Many higher education CIOs have been busy maintaining the SIS and CRM systems so vital to recruitment and enrollment and, therefore, have not had time to evaluate artificial intelligence (AI), blockchain, machine learning, chatbot, and robotic process automation (RPA) methodologies, which have a far greater payback than the SIS and CRM systems. Just as importantly, SIS and CRM vendors appear to have pathways for these new technologies but minimal real-world evidence of it working. Collectively we are in a predicament, as few colleges and universities or SIS and CRM vendors can fulfill orders (i.e., enrollments) like Amazon. Few are poised to give Generation Z the Amazon Prime customer experience.
Only a small handful of colleges and universities will have both the time and the money to invest in these new technologies in their fullest measure. Those that don’t have that luxury will need to collaborate to determine where, when, and to what level they invest. Balancing the new technologies while sustaining the transactional aspect of the CRM/SIS is not an easy endeavor, even though it has the greatest reward. The Center for Digital Education’s article “Micro-Innovations Create Education and Campus Ecosystems” demonstrates how investing in new technologies may play out.
As enrollments decline, many colleges and universities will place a strong focus on recruitment, enrollment management, retention, and student success. If ever we needed some quick added-intelligence to address a larger pool of prospective students, it was about 18 months ago. The added intelligence would be artificial intelligence (AI), and the larger pool would be the new AI driven data lake that allows all data to be seen in its raw form. The raw nature of a data lake is what makes AI work so well. When AI is applied with a well-designed data lake, recruitment, retention, and student engagement information is processed in a more meaningful way than in the data warehouse philosophy of most student and CRM systems. Data warehouse analytics is like fishing in a wading pool; the data lake and AI offer much deeper waters and greater opportunity.
Perhaps the greatest benefit of deploying an AI platform with a data lake is that there is no longer the wait for the promise of analytics from vendors. Instead, one can actually think “out loud” by asking the AI platform complex questions that query all raw data that exists in the data lake. AI rapidly sifts through all the raw data without the added level of analytics. When a person asks Siri, Alexa, Einstein, or Watson a question, it does not come back and say, “Let me give you some analytics to decipher the solution on your own. The AI platform instead brings back meaningful answers or additional questions that lead to the right answer.
With AI and the data lake, there is no need to normalize data in a data warehouse, install some analytical application, or integrate data systems. Student and CRM systems will still be needed, but institutions relying solely on those systems are merely fishing in a “wading pool” of data that has been normalized or sorted through the analytics application. The new reality in which we find ourselves is the very reason a complete transformation of fishing for data has changed. The payback of using AI against a data lake of non-normalized data sources located both inside and outside the institution should inspire institutions to move forward with clear decisions.
While AI will eventually improve multiple processes on campus, the need for having it working in recruitment and enrollment is critical. There is no doubt that the institutions that employ AI, chatbots, and machine learning will have a competitive advantage. Many are still waiting for their SIS/CRM solution to fix the problem. However, let’s apply this logic: If enrollment dropped in the majority of colleges and universities over the last two years, should we blame the SIS/CRM systems? No. Neither the systems nor the vendors are to blame for low enrollment any more than the systems or vendors should take credit for the years of good enrollment. The reality is that the intelligence of the organization (culture) is the augmented intelligence to the systems. Adding artificial intelligence to the human augmented intelligence will prove to be the best way to expand reach and enrich the systems’ basic functions—enrolling students.
The concept and reality of AI and a data lake working in unison for recruitment and enrollment management is a giant leap forward from waiting for analytics to be delivered for self-analysis through the data in the SIS/CRM systems. It’s time to go fishing in the deeper waters. Ask your SIS/CRM vendor to demonstrate their AI platform working with a data lake. If they do not have one, contact The Tambellini Group for advice, and review the great products being offered by both SeligoAI and N-Powered for a quick preview of the capabilities of fishing in the deep waters of AI and a data Lake. These entities, along with Ellucian, will be at the 2018 MOKA Conference in Tulsa, Oklahoma, November 6 and 7.
 https://aws.amazon.com/big-data/datalakes-and-analytics/what-is-a-data-lake/: A data lake is a centralized repository that allows you to store all your structured and unstructured data at any scale. You can store your data as-is, without having to first structure the data, and run different types of analytics—from dashboards and visualizations to big data processing, real-time analytics, and machine learning to guide better decisions.
The views and opinions expressed in this article are those of the author and do not necessarily reflect the official policy or position of The Tambellini Group. To express your views in this forum, please contact Katelyn Ilkani, Vice President, Client Services and Cybersecurity Research, The Tambellini Group.
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