Contrary to popular belief, the truth about analytics is that they don’t actually make your life easier.
Analytics have value because they automate tasks that take a lot of time, are prone to human error, and most of us don’t like very much. We are fully capable of digging through rows of activity stream data, aggregating that data into concepts, and visualizing the resulting information as patterns. There’s nothing about analytics that we couldn’t do manually. It’s just counting. It’s just math.
The problem with doing this work on our own is that it actually doesn’t require a whole lot of cognitive effort. Yes, it takes time. But, quite frankly, this low-level information processing is kind of beneath us. If it can be automated using technology, then it isn’t a capability that is unique to us humans. The wonderful thing about analytics is that they free us from mundane tasks and open up opportunities for us to engage with problems of a higher order.
Liberated from the task of simply describing what the data are, analytics allow us to focus on more important questions about what they mean, and what we should do about them.
An Analytics Analogy
Without help from the right technology, basic information processing tasks might take a lot of time and effort. But so does removing boulders from a field. Processing information and moving rocks are both important tasks, but they are only important because of what they make possible—making decisions and growing crops, respectively.
In farming, moving rocks is a critical task, but isn’t a cognitively challenging one. It can more easily be performed by a workhorse or a heavy machine. Successfully growing a crop, on the other hand, is something that requires the kind of wisdom that only comes through experience.
The same is true in a college or university. No institution wants to ‘do analytics.’ They want to make evidence-based decisions. Without automation, however, an institution may spend significant time and effort on manual reporting tasks, on the manual act of ‘computing.’ Or they just resign themselves to decision-making on the basis of anecdote alone, much like a farmer resolving to cultivating rocky soil.
The true value of analytics is what comes after the calculations have been made: the strategic decision-making and high-impact activities that happen when human wisdom is brought to bear on rich information. Institutions are complex, which means that asking questions about what data mean relative to a myriad of other factors is hard to do. Thinking through the implications of what data mean relative to institutional strategy is event harder. Acting on data to effectively execute on institutional strategy is harder still. But the results are hugely rewarding.
Cases in Point
Focusing on the ‘thinking’ side of analytics rather than the ‘doing’ side of it is what we saw in each and every one of the analytics presentations at BbWorld18:
- Indian River State College, for example, has all but closed its online-in person achievement gap thanks to a data-driven approach to instructional design.
- Concordia University Wisconsin has increased its student retention rate by 10% because of a thoughtful approach to intensive advising.
- Charles Darwin University is aligning reports to different moments in the teaching and learning lifecycle, while Concordia University Nebraska and Drake University are using Blackboard Intelligence to understand instructional costs and inform academic decision making.
The work that analytics makes possible is incredibly valuable. But it is also challenging. It is in recognition of this fact that Blackboard has adopted a new approach to analytics product development.
Collaborative Product Development
At BbWorld 2018, we announced a new analytics platform called Blackboard Data, which will aggregate data and surface insights from across the Blackboard portfolio. We are building it by working closely with our community.
Blackboard Data Collaboratives are comprised of customers and developers who are tasked with informing feature development AND creating white papers that describe strategies for ensuring the high-impact adoption of those features. The first collaborative has already been formed, and I look forward to sharing the exciting results of this effort in the coming months. Interested in joining a Blackboard Data Collaborative? Learn more and register here.