How the Data Integrates & Creates Insights
One of the most essential components of the customer experience is the data behind it. Data drives a whole series of decision points in the learner-as-customer experience – everything from what programs are offered to how many emails a prospect receives and what the messaging of those emails are.
Behind the data is a design – the data needs to flow back and forth between systems. Historically, this was done with “point-to-point” integrations. Eric Olson explains this in his blog post from 2017 on LinkedIn:
“Technology continues to evolve quicker and quicker leading to our thought process around integration needs to evolve just as quick. Twenty years products had few integration capabilities with other products. Unless tools and products were created by the same company, tightly coupled, and designed to work together it required a lot of effort to integrate them. If you wanted to move data from one application to another application, it usually required extracting the data out of the data repository creating a file then moving it. Along the way possibly transforming the file and placing it where a custom loader program would process it into the other system. Integration more than not closely resembled what had been a popular method in mainframes for decades” (Olson, para 1, 2017).
Point-to-point solutions are generally inefficient, particularly if there are many systems that need to have data flow back and forth. As data needs in higher education become more sophisticated, and attempt to both attract potential learners, match them to the right programs, and predict their troublespots that might prevent retention or graduation, integrations become more crucial.
The below image is an example of what happens when there are multiple point-to-point integrations – sometimes this is known as “spaghetti code”.
Instead of point-to-point solutions, now APIs are typically used. Check out this video, which explains what an API (or Application Programming Interface) is:
Using APIs to enable more real-time data flows can facilitate a whole new level of data integration. Accessing and interpreting this data then allows institutions to intervene in the “customer retention/learner success” arc, supporting progress and completion. Let’s look at the why and the “what happens next” component.
For most institutions, predictive or prescriptive analytics are not yet realized. However, there is a substantial ROI in learner outcomes and in the financial health of the institution, when these systems are implemented in higher ed environments.
Read this article from eLearn Magazine about the benefits and impacts of predictive analytics in higher ed. If the link is broken, and for a printable version, check out this backup copy.
This mini info-graphic provides some high level information on “Big Data” in online higher ed.
Page 1: Behind the Customer Experience
Page 2: Behind CX: More Tech Systems
Page 3: Behind CX: Data Integrations and Insights (you are here)
Page 4: Emerging Financial Models