involved in the edu-data movement early on.  While I am not a psychometrician, I do feel that I have some data-scientist propensity.  I may not be Wilson Lucas or even David McCandless, but I have worked with dozens of schools over the years on data initiatives which led to serious retention results.  And I was also immersed in data as the head of an Academic Research Center.  But I also know that data, data researchers, and data scientists alike are now dealing with baggage not coming from data, but from technology.  What baggage?  The over-promised, under-delivered baggage that often accompanies technology.  Because let’s face it, without technology, 99% of data is just a file room filled with paper.

So, back in the early 2000’s, people started talking through the “promise” of data.  They weren’t wrong in what data could do, might look like, or even how it might save students.  But nobody seemed to fully understand just how hard it would be setup, let alone how much harder it would be to extrapolate, use, analyze, etc.  The infrastructure of most colleges / universities was simply too analogue to handle the data promise and too siloed to generate meaningful information.  As well, by the 2000’s, almost every solution built for education was sold by vendors who had figured out the cultural context; meaning every solution was built for silos and therefore, within a silo.  Because departments on campus so rarely talk to one another, this of course meant that systems would not talk to other systems, including data coming in and out of those systems.  (Even the SIS – the god-father, or possibly better called the grandfather of all University systems did not have meaningful ways to bring in and share data across multiple tools.)  And then there is the math.  Spuriosity, statistical significance, dealing with variables, and on and on meant new positions were necessary (or at least huge contracts for stats consultants) if data was to be understood.  So, a lot of Presidents and Provosts got extremely excited about the story their data should tell, only to be disappointed that they could never quite get it to work. 

Meanwhile, outside of education, a lot of very successful companies and businesses, who had also been frustrated by data-immaturity, kept their eyes on the ball.  They understood the arc of technology, including the vision stage being very different than the implementation stage.  And within a decade or less, the technology and the infrastructure was finally able to handle the data.  These forward-thinking managers and executives started to see returns on prediction, prevention, awareness, marketing, sales, and beyond.  Over time, the big-data concept even penetrated hard to reach markets like healthcare.  Yet education still danced on the periphery, pirouetting there as I write this. 


Education doesn’t spend nearly as much on technology, digital tools, or data initiatives as most of the rest of the modern world.  Educators haven’t fully realized data’s potential nor have they setup infrastructure to realize it any time soon.  Academia struggles with how to use data, often citing extreme skepticism of statistics affecting education / students / teaching / etc, and therefore choosing to avoid big data concepts within the curriculum.  While a few (specific programs, specific universities, etc) are wading in up to their waists, most still have only one toe in the pool, waiting to see if anyone else is going to jump in.  Even the “cutting edge” data companies and student success platforms which are finally getting traction in the higher education space (like Civitas or Discourse Analytics) are doing what they do with one arm tied behind their backs, just chomping at the bit to be truly unleashed.  Meanwhile, education continues to struggle with enrollments, perceived value, retention, measurable learning, and more – all things that learning analytics could help with. 


And that really is the “so what” in all of this.  If a college or university genuinely figures this out, they very well might rule the day.  Armed with the knowledge that big data, learning analytics, and beyond have come SO far in the past two decades with so many places to grab inspiration and even implementation support from (outside of education), we can change the game.  Add to that wonderful insights into how we should effectively use data, from ethical to philosophical perspectives, like we hear from Todd Rose (The End of Average, 2016), and there is a lot of low-hanging fruit as well as potential here.  Yes it will take systems thinking.  It will take change management.  It will take investment AND commitment to seeing it through.  But it can make a major difference.