Humans have been harvesting petroleum for millennia, putting this viscous fossil fuel to good use as a mosquito repellant, to caulk ships and for heat and light. Another resource, data, has been around for millennia, but how we harvest, refine, and use this bounty might be a little unclear. You’re not alone in finding the topic murky. “The world of data is so broad,” notes developer and educator Robin Hunt in the LinkedIn Learning course Learning Data Analytics: 1 Foundations. “And in my humble opinion, I think it will remain broad because we’re still in the early days of discovery and tools development.”
See, it’s not just you; the vastness and broadness of the concepts, along with the sheer volume of data involved, make data analytics a daunting concept to absorb. But, when faced with squishiness, it’s time to firm up your understanding by going back to the basics.
In recent years, learning industry thought leaders have dedicated scores of articles to big data and this interest is reflected in the market size. A 5-year forecast anticipates that the big data analytics in the education industry will reach a market size of $47.82 billion by 2027.
That’s a lot of money but, since mathematician Clive Humby’s 2006 declaration that data is the new oil, it’s been a hot commodity. However, unlike oil, data is reusable and infinite. Humans are quite the prodigious data makers; according to 2020 estimates, around the globe we generate some 2.5 quintillion data bytes daily. Before you google it, there are 18 zeroes in a quintillion.
Whether we’re using laptops, smartphones, or the Internet of Things, we produce this huge quantity of information through our internet usage. Data might include—but isn’t limited to—our clicking patterns, our Google searches, the number of steps we took in a day, and the commands we shout at our Alexas.
In learning specifically, data sources include websites and social media platforms, surveys, Customer Relationship Management Systems (CRM) and, of course, Learning Management Systems (LMS). Collectively, these tiny bytes build into big data. If you’re struggling to draw the line between plain old data and big data, IBM has outlined four characteristics: volume, velocity, variety, and veracity. Simply put, for data to be big, there must be a lot of it, it was generated quickly, it was collected from different reference points, and it is reliable.
Now that we know how we get the data, let’s return to our oil/data parallels. Both oil and data fuel things: oil fuels vehicles; data fuels decisions and strategies. But both are largely useless until they are refined. There’s no humanly way to parse through it all; that is where our big data applications come in handy to collect, compile, store, and filter the information. Once refined, we can analyze, or interpret, the data. Automated tools identify patterns, trends, and other markers and extract insights. Then, using our human lens, we analyze these insights and apply them to future strategies.
Now it’s time to give you what you came here for: How big data and analytics will benefit the learning industry. We’ll start with the tip of the benefits iceberg—using data to measure efficacy and guide revisions of our learning solutions. We have enhanced data for our online solutions from data points throughout its lifecycle, beginning with the first email invite, where learners paused an embedded video, and how long they spent exploring a specific screen or document. It's trickier when we’re considering informal or face-to-face learning but we can expand our data sources to paint a picture of what happens offline. These data sources might be internet searches and performance metrics, and the voice of the customer, analyzed with an eye toward fixing performance gaps.
Next, let’s consider how data help us gauge return on investment. Data analytics can capture a detailed panoramic picture of the return on investment; for example, a tie between the learning provided to desired outcomes. In an era of increased visibility on the role of L&D, this can be straightforward evidence of learning’s value-add.
Finally, in an age ruled by adaption and customization, artificial intelligence (AI) informed by big data and in-the-moment analysis can help us craft adaptive memorable experiences focused on and customized for the modern learner.